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Dose versus Image Quality in Pediatric Radiology: Studies Using a Tomographic Newborn Physical Phantom with an Incorpora...


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S7030/S7031 series is a family of FFT-CCD image sensors specifically designed for low-light-level detection in scientific appli cations. By using the binning operation, S7030/S7031 series can be used as a linear image sensor having a long aperture in the direction of the d evice length. This makes S7030/S7031 series ideally suited for use in spectrophotometry. The binning operation offers significant improvement in S /N and signal processing speed compared with conventional methods by which signals are digitally added by an external circuit. S7030/S7031 se ries also features low noise and low dark signal (MPP mode operation). This enables low-light-level detection and long integration time, thus achieving a wide dynamic range. S7030/S7031 series has an effective pixel size of 24 24 m and is available in image areas ranging from 12.288 (H) 1.392(V) mm2 (512 58 pixels) up to a large image area of 24.576 (H) 6.000 (V) mm2 (1024 250 pixels). Featuresl Non-cooled type: S7030 series One-stage TE-cooled type: S7031 series l Pixel size: 24 24 m l Line, pixel binning l Greater than 90 % quantum efficiency at peak sensitivity wavelength l Wide spectral response range l Low readout noise l Wide dynamic range l MPP operation l High UV sensitivity with good stability Applicationsl Fluorescence spectrometer, ICP l Raman spectrometer l Industrial inspection requiring l Semiconductor inspection l DNA sequencer l Low-light-level detection IMAGE SENSORCCD area image sensor Back-thinned FFT-CCD S7030/S7031 series !" # # $%&%'%(%)*&+ ),*-+ *.-++.. -&(+ $%&%'%(%$ *&+ -+. *-+ -++ -++.. +(+. $%&%'%(%.*&+ +*)*-+ +*%-++.. )%%% $%&%'-%%) -%,, ), -%+, *. +,*$) -&(+ $%&%'-%%$-%,, -+.-%+, -+++,*$) +(+. $%&%'-%%. -%,, +*) -%+, +*% +,*$) )%%% $%,% $%&-'%(%)*&+ ),*-+ *.-++.. -&(+ $%&-'%(%$ *&+ -+. *-+ -++ -++.. +(+. $%&-'%(%.*&+ +*)*-+ +*%-++.. )%%% $%&-'-%%) -%,, ), -%+, *. +,*$) -&(+ $%&-'-%%$-%,, -+.-%+, -+++,*$) +(+. $%&-'-%%. /' 0' -%,, +*) -%+, +*% +,*$) )%%% $%,1 2 23+, +,!4 !5# +# 35#+# / /'6/708 259-+,:;2 <89+ =3 9->$%&-' ** % 8#')%?8' 9+>88$%&%'%(%)@'#88$%&%'%(%) A 88#:#81

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CCD area image sensor S7030/S7031 series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CCD area image sensor S7030/S7031 series 0 10 100200WAVELENGTH (nm)TRANSMITTANCE (%)30040050060070080090010001100120020 30 40 50 60 70 80 90 100 (Typ. Ta=25 C) QUARTZ WINDOW AR COATED SAPPHIRE QUANTUM EFFICIENCY (%)WAVELENGTH (nm)(Typ. Ta=25 C) 0 20040060080010001200 10 20 30 40 50 60 70 80 90 100 FRONT-SIDED FRONT-SIDED (UV COAT) BACK-THINNED <8 <8 $%&%G$%&=39-)>8 8'E @'#9-$ $%&+ 8'0' E @'#9-$ >88' 9-)>@ 9-$> *15: Spectral response with quartz glass (or AR-coated sapphire glass) is decreased by the transmittance Spectral response (without window) *15 Spectral transmittance characteristicsKMPDB0058EA KMPDB0110EA Dark current vs. temperature 3 -50-40-30 -20 0 -10102030TEMPERATURE ( C)0.01 1 0.1 10 100 1000DARK CURRENT (e-/pixel/s)(Typ.) KMPDB0256EA 0#B+*?E#8 2 6 6 C !'78 '! +,% &+% 78 39$ 78 .%% -%%% 5' : -.++'4!G' +*? -%% -%%% :59. 622 %? : -% -%% 'GG @9('.-)' F -%%%%% -+*%%% ' :9-% :@ &$$*% ,%%%% ' 2#'9--2@C'I&I-%J +%%--%% <#''%' 29-+K5''-%' 9-&''&' K# 9-,' ''%' 9$>#I-*J 9.>:5*$? 9(>68#,..% :8#:>',%?E A> -*%53 9-%>::@B78G@ 9-->6'# #88# 9-+><# 28#5###-5 '%? K5 28#8#'##68##'# ## 9-&>+( 9-,>-%Fixed pattern noise (peak to peak) Signal 100Photo response non-uniformity (PRNU) [%]

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CCD area image sensor S7030/S7031 series Device structure 2322 21 20 1415 2412 12 11 89 3 4 52 BEVEL SIGNAL OUT 2n 4 BLANK4 BLANK V=58, 122, 250 H=512, 10244 BEVEL THINNINGTHINNING1 23 45 2 3 45V H6 BEVEL 6 BEVEL 2 nSIGNAL OUT 13 10KMPDC0016EB INTEGRATION PERIOD (Shutter must be open) VERTICAL BINNING PERIOD (Shutter must be closed) P1V P2V, TG P1H P2H, SG READOUT PERIOD (Shutter must be closed) 3..62 3..126 3..254 63 127 255 64 128 256 58 + 6 (BEVEL): S703 -0906/-1006 122 + 6 (BEVEL): S703 -0907/-1007 250 + 6 (BEVEL): S703 -0908/-1008 Tpwv Tovr Tpwh, Tpws Tpwr 123 531 1043 532 1044 : S703 -0906/-0907/-0908 : S703 -1006/-1007/-1008 4..530 4..1042 12 D19 D2 D1D20 D3..D10, S1..S1024, D11..D18 RG OS S1..S512: S703 -0906/-0907/-0908 : S703 -1006/-1007/-1008 Timing chartKMPDC0017EB 2 @5 6 6 C 28#8 )9-(.'4 2-!E2+!E1 @E 9-.-%'' 28# 8# *%% +%%% @ #E# -% ' 2-E2+ : 9-. *% J 28#8*%%+%%%' @E-%'' 1 :' '*%'J 28# 8 -%% ' @1 @ E ' 1L2-/'&''4 9-.>#5#*%J5 9-(>;$%&%G$%&-'%(%.E'-%%$ Line bininng 4

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CCD area image sensor S7030/S7031 series 2 @5 6 6 C 28#8 )9+-.'4 2-!E2+!E1 @E 9+%-%'' 28# 8# *%% +%%% @ #E# -% ' 2-E2+ : 9+% *% J 28#8*%%+%%%' @E-%'' 1 :' '*%'J 28# 8 -%% ' @1 @ E ' 1'2-/'&''4 9+%>#5#*%J5 9+->;$%&%G$%&-'%(%.E'-%%$5 INTEGRATION PERIOD (Shutter must be open) P1V RG OS P2V, TG P1H P2H, SG READOUT PERIOD (Shutter must be closed) ENLARGED VIEW Tpwv Tovr Tpwr D1D2D3D4D18D19D20 D5..D10, S1..S1024, D11..D17 P2V, TG P1H P2H, SG RG OS Tpwh, Tpws 123 S1..S512 : S703 *-0906/-0907/-0908 : S703 *-1006/-1007/-1008 4..63 4..127 4..255 64 58 + 6 (BEVEL): S703 *-0906/-1006 128 122 + 6 (BEVEL): S703 *-0907/-1007 256 250 + 6 (BEVEL): S703 *-0908/-1008KMPDC0127EA Area scanning: large full well mode

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CCD area image sensor S7030/S7031 series6 WINDOW 16.3 *22 8.2 *22 34.0 0.34 50.0 0.30 2.54 0.13 22.9 0.3 19.0 4.0 42.0 22.4 0.3 a 7.3 0.63 1.0 7.7 0.68 6.65 0.63 4.89 0.15 ACTIVE AREA12.29 PHOTOSENSITIVE SURFACE 1st PIN INDICATION PAD 3.0 TE-COOLER S7031-0906: a=1.392 S7031-0907: a=2.928 S7031-0908: a=6.000 (24 ) 0.5 0.05 4.4 0.44 4.8 0.49 2.35 0.15 3.75 0.44 PHOTOSENSITIVE SURFACE 1st PIN INDICATION PAD3.0 (24 ) 0.5 0.05 WINDOW 16.3 *22 8.2 *22 34.0 0.34 2.54 0.13 22.9 0.30 22.4 0.30 a ACTIVE AREA12.29 S7030-0906: a=1.392 S7030-0907: a=2.928 S7030-0908: a=6.000 :> S7030-0906/-0907/-0908 S7030-1006/-1007/-1008M62:%%,)0 M62:%%,$0: S7031-0906/-0907/-0908 S7031-1006/-1007/-1008M62:%%,.0 M62:%%,(0: 3.0 PHOTOSENSITIVE SURFACE4.4 0.44 2.35 0.15 4.8 0.49 3.75 0.44 WINDOW 28.6 *22 22.9 0.3 22.4 0.3 ACTIVE AREA 24.58 a 8.2 *22 44.0 0.44 2.54 0.13 1st PIN INDICATION PAD S7030-1006: a=1.392 S7030-1007: a=2.928 S7030-1008: a=6.000 (24 ) 0.5 0.05 (24 ) 0.5 0.05 7.3 0.63 1.0 3.0 6.65 0.63 4.89 0.15 PHOTOSENSITIVE SURFACE 7.7 0.68 1st PIN INDICATION PAD a 4.0 19.0 22.4 0.3 22.9 0.344.0 0.44 52.0 60.0 0.3 2.54 0.13 WINDOW 28.6 *22ACTIVE AREA 24.58 8.2 *22 S7031-1006: a=1.392 S7031-1007: a=2.928 S7031-1008: a=6.000 TE-COOLER*22: Size of window that guarantees the transmittance in the "Spectral transmittance characteristics" graph

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CCD area image sensor S7030/S7031 series 2 $%&% $%&2 7 7 @5 -@:@@:@D-+! + / / / / @FB-%5-%%5 &/://:/D+%! /1 / /1 / D&! *112+ ) ' $'' 2+ :#35'+ 2+ :#35'+ (2-:#35'-2-:#35'-% ;1+ #3'+ ;1+ #3'+ %! --;1-#3'-;1-#3'-%! -+ ; #3 ; #3 @: -& 9+& 9+&2+! -, 2+! :5'+ 2+! :5'+ -*2-!:5'-2-!:5'-) ## -$'#+# -. 2' 0'' -('2D0'D +% 1: 1: 1: +-;!;!@: ++ ;1+! '+ ;1+! '+ %! +&;1-!'-;1-!'-%! +, @1 @ @1 @ 9+&>;8#3;E1## 2+! 0 1 2 3VOLTAGE (V) CCD TEMPERATURE ( C)4 7 6 5 -40 -30 4 3 2CURRENT (A)1 0 -20 -10 0 10 20 30 (Typ. Ta=25 C) VOLTAGE vs. CURRENT CCD TEMPERATURE vs. CURRENT 0 1 2 3VOLTAGE (V) CCD TEMPERATURE ( C)4 7 6 5 -40 -30 2.0 1.5 1.0CURRENT (A)0.5 0 -20 -10 0 10 20 30 (Typ. Ta=25 C) VOLTAGE vs. CURRENT CCD TEMPERATURE vs. CURRENTM62:K%-$.0 M62:K%-$(0 S7031-0906/-0907/-0908 S7031-1006/-1007/-1008 7 '0'@ 2 $%&-'%(%)G'%(%$G'%(%. $%&-'-%%)G'-%%$G'-%%. C ;@B+*?+*-+ 69+, ; 9+*B#9+)B+*? -* &% 6!9+*B#9+)B+*?&.&)! 6#9+$ = &, *< 6 # '$%$%? 9+,>6;> ;###8##E### N#;######## E###)%J# 9+*>## 9+)>### 9+$>6#= #######8## #

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CCD area image sensor S7030/S7031 series HAMAMATSU PHOTONICS K.K., Solid State Division 1126-1 Ichino-cho, Hamamatsu City, 435-8558 Japan, Telephone: (81) 053-434-3311, Fax: (81) 053-434-5184, www.hamamatsu.comU.S.A.: Hamamatsu Corporation: 360 Foothill Road, P.O.Box 6910, Bridgewater, N.J. 08807-0910, U.S.A., Telephone: (1) 908-231-0 960, Fax: (1) 908-231-1218 Germany: Hamamatsu Photonics Deutschland GmbH: Arzbergerstr. 10, D-82211 Herrsching am Ammersee, Germany, Telephone: (49) 08152 -3750, Fax: (49) 08152-2658 France: Hamamatsu Photonics France S.A.R.L.: 8, Rue du Saule Trapu, Parc du Moulin de Massy, 91882 Massy Cedex, France, Telepho ne: 33-(1) 69 53 71 00, Fax: 33-(1) 69 53 71 10 United Kingdom: Hamamatsu Photonics UK Limited: 2 Howard Court, 10 Tewin Road, Welwyn Garden City, Hertfordshire AL7 1BW, Unit ed Kingdom, Telephone: (44) 1707-294888, Fax: (44) 1707-325777 North Europe: Hamamatsu Photonics Norden AB: Smidesv Š gen 12, SE-171 41 Solna, Sweden, Telephone: (46) 8-509-031-00, Fax: (46) 8-509-031-01 Italy: Hamamatsu Photonics Italia S.R.L.: Strada della Moia, 1/E, 20020 Arese, (Milano), Italy, Telephone: (39) 02-935-81-733, Fax: (39) 02-935-81-741 Information furnished by HAMAMATSU is believed to be reliable. However, no responsibility is assumed for possible inaccuracies or omissions. Specifications are subject to change without notice. No patent rights are granted to any of the circuits described herein. 200 6 Hamamatsu Photonics K.K. ; !:-!-D!-'!+!:+! !7D*!E+%% D-*!ED-%% '-*!E'-%% D+,!E&% D*!E&%$%,D*!E+*$%,D-+!E-%%$%,6 6/ 65 6/E -63 Featuresl C7040: for S7030 series C7041: for S7031 series l Area scanning or full line-binnng operation l Readout frequency: 250 kHz l Readout noise: 20 e rms l T=50 C ( T changes by cooling method.)Cat. No. KMPD1023E10 Jan. 2006 DN Multichannel detector heads C7040, C7041 Precaution for use (Electrostatic countermeasures) Handle these sensors with bare hands or wearing cotton gloves. In addition, wear anti-static clothing or use a wrist band with an earth ring, in order to prevent electrostatic damage due to electrical charges from friction. Avoid directly placing these sensors on a work-desk or work-bench that may carry an electrostatic charge. Provide ground lines or ground connection with the work-floor, work-desk and work-bench to allow static electricity to discharge. Ground the tools used to handle these sensors, such as tweezers and soldering irons. It is not always necessary to provide all the electrostatic measures stated above. Implement these measures according to the amount of damage that occurs. Element cooling/heating temperature incline rateWhen cooling the CCD by an externally attached cooler, set the cooler operation so that the temperature gradient (rate of temperature change) for cooling or allowing the CCD to warm back is less than 5 K/minute. '###58#:#E#:#8# 8###8A @-B@+ K-G-'-G+ 8#@-#-M @+#+M K'#KM ####8 @+(.MB-%5 K+(.MG&+&MB&,*%MKMPDB0111EB10 k 220240260TEMPERATURE (K)RESISTANCE280300 100 k 1 M 8


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DOSE VERSUS IMAGE QUALITY IN PEDIATRIC RADIOLOGY: STUDIES USING A TOMOGRAPHIC NEWBORN PHYSICAL PHANTOM WITH AN INCORPORATED DOSIMETRY SYSTEM By AARON KYLE JONES A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLOR IDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2006

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Copyright 2006 by Aaron Kyle Jones

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This work is dedicated to my loving and devoted fiance Marisa, and to my wonderful parents, David and Lynnette J ones. Without their support, no ne of this would have been possible.

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ACKNOWLEDGMENTS First and foremost, I would like to extend my deepest gratitude and thanks to my advisor and the chairman of my supervisory committee, Dr. David Hintenlang. Without his support, encouragement, friendship, and instruction, this would have been a much longer road to travel. The amount I learned from him in this short time is unfathomable. I also owe Dr. Wesley Bolch a large debt of gratitude, as he, too, has been instrumental in guiding me to where I am today. And I thank Dr. Bolch for teaching me how to write a sensational scientific paper. In addition, I would like to thank each of my other committee members, including Dr. Manual Arreola, Dr. Jonathan Williams, and Dr. Hans van Oostrom. My committees constructive criticisms and helpful advice on not only medical physics, but everything from career to life in general, has served to steer me in the right direction for the last five years. I would also like to thank my fellow students, past and present, in the Nuclear and Radiological Engineering Department. Those who went before me, including Christopher Pitcher and Luis Benevides, offered countless tips and invaluable advice, and were always willing to answer the phone and my questions. Also, a special thank you goes to Robert Staton, in whose office many a conversation was had concerning the Pediatric Organ Dose project, and just as many concerning the Mighty Orange and Blue. Another big thank you goes to the staff in the Nuclear and Radiological Engineering Department, who were always there to straighten out whatever mess I seemed to have entangled myself in at the time. Also, I appreciate Dr. Alireza iv

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Haghighats generosity in funding student travel to national meetings, where I had the opportunity to present my work and see what others in the medical physics community were working on. Finally, I would like to thank my family and friends for their devotion, love, and support. Mike, Bart, Bill, and Jeff, you were always there when I was discouraged and needed someone to talk to, shoot hoops with, play video games with, or throw some darts, and that is something I will never forget. Most importantly, I would like to thank my parents, David and Lynnette Jones, for instilling in me the work ethic, morals and values I needed to succeed in life, and my fiance Marisa, whom I love with all my being, for her undying devotion, support, and love. v

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TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES.............................................................................................................xi LIST OF FIGURES.........................................................................................................xiii LIST OF OBJECTS........................................................................................................xvii ABSTRACT.....................................................................................................................xix CHAPTER 1 INTRODUCTION........................................................................................................1 Radiation Effects and Risks..........................................................................................2 Pediatric Radiology......................................................................................................4 Doses to Pediatric Patients....................................................................................5 Strategies for Reducing Doses to Pediatric Patients..............................................6 Patient Simulation in Diagnostic Radiology.................................................................7 Tissue-equivalent Materials...................................................................................7 Patient Phantoms...................................................................................................8 Dosimetry in Diagnostic Radiology.............................................................................9 Dosimeters in Diagnostic Radiology.....................................................................9 Dosimetric Quantities in Diagnostic Radiology..................................................12 Image Quality Assessment in Diagnostic Radiology.................................................14 Methods of Image Quality Assessment in Diagnostic Radiology.......................14 Computational Observers and Computer-Aided Diagnosis (CAD)....................16 Human Observer Studies.....................................................................................17 Hybrid Methods for Assessing Image Quality....................................................18 Objectives of this Research........................................................................................19 2 MOSFET DOSIMETER DEPTH-DOSE MEASUREMENTS IN HETEROGENEOUS TISSUE-EQUIVALENT PHANTOMS AT DIAGNOSTIC X-RAY ENERGIES...................................................................................................21 Introduction.................................................................................................................21 Materials and Methods...............................................................................................22 Cylindrical Phantoms..........................................................................................23 vi

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MOSFET Dosimetry System...............................................................................24 Experimental Parameters.....................................................................................25 Monte Carlo Simulations.....................................................................................26 Results.........................................................................................................................27 Discussion...................................................................................................................28 Conclusions.................................................................................................................32 3 A TOMOGRAPHIC PHYSICAL PHANTOM OF THE NEWBORN CHILD WITH REAL-TIME DOSIMETRY I METHODS AND TECHNIQUES FOR CONSTRUCTION.....................................................................................................34 Introduction.................................................................................................................34 Materials and Methods...............................................................................................36 Data Formatting and Output................................................................................37 Production of Soft Tissue Blanks........................................................................38 Formation of Slices.....................................................................................................40 Bone Introduction................................................................................................41 Lung Construction...............................................................................................43 Dosimeter Localization.......................................................................................46 Phantom Assembly..............................................................................................47 Results.........................................................................................................................49 Discussion...................................................................................................................50 Conclusions.................................................................................................................56 4 A TOMOGRAPHIC, PHYSICAL PHANTOM OF THE NEWBORN CHILD WITH REAL-TIME DOSIMETRY II SCALING FACTORS FOR CALCULATION OF AVERAGE ORGAN DOSE IN PEDIATRIC RADIOGRAPHY.......................................................................................................58 Introduction.................................................................................................................58 Materials and Methods...............................................................................................59 Modifications to the Newborn Computational Phantom.....................................59 Monte Carlo Codes for Radiograph Simulation..................................................63 Simulated fields of view...............................................................................63 X-ray source modeling and beam characterization......................................64 Monte Carlo ionization chamber simulations..............................................65 Creation of Point-to-Organ Dose Scaling Factors (SFPOD).................................66 Newborn Radiographic Exams............................................................................68 Results and Discussion...............................................................................................70 Conclusions.................................................................................................................72 5 CHARACTERIZATION AND TESTING OF THE FIBER OPTIC-COUPLED (FOC) DOSIMETER..................................................................................................74 Introduction.................................................................................................................74 Materials and Methods...............................................................................................74 Energy Dependence.............................................................................................75 vii

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Dose Linearity.....................................................................................................75 Angular Dependence...........................................................................................76 Dosimeter Response at Varying Bend Radii.......................................................76 The Twenty-Five Fiber FOC Dosimetry System................................................77 Sensitizing the FOC dosimeters...................................................................80 Calibration of the FOC dosimeters...............................................................80 Results and Discussion...............................................................................................83 Conclusions.................................................................................................................85 6 OPTIMIZATION OF DOSE WHILE MAINTAINING ADEQUATE IMAGE QUALITY IN PEDIATRIC COMPUTED TOMOGRAPHY...................................87 Introduction.................................................................................................................87 Materials and Methods...............................................................................................93 Dosimetry............................................................................................................93 UF Newborn tomographic phantom.............................................................94 Fiber optic-coupled dosimetry system.........................................................94 CT scanning of the phantom........................................................................96 Dose calculation...........................................................................................99 Image Quality Analysis.....................................................................................103 Catphan CTP515 module...........................................................................103 Phantom image scoring software...............................................................104 Threshold contrast-to-noise ratio determination........................................106 Protocol Selection..............................................................................................108 Results.......................................................................................................................109 Discussion.................................................................................................................119 Conclusions...............................................................................................................125 Calibration of the FOC Dosimeter....................................................................129 General Trends in Pediatric CT Protocols.........................................................129 Future Work.......................................................................................................131 7 DOSE COMPARISON BETWEEN PHYSICAL MEASUREMENTS AND COMPUTATIONAL SIMULATIONS....................................................................133 Possible Sources of Error..........................................................................................139 Incorrect Use of Normalization Factors............................................................141 Other Possible Sources of Error........................................................................143 Investigation of Possible Error Sources....................................................................144 Physical Measurement Errors............................................................................144 Computational Simulation Errors......................................................................147 Conclusions and Future Work..................................................................................149 8 CONCLUSION.........................................................................................................151 Results of this Work.................................................................................................151 Opportunities for Future Work and Development....................................................151 Improvement of the Production Process for Tissue-Equivalent Materials........151 viii

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Automation of the Physical Phantom Construction Process.............................152 Image Quality Phantom Construction...............................................................155 Modifications to FOC Dosimetry System.........................................................157 The Future of the Pediatric Organ Dose Project.......................................................159 Final Words..............................................................................................................162 APPENDIX A TISSUE-EQUIVALENT MATERIALS FOR CONSTRUCTION OF TOMOGRAPHIC DOSIMETRY PHANTOMS IN PEDIATRIC RADIOLOGY..164 Introduction...............................................................................................................164 Materials and Methods.............................................................................................165 Soft Tissue-Equivalent Substitute for the Newborn (STES-NB)......................166 Bone Tissue-Equivalent Substitute for the Newborn (BTES-NB)....................166 Soft and Bone TE Substitutes for the Child/Adult (STES and BTES)..............167 Lung Tissue-Equivalent Substitute for the Newborn/Child/Adult (LTES).......167 Manufacturing Process......................................................................................168 Measurement of TE Material Mass Density......................................................168 Comparison of Radiation Interaction Coefficients............................................169 Calculations of X-ray Attenuation and Absorbed Dose at Depth.....................171 Results and Discussion.............................................................................................172 Comparisons of UF Tissue Substitutes to Reference Tissue Compositions......172 Comparison of UF Tissue Substitutes to Other TE Materials...........................177 Calculations of X-ray Attenuation and Absorbed Dose at Depth.....................181 Conclusions...............................................................................................................182 B BITMAP IMAGES OF THE 5 MM SLICES USED AS TEMPLATES FOR PHANTOM CONSTRUCTION...............................................................................185 C MCNP CODE FOR CYLINDRICAL PHANTOM DEPTH-DOSE COMPARISONS......................................................................................................269 MCNP Code for Homogeneous Soft Tissue Phantom.............................................270 MCNP Code for Heterogeneous Soft and Bone Tissue Phantom............................274 MCNP Code for Heterogeneous Soft, Bone, and Lung Tissue Phantom.................278 MCNP Code for Ionization Chamber Simulation....................................................282 D LABVIEW CODE FOR READING OF LINEAR CCD ARRAY USED IN FIBER OPTIC-COUPLED DOSIMETRY SYSTEM..............................................285 E MATLAB CODE FOR AUTOMATED SCORING OF IMAGE QUALITY PHANTOM IMAGES..............................................................................................292 F INSTRUCTION SHEET FOR RADIOLOGISTS SCORING OF PHANTOM IMAGES AND FINAL TALLY OF RADIOLOGISTS PHANTOM SCORES....304 LIST OF REFERENCES.................................................................................................307 ix

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BIOGRAPHICAL SKETCH...........................................................................................320 x

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LIST OF TABLES Table page 1-1 Tissue weighting factors from ICRP 60...................................................................14 3-1. Locations selected for dosimeter placement in the tomographic physical phantom .................................................................................................................48 4-1 Organ and skeletal masses within the modified newborn computational phantom.61 4-2 Radiographic SFPOD for the newborn phantom........................................................71 4-3 The effective dose per unit integrated tube current calculated with and without the application of point-to-organ dose scaling factors SFPOD..................................71 6-1 Dose measurement locations for head exams..........................................................99 6-2 Point dose measurement locations for CAP exams................................................101 6-3 Tissue weighting factors from ICRP 60.................................................................102 6-4 Protocol element selection for evaluation..............................................................109 6-5 Sample organ dose table for head exams...............................................................118 6-6 Sample organ dose table for CAP exams...............................................................119 6-7 Calibration factor correction factors......................................................................121 6-8 Magnitudes of average expected dose reductions when adjusting scanning protocols.................................................................................................................124 6-9 Default pediatric protocols at Shands Hospital......................................................126 7-1 Comparison of simulated and measured effective and organ doses for head exams at 80 kV.......................................................................................................135 7-2 Comparison of simulated and measured effective and organ doses for head exams at 100 kV.....................................................................................................136 7-3 Comparison of simulated and measured effective and organ doses for head exams at 120 kV.....................................................................................................137 xi

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7-4 Comparison of simulated and measured effective doses for head exams at 100 mAs and a pitch of 1.0...........................................................................................137 7-5 Comparison of simulated and measured effective and organ doses for CAP exams at 80 kV.......................................................................................................138 7-6 Comparison of simulated and measured effective and organ doses for CAP exams at 100 kV.....................................................................................................138 7-7 Comparison of simulated and measured effective and organ doses for CAP exams at 120 kV.....................................................................................................140 7-8 Comparison of simulated and measured effective doses for CAP exams at 100 mAs and a pitch of 1.0...........................................................................................141 7-9 Approximate errors associated with using head normalization factors for CAP exams......................................................................................................................142 8-1 Data for construction of proposed modifications to the Catphan 500....................158 A-1 Elemental composition and effective atomic numbers for the UF newborn tissue-equivalent substitutes and their corresponding reference tissue compositions......173 A-2 Elemental composition and effective atomic numbers for the UF tissue-equivalent substitutes needed for phantom construction at ages of 1-year and older .....................................................................................................................174 A-3 Results for calculations of narrow-beam photon fluence-rate attenuation through 4 cm of tissue-equivalent material and the resulting single-collision absorbed dose.........................................................................................................................183 E-1 Sample calibration factor file.................................................................................289 xii

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LIST OF FIGURES Figure page 1-1 The attributable lifetime risk from a single small dose at various ages at the time of exposure.................................................................................................................3 1-2 Estimated radiation-related excess relative risk for solid-cancer mortality among A-bomb survivors ....................................................................................................3 1-3 Lifetime attributable cancer mortality risk as a function of age at examination for a single typical CT examination of head and abdomen........................................4 2-1 Schematic of the three phantom configurations used in the depth-dose study........23 2-2 Comparison of measured and simulated tissue absorbed dose with depth within the homogeneous soft tissue phantom......................................................................27 2-3 Comparison of measured and simulated tissue absorbed dose with depth within the heterogeneous soft tissue and bone tissue phantom...........................................29 2-4 Comparison of measured and simulated tissue absorbed dose with depth within the heterogeneous soft tissue, bone tissue, and lung tissue phantom.......................30 3-1 Typical bitmap image demonstrating the four regions used in this phantom..........38 3-2 Example of the Teflon and clay mold used to form the raw soft tissue blanks........39 3-3 Sanded blank used to create the soft tissue outline of each slice of the phantom....40 3-4 Slices after Step 3 of the phantom creation process.................................................41 3-5 Slices after bone introduction...................................................................................43 3-6 Lung tissue blanks cut into slices after removal from PVC pipe mold....................44 3-7 Soft tissue outlines for phantom slices containing lung tissue and corresponding lung regions saved from bitmap transparencies.......................................................45 3-8 View of a phantom slice containing lung after bone introduction, prior to sanding.....................................................................................................................46 3-9 Slice 27.....................................................................................................................50 xiii

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3-11 Profile photograph of the completed phantom, without arms..................................52 3-12 Photograph of the completed phantom, with arms...................................................53 3-13 Typical dosimeter channels in completed slices......................................................55 4-1 UF Newborn showing the internal organ structure and exterior of the computational phantom along with the exterior of the corresponding physical phantom....................................................................................................................60 4-2 Axial slices through the UF Newborn phantom.......................................................60 5-1 Coated active areas of the FOC dosimeters.............................................................77 5-2 Schematic of the design of the FOC dosimetry system...........................................79 5-3 Graphical depiction of the effects of the sensitization of the FOC dosimeters .....81 5-4 Illustration of the entire calibration setup................................................................82 5-5 Close-up of the FOC dosimeter alignment during calibration.................................82 5-6 Energy dependence of the FOC dosimeter...............................................................83 5-7 Dose linearity of the FOC dosimeter........................................................................84 5-8 FOC dosimeter response versus fiber bend radius...................................................84 6-1 Illustrations of the general scanning setup...............................................................97 6-2 Photographs of the scanning setup for head exams and body exams.......................98 6-3 Approximate scanning coverage of the head exams..............................................100 6-4 Approximate scanning coverage of the CAP exams..............................................102 6-5 Effective doses corresponding to Head 80 kV and CAP 80 kV.............................110 6-6 Object scores corresponding to Head 80 kV and CAP 80 kV...............................111 6-7 Object scores (sorted by pitch) corresponding to Head 80 kV and CAP 80 kV....114 6-8 Object score and effective dose data presented on the same plot for CAP 80 kV and CAP 120 kV....................................................................................................116 6-9 Total object scores for head exams at 100 mAs, effective doses for head exams at 120 kV................................................................................................................127 xiv

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6-10 Total object scores for CAP exams at 100 mAs, effective doses for CAP exams at 100 mAs.............................................................................................................128 7-1 Raw data from original organ dose measurement scan..........................................146 7-2 Raw data from dose measurement scan with PMT housing shielded....................147 7-3 Raw data from scanning of unshielded and shielded patch cable..........................148 8-1 Various views of the completed vacuum chamber.................................................153 8-2 Photographs of the new phantom construction system..........................................155 8-3 Schematic of the proposed modifications to the Catphan 500...............................157 A-1 Ratios of mass attenuation coefficients for the STES-NB, BTES-NB, and LTES tissue substitutes to their corresponding reference values.....................................175 A-2 Ratios of mass energy-absorption coefficients for the STES-NB, BTES-NB, and LTES tissue substitutes to their corresponding reference values...........................176 A-3 Ratios of mass attenuation coefficients for the STES, BTES, and LTES tissue substitutes to their corresponding reference values................................................177 A-4 Ratios of mass energy-absorption coefficients for the STES, BTES, and LTES tissue substitutes to their corresponding reference values.....................................177 A-5 Ratios of both / and en/ for STES-NB and acrylic to their corresponding reference values as a function of photon energy....................................................178 A-6 Ratios of both / and en/ for BTES-NB and aluminum to their corresponding reference values as a function of photon energy....................................................179 A-7 Ratios of both / and en/ for LTES, LN10/75, and air to their corresponding reference values as a function of photon energy....................................................179 A-8 Ratios of both /and en/ for STES, MS11, and acrylic to their corresponding reference values as a function of photon energy....................................................180 A-9 Ratios of / for BTES, IB1, SB5, weighted combination of IB1 and SB5, and aluminum to their corresponding reference values as a function of photon energy.....................................................................................................................181 A-10 Ratios of en/ for BTES, IB1, SB5, weighted combination of IB1 and SB5, and aluminum to their corresponding reference values as a function of photon energy.....................................................................................................................182 E-1 Screenshot of the front panel of the main VI.........................................................286 xv

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E-2 Screenshot of the front panel of the calibration VI................................................287 E-3 Screenshot of calibration routine after background acceptance.............................288 E-4 Screenshot of calibration VI after irradiation.........................................................289 xvi

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LIST OF OBJECTS Object page 4-1 Radiograph Organ Dose Calculator developed by Robert Staton and Aaron Kyle Jones at the University of Florida (61.5 KB, Radiograph_Organ_Dose_ Calculator.xls)..........................................................................................................68 5-1 PDF document containing the complete specifications of the S7031-1007 CCD image sensor and C7041 detector head (237 KB, CCD_specs.pdf).........................78 6-1 CT Organ Dose Calculator developed by Robert Staton and Aaron Kyle Jones at the University of Florida (57 KB, CT_Organ_Dose_Calculator.xls)....................103 6-2 Effective dose plots generated as a result of this study (217 KB, Effective_Dose_ Plots.xls).................................................................................................................117 6-3 Object score plots generated as a result of this study (255 KB, Autoscore_ Scores.xls)..............................................................................................................117 6-4 Effective dose plots with object scores (as in Figure 6-8) generated as a result of this study (229 KB, Effective_Dose_Pl ots_with_Image_Quality_Data.xls).........118 6-5 Organ dose tables for Head 80 kV (50 KB, Head_80_kV_Summary.xls)............118 6-6 Organ dose tables for Head 100 kV (52 KB, Head_100_kV_Summary.xls)........118 6-7 Organ dose tables for Head 120 kV (52 KB, Head_120_kV_Summary.xls)........118 6-8 Organ dose tables for CAP 80 kV (58 KB, CAP_80_kV_Summary.xls)..............118 6-9 Organ dose tables for CAP 100 kV (58 KB, CAP_100_kV_Summary.xls)..........119 6-10 Organ dose tables for CAP 120 kV (58 KB, CAP_120_kV_Summary.xls)..........119 E-1 LabView code CCD7041 (858 KB, CCD7041_code.llb)......................................291 E-2 Executable file of the same code contained in Object E-1, CCD7041.llb, along with necessary run-time engines (18 MB, CCD7041.zip).....................................291 F-1 Scoresheet of final tally of radiologists phantom scores (28 KB, Radiologists _Phantom_Scores.xls)............................................................................................306 xvii

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy DOSE VERSUS IMAGE QUALITY IN PEDIATRIC RADIOLOGY: STUDIES USING A TOMOGRAPHIC NEWBORN PHYSICAL PHANTOM WITH AN INCORPORATED DOSIMETRY SYSTEM By Aaron Kyle Jones May 2006 Chair: David E. Hintenlang Major Department: Nuclear and Radiological Engineering Pediatric patients in hospitals have benefited enormously from the many advances in medical care during the past few decades. As a result of these advances, pediatric patients in hospitals are subjected to many diagnostic exams, and as technology continues to improve, many of these exams deliver higher and higher radiation doses to pediatric patients. One of the most rapidly advancing technologies, and perhaps the technology most responsible for the increase of favorable outcomes in premature infants and pediatric patients, is medical imaging. However, rapidly improving image quality has also led to rapidly escalating radiation doses due to diagnostic imaging, and little is known about the magnitude of these doses in pediatric patients, nor the potential for reducing these doses. The ultimate goal of this work was to accurately quantify the doses delivered to pediatric patients during computed tomography (CT) exams, and thus identify potential dose-saving protocols that maintain adequate image quality. A tomographic newborn xviii

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physical phantom was constructed from tissue-equivalent materials for use in evaluating the doses delivered to pediatric patients as a result of diagnostic imaging. Fiber optic-coupled (FOC) dosimeters were used along with the physical phantom to measure average organ doses during projection radiography. Also, average organ doses were measured during CT exams across a wide range of protocol parameters. Then, images of the Catphan CTP515 low contrast module were acquired using the same protocols, and scored automatically with a custom-written scoring routine. Thus, potential dose-saving protocols were evaluated to identify those that maintained adequate image quality. The results of this work suggest that the vast majority of pediatric CT scanning be performed using a pitch value of 1.0, with the exception of certain challenging imaging tasks and specialized imaging protocols. The results also suggest that the majority of pediatric CT scanning be performed using a collimated beam width of 24 mm (16 x 1.5) and a gantry cycle time of 0.5 second in order to complete the scan as quickly as possible and keep dose to a minimum without compromising image quality. It was also confirmed that increasing the tube potential used for CT scanning results in large dose penalties, with a twofold (approximately) dose increase expected when the tube potential is increased from 80 kV to 100 kV, and a 30 percent (approximately) dose increase expected when the tube potential is increased from 100 kV to 120 kV. It was also confirmed that reducing the scanning pitch below 1.0 results in large dose penalties as well; however, it is of interest to note that a pitch of 1.25 did generally lead to inferior image quality (i.e., low contrast detectability) when compared to a pitch value of 1.0. xix

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CHAPTER 1 INTRODUCTION Pediatric patients in hospitals have benefited enormously from the many advances in medical care during the past few decades. As a result of these advances, pediatric patients in hospitals are subjected to many diagnostic exams, and as technology continues to improve, many of these exams deliver higher and higher radiation doses to pediatric patients. One of the most rapidly advancing technologies, and perhaps the technology most responsible for the increase of favorable outcomes in premature infants and pediatric patients, is medical imaging. Computed tomography (CT), specifically, has undergone numerous transformations and upgrades, including helical scanning and the introduction of multiple-row detectors. This has led to a vast increase in the utilization of CT and the extension of CT to new types of clinical diagnoses.1,2 However, there is a trade-off involved in this situation. While CT provides unmatched diagnostic information, it is also inherently a high-dose imaging modality. It has been reported that upwards of 40% of the total dose from medical imaging is a result of CT examinations,3,4 and in the U.S., due to the absence of limits on dose per scan, this number may approach 60%.5 There have been an enormous number of studies done regarding dose, image quality, or both in pediatric CT, encompassing all types of exams. Several studies have already found that a 30-40% reduction in dose is possible with comparable image quality, and perhaps even more dose reduction is possible with no loss of diagnostic information.6-8 1

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2 Radiation Effects and Risks Children are ten times more radiosensitive than adults, and one abdominal CT scan delivers an effective dose equal to 500 chest radiographs and is equivalent to the average national background radiation over a period of more than 3 years. Also, as a rule of thumb, the lifetime cancer mortality risk attributable to the radiation exposure from a single abdominal CT exam in a 1-year-old child is of the order of one in a thousand, an order of magnitude larger than a similar exam for an adult.9 There are many national and international committees and organizations dedicated in whole or in part to quantifying the risks of both non-stochastic and stochastic effects due to radiation exposure. These include, to name a few, ICRU, NCRP, BEIR, and the ICRP. They draw their conclusions (on which regulations are based) from several data sets, including the Japanese bomb survivors, medical exposures, animal data, and occupational exposures. Much useful information can be gleaned from these conclusions, and a look at that information follows. Radiation risk varies dramatically with age, and there is also a clear gender difference that is more pronounced at early ages, with females being more radiosensitive than males.10 This can be seen more clearly in Figure 1-1. It is apparent from Figure 1-1 that for pediatric patients, the risk for induction of a fatal cancer ranges from 10-15% per Sv (however, this risk will not be expressed until later in life10), while for middle-aged adults this risk falls to around 2-4% per Sv. In addition, A-bomb survivor data hve now matured, and the 35,000 A-bomb survivors who received doses lower than 0.25 Sv have been studied.11 The results show a small but statistically significant excess incidence of cancer at doses down to 50 mSv, overlapping

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3 the range of organ doses and effective doses involved in helical CT. Also, as can be seen from Figure 1-2, the risk of solid cancers appears to be a linear function of dose.9 Figure 1-1. The attributable lifetime risk from a single small dose at various ages at the time of exposure, assuming a dose and dose-rate effectiveness factor (DDREF) of 2. The higher risk for the youngest age group will not be expressed until later in life. Note the dramatic decrease in risk with increasing age. From ICRP.10 Figure 1-2. Estimated radiation-related excess relative risk (and standard error) for solid-cancer mortality among A-bomb survivors. Also shown is the range of organ doses characteristic of helical CT. From Hall.9

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4 Finally, Figure 1-3 shows the risk of fatal cancer induction per 10,000 scans at 200 mAs for both abdominal CT and head CT.12 Age at CT (y) 0102030405060708090Risk of fatal cancer/10,000 scans at 200 mAs 024681012 Abdominal CTHead CT Figure 1-3. Lifetime attributable cancer mortality risk as a function of age at examination for a single typical CT examination of head and abdomen. Dose and therefore risk are proportional to mAs and can be scaled accordingly. Note the rapid increase in risk with decreasing age. Adapted from Brenner et al.12 Pediatric Radiology As mentioned in the Introduction, the field of radiology has enjoyed tremendous advancement and progress over the past decade. Pediatric radiology, in particular, has also benefited from technological advances in radiology, ranging from the ability to diagnose congenital defects and repair them to the virtual elimination of the need for sedation in young patients undergoing CT exams. However, these advancements have not come without a cost. That cost is dose. CT involves doses orders of magnitude greater than those involved in conventional radiography, and as mentioned in the previous section, with these increased doses comes increased risk.

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5 Doses to Pediatric Patients Pediatric patients are frequently subjected to higher average organ, and therefore higher effective doses (leading to a higher risk for radiation effects), than adult patients for a number of reasons. First, children are much more radiosensitive than adults due to their rapidly growing body tissues.13 This makes young children 10-15 times more likely to develop a radiation-induced malignancy than an adult.10 Second, a childs skeleton is comprised of a greater percentage of active bone marrow, a highly radiation-sensitive organ, than adults.14 Third, the greater post-exposure lifetime of infants and children increases the possibility of the manifestation of radiation-induced effects.15 Fourth, pediatric patients generally have a larger fraction of their anatomy located within an X-ray field compared to adults, and their organs are spaced more closely together, resulting in significant irradiation of organs outside the primary beam. Fifth, pediatric patients are often uncooperative, and therefore faster (and higher dose) scanning modalities such as helical CT are used more frequently. Finally, for a given procedure, the effective dose is larger in smaller patients than in adults, as demonstrated by Ware et al.16 and Nickoloff et al.17 The convenience and ease of use of CT have led to the unfortunate consequence that little attention is paid to adapting examination protocols developed for adult patients to better suit pediatric patients, the result being much higher doses to pediatric patients than is necessary to achieve acceptable image quality.7 This is seldom noticed because there is no clinical consequence for using too much radiation in CT, unlike plain-film radiography.

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6 Strategies for Reducing Doses to Pediatric Patients A starting point for the reduction of dose to pediatric CT patients is the obvious set of parameters that have a direct effect on dose. As an example, dose is directly proportional to the tube current-time product (mAs) in any imaging modality, including CT. Also, although it is not as plain to see, dose decreases with increasing pitch8 and dose also decreases with decreasing kVp.17-19 Other factors directly affecting dose in pediatric CT are beam collimation, scan mode, and gantry cycle time (directly related to the tube current-time product). The effects of the interplay of all of these parameters can be tested directly by performing experiments while varying the parameters. However, in addition to the above parameters that directly affect CT doses in known ways, there are other approaches to dose reduction in pediatric CT. Perhaps the easiest place to start is simply judicious use of CT.20 It is estimated that perhaps 40% of all pediatric CT examinations are not clearly indicated.21 Frush mentions that it is impossible to determine the appropriateness of every CT examination, but gives several potential strategies to minimize the number of unnecessary CT examinations, including good communication between radiologists and pediatric care providers (consultations that lead to alternative exams, etc.) and periodic reviews of CT requests that can lead to recommendations and advice for those who consistently order poorly indicated examinations.20 Another suggestion made by Frush under the heading of judicious use of CT is the limitation of exams to the area in question, i.e., minimizing the length of coverage.20,22 Finally, Frush also suggests that multi-phase scanning is overused at many institutions, indicating that it is only necessary (at most) 5% of the time. He also states that when necessary, multiphase examinations should adjust scan parameters (mAs, kVp, pitch, etc.) at each phase to minimize radiation dose.20,22 It is apparent from these

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7 statistics and comments that a vast reduction in the dose delivered to pediatric patients during CT can be achieved before the technologist touches the control console of the CT scanner. Patient Simulation in Diagnostic Radiology Patient simulation in diagnostic radiology takes on various forms, from the very basic (single material and simple geometry) to the incredibly detailed (anthropomorphic or tomographic phantoms constructed from tissue-equivalent materials). This section will give an overview of the various methods that have been used to simulate patients for dose measurement in diagnostic radiology, with particular focus on the simulation of pediatric patients. Tissue-equivalent Materials The five most common types of tissue-equivalent materials in diagnostic radiology are soft tissue, lung tissue, bone tissue, adipose breast tissue, and glandular breast tissue. The most frequently used tissue-equivalent materials in diagnostic radiology are those that are both easy to work with and relatively inexpensive, including acrylic (PMMA), water, air, aluminum, and copper. Extensive sets of tissue-equivalent materials have been developed by individuals, the most notable being White,23,24 and White et al.25 Also, an exhaustive list of existing tissue-equivalent materials has been compiled by the ICRU.26 However, pediatric tissue simulation is performed exclusively with generic materials used to represent patients of all ages, including acrylic, water, air, and aluminum. However, this is not an optimal method of simulating pediatric body tissues, as shown by Jones et al. in their work with tissue substitutes for use in pediatric radiology.27 A complete set of tissue-equivalent substitutes were developed as a result of his work, for use with both pediatric patients, and patients of other ages.

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8 Patient Phantoms As mentioned previously, patient phantoms range from very basic acrylic cylinders to very complex anthropomorphic phantoms. While several adult phantoms exist for use in diagnostic dosimetry, the current state of pediatric phantoms will be discussed in this section, as this is the focus of this research. The most common type of pediatric phantom is a simple acrylic cylinder. Nickoloff et al. have constructed a series of phantoms to represent patients of various ages (6, 10, 16, 24, and 32 cm diameter acrylic cylinders) and examined the effect of patient size on dose, and found that dose increases with decreasing patient size.17 Also, one of the most commonly used practices to measure doses to pediatric patients is the use of the 16 cm adult head phantom from the current AAPM protocol28 to represent a pediatric body. A second category of pediatric dosimetry phantoms includes those that involve unique geometries or a combination of several simple geometries. Phantoms in this class include a neonatal phantom constructed by Jones et al. from eight 1 cm thick acrylic sheets with air spaces machined in each sheet to represent lungs29 and a block-style phantom constructed by Duggan et al. including a head and torso made from water-equivalent material and a lung-equivalent insert in the torso.30 A third class of pediatric dosimetry phantoms is characterized as anthropomorphic. These phantoms are intended to more closely represent the true external and internal anatomy of the human body. Examples of phantoms in this class are those representing 0, 2, 6, and 12-year-old children constructed by Giacco et al.31 from various cylindrical shapes representing the head, torso, arms, legs, and lungs of the patients. These phantoms were constructed by forming an acrylic shell which was then filled with

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9 deionized water (the lungs remain air-filled). Also included in this class are stylized newborn and 1-year-old phantoms based on the MIRD geometry constructed using tissue-equivalent materials at the University of Florida (UF).32 Finally, perhaps the best example of phantoms in this class are the those developed by Varchena et al.,33,34 which comprise a set of tissue-equivalent anthropomorphic phantoms of various ages: 0, 1, 5, 10, and 15 years old. While these phantoms provide detailed modeling of pediatric patients exterior, skeletal, and lung anatomy, they are not based on corresponding real patient CT or MR image data sets. Dosimetry in Diagnostic Radiology Diagnostic dosimetry incorporates a variety of dosimeters and dosimetric quantities, which will be discussed in detail in the following sections. CT dosimetry is a specialized application of diagnostic dosimetry, involving some of its own quantities. Dosimeters in Diagnostic Radiology There are many types of dosimeters available for use in diagnostic dosimetry, including, but not limited to, ionization chambers, thermoluminescent dosimeters (TLDs), metal-oxide semiconducting field-effect transistors (MOSFETs), diodes, optically-stimulated luminescence (OSL), and fiber-optic-coupled dosimeters. Dose-area-product meters are also used, but to a lesser extent, and are not used in CT. These various dosimeters will be examined below in regards to their individual advantages and disadvantages. The ionization chamber is the gold standard in radiation dosimetry, and is mainly used for various computed tomography dose index (CTDI) measurements, as well as dose-length product (DLP) measurements. Advantages include high sensitivity, excellent linearity, uniform energy response, and little to no fading. However, ion chambers are

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10 generally fairly large in size, and therefore are not suitable for making many in-phantom measurements simultaneously. Also, they are expensive and must be sent away for periodic calibration. The thermoluminescent dosimeter (TLD) is by far the most common dosimeter used for in-phantom dosimetry in diagnostic radiology. Its small size makes it very useful for in-phantom measurements, as dose measurements in many different locations can be made simultaneously. The TLD also exhibits an angularly independent response, and is fairly tissue-equivalent. There are many different types of TLDs available, and depending upon the application, a TLD with a uniform response and good linearity and reproducibility over the desired energy range can be found. TLDs are subject to some fading, but this should not be a problem if they are read soon after they are exposed. However, TLDs also have their shortcomings. TLDs are very small and difficult to handle, and must be annealed prior to each use. Also, a time-consuming reading process (using a suitable TLD reader) must be carried out after the TLD has been exposed. TLD response may vary significantly with reading parameters (e.g., temperature) as well. In addition, care must be used when handling TLDs to avoid contamination with dirt or oil from the skin, which can affect the response of the TLD after it has been calibrated. Various types of TLDs have been used in phantom studies, including LiF:Mg,Ti35,36 and LiF:Mg,Cu,P,30,31 with LiF:Mg,Cu,P being preferred due to its extremely high sensitivity (its lower measurement limit being 1 Gy30), however, it does under-respond by 10% to 20% at energies less than 20 keV and between 80 keV and 300 keV.30 Diodes have not found their way into experimental use in diagnostic radiology as of yet, but there are advances being made towards that goal.37,38 Aoyama et al. have

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11 developed a pin silicon photodiode detector that has a sensitivity of approximately 0.02 mGy (20 Gy) and a linear output up to doses of 12 mGy.39 However, it can also be seen from their work that there is a significant energy dependence of the response of their dosimeter, as well as a non-uniform angular response. Optically-stimulated luminescence (OSL) is the term given to the use of Al2O3:C as a radiation detector.40-42 OSL material is not heated to release the energy stored in traps due to radiation deposition events; instead, pulsed laser light is used. OSL has a lower sensitivity limit of approximately 0.01 mGy (10 Gy).40-42 It also possesses all the benefits of TLDs without the hassle of annealing. However, it cannot be exposed to light (except very long wavelength red light), as this will release some of the energy stored in the traps in the material. OSL is currently only available in sheets (powder deposited on a plastic substrate) or discs and can only be read using readers owned by companies dealing in radiation protection dosimetry. However, a real-time OSL reader utilizing rods of OSL material coupled to fiber-optic cables is in production, but no timetable is available for its release to the research community.43 The MOSFET dosimeter is also a (relatively speaking) newcomer to the diagnostic dosimetry field. While it has been well-established as a useful dosimeter in radiotherapy,44-50 its examination for use as a dosimeter in diagnostic applications has just begun to be investigated.15,51-55 MOSFETs incorporate some of the advantages of TLDs, including excellent linearity,50,51 tissue-equivalency (better than TLDs56), and reproducibility at higher doses (9.5% at 35 mGy/fx to 1.2% at 2.5 Gy/fx,50 and reportedly even better with the new generation of MOSFET dosimeters). MOSFET dosimeters have exhibited evidence of post-exposure fading;51 thus it is important to read the dosimeter at

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12 a consistent time after each irradiation. Also, it has been determined that previous generations of MOSFET dosimeters have exhibited an overresponse from slightly above a factor of 3 (~ 50 keV)56 to slightly above a factor of 4 (33 keV)57 when normalized to the response of the dosimeter to 6 MV photons. It has also been reported that the manufacturer released unpublished data which show an overresponse by a factor of 4.4 at a mean x-ray energy of 45 keV.57 However, MOSFET dosimeters can claim an advantage over TLDs due to the fact that they can be read immediately after exposure, and can therefore be utilized in a near real-time dosimetry system. Also, there is no need for annealing or any sort of post-processing after exposure and reading. In addition, the dose history is retained in the dosimeter due to the build-up of space charge.45,58 This does contribute to a reduction in response with increasing dose history, but only by a small amount (~1%/V).56 Another newcomer to the field of diagnostic dosimetry is the fiber-optic-coupled dosimeter (FOC).59-63 The active area of these dosimeters is constructed from fused-quartz glass doped with Cu1+ ions. Like OSL, they can be read using a photomultiplier tube (PMT), however, FOC dosimeters are phosphorescent detectors, requiring no stimulation for reading, and the output can be integrated over the irradiation time, making these dosimeters true real-time dosimeters. More details about the construction and operation of FOC dosimeters can be found in Justus et al.59-61 and Huston et al.62-63 Dosimetric Quantities in Diagnostic Radiology A wide range of quantities is used to either describe the doses received by a patient during an exam or provide some sort of estimate or relationship by which to derive the dose received by a patient. Amongst these quantities are energy imparted, dose-area product (DAP), dose-length product (DLP), computed tomography dose index (CTDI),

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13 entrance surface dose (ESD), and effective dose (E) (CTDI and DLP are used exclusively in CT, while DAP is not used in CT). While the first four of these quantities are easily calculated or measured, effective dose is the only quantity that can actually be used to quantify the risk to a patient from a diagnostic exam.10 As a matter of fact, many of the previously mentioned quantities are used as a starting point to derive a value for effective dose in the absence of measured organ doses. Chapple et al. have derived DLP/E values35 using the phantoms manufactured by Varchena et al.33,34 Huda et al. have derived E/unit energy imparted factors64,65 from Monte Carlo simulations. Finally, Pages et al. have calculated relationships between DLP, CTDIw, and E using normalized organ specific dose factors determined for pediatric mathematical phantoms using Monte Carlo.66 However, it is well known that The effective dose cannot be easily derived from other dose descriptors.67 The effective dose can, however, be calculated from measured average organ doses. Effective dose is given by the following expression: TTEw TD (1-1), whereis the tissue weighting factor for tissue T andis the dose to tissue T (this form of the expression assumes a radiation weighting factor of 1).10 Table 1-1 shows a list of the currently accepted tissue weighting factors. Tw TD Therefore, by measuring the appropriate organ doses, one can calculate the effective (whole body) dose to a patient and attribute some excess risk of cancer mortality due to a radiation exposure. The feasibility of measuring organ doses in phantoms in order to calculate effective dose has been demonstrated by Hintenlang et al.15 and Chapple et al.35

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14 Table 1-1. Tissue weighting factors from ICRP 6010 Tissue or organ Tissue weighting factor, wT Gonads 0.20 Bone marrow (active) 0.12 Colon 0.12 Lung 0.12 Stomach 0.12 Bladder 0.05 Breast 0.05 Liver 0.05 Esophagus 0.05 Thyroid 0.05 Skin 0.01 Bone surface 0.01 Remainder 0.051,2 1Remainder is composed of : adrenals, brain, upper large intestine, small intestine, kidney, muscle, pancreas, spleen, thymus, and uterus. 2In cases in which a single remainder tissue or organ receives an equivalent dose in excess of the highest dose in any of the twelve organs for which a weighting factor has been specified, a weighting factor of 0.025 should be applied to that tissue or organ and a weighting factor of 0.025 to the average dose in the rest of the remainder as defined above. Image Quality Assessment in Diagnostic Radiology Image quality assessment in diagnostic radiology is fairly straightforward, but when extended to CT can become very complicated. Therefore, this section will be divided into two separate discussions. The first will discuss image quality assessment techniques in general in diagnostic radiology, focusing mainly on projection radiography. Another section will follow, discussing the extension of the previously examined methods to CT. Methods of Image Quality Assessment in Diagnostic Radiology Image quality in diagnostic radiology is not determined by a single aspect, but instead is determined by the product of several factors, including spatial resolution, contrast, image noise, and the presence/absence of any distortion or artifacts. There is a

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15 variety of image quality assessment techniques used in diagnostic radiology. Some of these techniques examine one aspect of image quality, while others seek to quantify image quality by examining two or more aspects of image quality simultaneously. Several of the techniques used to assess image quality will be discussed in the following section. The contrast-detail phantom (such as the ACR mammography phantom) is one tool for assessing image quality that attempts to examine each aspect of image quality. Phantoms such as these contain small specks or fibers that are used to assess spatial resolution, low-contrast objects (e.g., discs) to assess image noise and contrast (or contrast-to-noise ratio, CNR), and the image of the phantom can also be examined for the presence of any artifacts or image distortions. This method can be used to qualitatively assess image quality, or a quantitative assessment can be generated by calculating a total score from all of the individual elements. Also, these phantoms often incorporate acrylic or some other form of tissue-equivalent material to simulate imaging through the appropriate thickness of a patients anatomy. Other types of phantoms are also used for image quality assessment in diagnostic radiology. These include spatial resolution phantoms such as line pair phantoms and contrast phantoms that contain low-contrast objects of various sizes and contrast levels. One commonly used phantom is the Leeds Test Objects, including the TOR[CDR], which incorporates both line pair phantoms and low-contrast discs.a Also, there are other phantoms used for qualitative assessment of image quality on the market that seek to simulate some part of a patients anatomy (hand, lung, etc.). a Leeds Test Objects Ltd, Wetherby Road, Boroughbridge, North Yorkshire, YO51 9UY, UK

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16 Another common method of assessing image quality is the measurement of the modulation transfer function (MTF) of an imaging system. The MTF is a graphical representation of how well an imaging system preserves contrast at increasing spatial resolution. MTFs are derived by measuring the line-spread function (LSF) of an imaging system (frequently done using edges or wires), then taking the Fourier transform of the LSF, which is the optical transfer function (OTF) of the system. The real part of the OTF is the MTF. The MTF is a convenient graphical description of the performance of an imaging system. Computational Observers and Computer-Aided Diagnosis (CAD) A recent development in diagnostic radiology is the use of computational observers. Among the most popular computational observers is the channelized Hotelling observer (CHO), which seeks to predict and mimic human visual performance. Similar to a neural network, these observers are trained by giving them information about the signal for which they are searching. The Hotelling observer is also general enough to include all sources of randomness, including background and noise.68 However, the use of Hotelling observers has been confined to ideal situations (i.e., artificially generated images) for the most part, and is frequently examined for use in SPECT and PET imaging. Computer aided-diagnosis is also beginning to be examined for use in diagnostic radiology, mainly in mammography and CT (specifically in lung imaging). Various studies have examined computerized schemes,69 model-based detection,70 or computer-aided diagnosis (CAD)71-74 as possible surrogates for radiologists or to supplement radiologists. Many of these techniques use some sort of thresholding in order to narrow the number of possibilities to a feasible range. However, the training of the computers used in these studies can require enormous amounts of data in order to

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17 reach reasonable detection rates,70 and some of the techniques are meant only to supplement or aid radiologists73 or require a radiologist to identify a region containing a possible abnormality in order to narrow the ROI to allow for the computer to search the area in a reasonable amount of time.69 Human Observer Studies Perhaps the most widely used (and widely accepted) quantitative measure of image quality is the receiver-operating characteristic (ROC) study. The basic premise of an ROC study is as follows: an observer (one of many) is presented with a series of images that may or may not have a signal present. The observer is then asked to rank his confidence about the presence of a signal on a scale such as definitely not present, probably not present, not sure, probably present, or definitely present. Using this data, values for the observers sensitivity (true positive fraction, or TPF) and (1-specificity) (false positive fraction, or FPF) can be calculated at various decision thresholds. This, in essence, yields values for the performance of an imaging system (or alternatively, an observers performance) at several different decision thresholds, which can then be plotted. The area under the ROC curve, Az, can then be used to quantify the performance of an imaging system, or to compare the performance of several imaging systems. A close relative of the ROC study is the two-alternative forced-choice (2-AFC) method. The meaning of the area under the ROC curve, Az, is actually given in terms of the results of a 2-AFC technique.75 It can be shown that the expected fraction of correct decisions in the 2-AFC experiment is equal to the expected area under the ROC curve that would be measured with the same images viewed one at a time in a conventional ROC experiment.75-78 The 2-AFC experiment utilizes pairs of images, one containing

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18 only noise and the other containing some known signal on the noisy background. An observer is then presented with various pairs of images, and asked to specify which of the two images contains the signal. Then, Az can be estimated by simply calculating the fraction of the pairs of images where the signal was identified correctly. However, the ROC technique has a distinct advantage over the 2-AFC technique. It can be shown that, in order to obtain a similar level of confidence, one would need almost twice the number of images if using a 2-AFC technique versus an ROC technique.75 One final observer study is also related to the 2-AFC experiment. The M-alternative forced-choice (M-AFC) technique is a more general extension of the 2-AFC experiment. An observer is presented with an image which displays a known signal in a known position. Within the image, there are M locations that could possibly contain the same signal. The signal is present in one of these locations, and absent in the other M-1 locations. The observer is then asked to identify which of the locations contains the signal.79 Aufrichtig provides an excellent demonstration of the use of an M-AFC experiment.80 Hybrid Methods for Assessing Image Quality Hybrid methods for assessing image quality provide some of the advantages of both computational observers and human observers, while eliminating some of the drawbacks associated with each. While computational observers are ideal for computer-aided diagnosis, they are not ideal for image quality studies, due to the fact that ultimately a human observer will be making decisions regarding clinical images. Along similar lines, it is often not feasible to use human observers for research studies involving image quality assessment due to the large number of images to be read, and the associated time commitment required of the professional staff. In addition, the strain on a

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19 clinical CT tube from performing hundreds of scans for a 2-AFC or M-AFC study must also be considered. This is where the advantages of a hybrid method for image quality assessment become apparent. The best, and perhaps only, example of an existing hybrid method is that used by Pitcher to examine image quality in pediatric computed radiography.81 This method utilized human observers to determine a threshold contrast-to-noise ratio (CNR) that could be detected in a phantom image, then applied this threshold in a software program that was used to automatically score contrast-detail phantom images based on the calculated threshold CNR. Objectives of this Research The objectives of this research are described below. Together, these objectives, and the work towards achieving them, will lead to the identification of low-dose CT scanning protocols that maintain adequate image quality while significantly reducing the radiation dose delivered to pediatric patients. 1. Identify a suitable dosimeter for use in a dosimetry phantom system, and test the components of the system, including the dosimeters and the tissue-equivalent materials (previously developed at UF by the author27). 2. Construct a physical, tomographic newborn dosimetry phantom with an incorporated real-time dosimetry system The phantom will be constructed from tissue-equivalent materials previously developed by the author.27 Effective dose is the most accurate and widely-accepted method for calculating the risk of stochastic effects from radiation exposure, and this phantom will allow the calculation of average organ and effective doses when coupled with point-to-organ dose scaling factors that are currently being developed and have been explored previously.82 It is hypothesized that this phantom will provide the most accurate average organ and effective dose measurements to date for neonate patients undergoing diagnostic exams. 3. Develop the tools necessary to quantitatively assess image quality (low-contrast detectability, in particular) in computed tomography (CT) imaging Dose reduction is prudent only to the point where satisfactory images result, i.e. images that provide adequate diagnostic information These tools will allow for the automated

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20 scoring of phantom images, and will be based on trained observers (i.e. radiologists) decision thresholds. 4. Use the tools described in (3) and (4) to identify low-dose protocols in CT, and select those low-dose protocols that maintain adequate image quality. The remainder of this dissertation describes in detail the methods used to achieve the goals listed above, the results obtained from this work, conclusions that can be drawn from this work, and suggestions for future improvements and further work. It is divided into chapters based on papers either already published in peer-reviewed journals, or papers to be submitted to peer reviewed journals, with the exception of the Introduction and Conclusion chapters. Several appendices have been included in addition to the main body of work. These appendices provide information that is unsuitable (e.g., bitmap images of the phantom slices, long computer codes, etc.) for publication in peer-reviewed journals. The absence of this information from the chapters themselves does not hinder the understanding or reading of each chapter. However, this information may be of interest to those in the scientific community reading this dissertation, and will certainly be useful for those following in my footsteps on this project.

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CHAPTER 2 MOSFET DOSIMETER DEPTH-DOSE MEASUREMENTS IN HETEROGENEOUS TISSUE-EQUIVALENT PHANTOMS AT DIAGNOSTIC X-RAY ENERGIES Introduction The MOSFET dosimeter is a relatively new device in the diagnostic dosimetry field. MOSFET dosimeters are currently widely used for measurements of patient absorbed dose in radiation therapy, and are increasingly utilized in interventional radiology. Due to their small size, they do not perturb the radiation field during measurement, and multiple dosimeters may be placed at several locations simultaneously. The active area of a MOSFET dosimeter is 0.04 mm2 with a thickness of less than 2 m, and the overall size, including the epoxy bubble, is less than 4 mm2.83 While it has been well-established as a useful dosimeter in radiotherapy,44,45,47-50 its use as a dosimeter in diagnostic applications has only recently been investigated.15,51-53,55,84 Past generations of MOSFET dosimeters have demonstrated many of the advantages of thermoluminescent dosimeters (TLDs), including excellent linearity,50,51 tissue-equivalency (better than TLDs),56 and reproducibility at high doses (9.5% at 35 mGy/fx to 1.2% at 2.5 Gy/fx).50 MOSFET dosimeters have exhibited evidence of post-exposure fading; however, this behavior has improved with subsequent generations of dosimeters. It is still good practice to read the dosimeter following a consistent time interval after each irradiation. It has also been reported that previous generations of MOSFET dosimeters have exhibited an over-response from slightly above a factor of 3 (~50 keV)56 to slightly above a factor of 4 (33 keV)57 when normalized to the response of the dosimeter to 6-MV 21

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22 photons. Calibrating the dosimeters at the energies for which they will be used typically solves this problem. However, MOSFET dosimeters can claim a significant advantage over TLDs as they may be read immediately following exposure. Furthermore, there is no need for annealing or post-processing following exposure and reading. In addition, the dose history is retained in the dosimeter due to the build-up of space charge.45,58 This does contribute to a reduction in response with increasing dose history, but only by a small amount (~1%/V).56 In a recent paper by Sessions et al.,82 the potential use of MOSFET dosimeters for making measurements of tissue dose within heterogeneous stylized phantoms was described for dose assessments in pediatric radiology. The objective of the current study was to explore the use of MOSFET dosimeters for measuring tissue depth-dose at diagnostic photon energies in both homogeneous and heterogeneous tissue-equivalent materials. Simple cylindrical phantoms were employed as a prelude to more complex measurements in a tomographic physical phantom. Monte Carlo radiation transport was used to determine values of tissue point-dose at depth, against which the MOSFET dosimeter measurements were then compared. Materials and Methods The experimental setup employed in this study utilized tissue-equivalent substitutes developed specifically for use at diagnostic photon energies, and reported previously by our research group.27 These tissue substitutes were designed to mimic the radiation attenuation and absorption properties of newborn reference tissues as defined by Cristy and Eckerman for the Oak Ridge National Laboratory (ORNL) series of stylized anatomic models.85

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23 Cylindrical Phantoms A modular cylindrical phantom was manufactured for this study comprised of a series of 5-cm diameter discs, each 1 cm in thickness. The tissue-equivalent materials used to manufacture the discs included STES-NB (soft tissue substitute for the newborn), BTES-NB (bone tissue substitute for the newborn), and LTES (lung tissue substitute).27 In addition, channels were machined into one disc of each material to allow for consistent positioning of the MOSFET dosimeters. Manufacturing the phantoms in this way allowed for the creation of any desired experimental setup. Three phantom configurations were chosen for evaluation and they are shown schematically in Figure 2-1. Figure 2-1. Schematic of the three phantom configurations used in the depth-dose study. Each configuration had a total thickness of 7 cm, allowing for measurements ranging from the phantom surface to a tissue depth of 6 cm (e.g., top of the seventh and final disc). Phantom SSSSSSS thus indicates seven contiguous slices of soft-tissue equivalent discs, while phantom SSBBSSS denotes a stack of two soft-tissue discs,

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24 followed by two discs of bone-equivalent material, and then three discs of soft-tissue equivalent material. In a similar manner, phantom SBLLBSS represents a heterogeneous stack of either soft-tissue (S), bone (B), or lung (L) tissue-equivalent discs. MOSFET Dosimetry System The system used in this study was a MOSFET dosimetry system manufactured by Thomson and Nielsen Electronics, Ltd.a and consisted of the TN-RD-60 patient dosimetry system (including dual-sensitivity bias supplies used at their high sensitivity setting) in conjunction with the TN-1002RD dosimeter, the most current dosimeter in the isotropic, high-sensitivity line of MOSET devices from this company. It is our recommendation that the MOSFET dosimetry system be calibrated using the x-ray energies that will be used during the experiment, as the response of the MOSFET dosimeter can vary across the energies encountered in diagnostic radiology. Consequently, the following calibration protocol is used by the Pediatric Organ Dose (POD) research group at the University of Florida. When used for experiments with diagnostic x-ray energies, the dosimeters are calibrated using a pancake ionization chamber. We are currently using a Keithley Model 96035B dual entrance window ionization chamber along with a Keithley Model 35050A electrometer.b Both the MOSFET dosimeters and the ionization chamber are placed on some form of tissue-equivalent backscatter material (STES-NB, acrylic, etc.) and irradiated simultaneously until an exposure of ~1 R is accumulated within the ionization chamber. The MOSFET dosimeters are then read, and calibration factors are assigned. a Thomson and Nielsen Electronics Ltd., 25E Northside Road, Nepean, Ontario, Canada, K2H 8S1 b Keithley Instruments Inc., 28775 Aurora Road. Cleveland, Ohio 44139

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25 This process is then repeated several times to determine a reliable calibration factor for each individual dosimeter (this process is simplified by the software included with the dosimetry system and the user simply needs to input the exposure accumulated within the ionization chamber). Experimental Parameters Experimental data for this study were acquired using a diagnostic x-ray tube at 66 kVp a tube potential selected from technique charts at Shands Hospital at the University of Florida as representative of those appropriate for newborn patients. The tube was characterized and found to have a half-value layer of 2.35 mm of Al and 5% voltage ripple at 66 kVp. A tube current of 200 mAs was used throughout the study to provide better measurement statistics in a shorter period of time. Nine repeated measurements were taken at each of seven depths in the cylindrical phantom (0, 1, 2, 3, 4, 5, and 6 cm) for each of three phantom configurations shown in Figure 2-1. An interval of 10 seconds was allowed to elapse following each irradiation prior to the dosimeter read (this was also the case during dosimeter calibration). By reading the dosimeters at a consistent post-irradiation time, variations in experimental data due to charge fading or ramping of the voltage are minimized. However, neither of these phenomena appears to be a significant issue with current generations of MOSFET dosimeters. Point estimates of tissue absorbed dose can be inferred from MOSFET dosimeter measurements using the following relationship: entissuetissue-MOSFETexposure-to-kermaenairDmCFk (2-1),

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26 where m is the MOSFET response (in mV), CF is the energy-dependent MOSFET calibration factor (C kg-1 mV-1), k is the conversion factor from exposure to air kerma (mGy per C kg-1), and en air and en tissueare the mass energy-absorption coefficients for air and soft tissue, respectively, weighted over the x-ray energy spectrum incident upon the cylindrical phantom. Monte Carlo Simulations In order to estimate actual point-values of tissue absorbed dose within the physical phantom used for the MOSFET measurement, a series of Monte Carlo simulations were also performed using the radiation transport code MCNP5.86 X-ray energy spectra (66 kVp) used as input to MCNP5 were obtained using the TASMIP tungsten anode spectral model of Boone and Siebert.87 Point estimates of tissue dose were calculated in MCNP5 by tallying the energy deposited by photons and their secondary electrons within 1-mm diameter spheres located at the lower edges of each simulated tissue-equivalent discs of the cylindrical phantoms of Figure 2-1. The material composition of each sphere was identical to that of the disc in which it was positioned. Again, the purpose of these simulations was to provide true estimates of dose versus depth for comparison of the point doses measured by MOSFET dosimetry, and not to computationally model the microelectronic structure and irradiation response of each MOSFET dosimeter. Consequently, various problems in modeling the dosimeter geometry, its material composition, and energy deposition events with the small sensitive volume of the dosimeter88 were circumvented.

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27 Results In Figure 2-2, values of absorbed dose are plotted as a function of depth in the homogeneous soft-tissue phantom as calculated via Monte Carlo simulation (open circles). These values are then compared to measured values of point dose as given by the MOSFET dosimeters. Phantom SSSSSSSDepth (cm) 0123456 Absorbed Dose (mGy) 0123456 Measurement bubble side facing beam Monte Carlo simulation A Phantom SSSSSSSDepth (cm) 0123456 Absorbed Dose (mGy) 0123456 Measurement flat side facing beam Monte Carlo simulation B Figure 2-2. Comparison of measured and simulated tissue absorbed dose with depth within the homogeneous soft tissue phantom. Two orientations of the MOSFET dosimeters were considered: (A) Epoxy bubble facing the x-ray beam, and (B) Flat side of the dosimeter facing the x-ray beam.

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28 Experimental data points (closed circles) represent the mean one standard deviation from nine replicate measurements. The dose resolution of the dosimetry system was determined to be ~0.3 mGy, based on the MOSFET dosimeter sensitivity and the voltage resolution of the reader. In Figure 2-2A, the dosimeters were positioned at each depth such that the epoxy bubble side of the dosimeter faced the incident x-ray beam. In Figure 2-2B, the dosimeter positions were reversed such that the flat side of the dosimeter at each depth faced the incident beam. Similar comparisons of simulated and measured point doses as a function of depth are given in Figure 2-3 for the heterogeneous phantom SSBBSSS and in Figure 2-4 for the heterogeneous phantom SBLLBSS. In each case, the calibration factors of Equation 2-1 were determined with the dosimeters positioned with the epoxy bubble side facing the x-ray beam as recommended by the manufacturer.c,d Discussion The data of Figures 2-2A, 2-3A, and 2-4A indicate that at depths exceeding 2 cm, strong agreement is seen between measured and simulated values of point tissue dose within both the homogeneous and heterogeneous phantoms regardless of the dosimeter orientation. However, when the MOSFET dosimeters are positioned with the epoxy bubble facing the x-ray beam, measured values of tissue absorbed dose consistently fall below simulated values within the first 2 cm of each cylindrical phantom. The maximum percent difference was noted to be 21% at a depth of 1 cm within the homogeneous phantom of Figure 2-2A. However, when the 0 to 2 cm depth measurements were repeated with the flat side of the dosimeters facing the x-ray beam, the agreement c http://www.thomson-elec.com/downloadables/m20calibration.pdf d http://www.thomson-elec.com/downloadables/ascalibration.pdf

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29 between measured and simulated point doses improved significantly within the homogeneous SSSSSSS and heterogeneous SSBBSSS phantoms. Phantom SSBBSSSDepth (cm) 0123456 Absorbed Dose (mGy) 01234567 Measurement bubble side facing beam Monte Carlo simulation A Phantom SSBBSSS Depth (cm) 0123456 Absorbed Dose (mGy) 0123457 Measurement flat side facing beam Monte Carlo simulation 6 B Figure 2-3. Comparison of measured and simulated tissue absorbed dose with depth within the heterogeneous soft tissue and bone tissue phantom. Two orientations of the MOSFET dosimeters were considered: (A) Epoxy bubble facing the x-ray beam, and (B) Flat side of the dosimeter facing the x-ray beam.

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30 Phantom SBLLBSS Depth (cm) 0 123456 Absorbed Dose (mGy) 04567 8 123 Measurement bubble side facing beam Monte Carlo simulation A Phantom SBLLBSS Depth (cm) 0123456 Ab(mGy sorbed Dose ) 023467 158 Measurement flat side facing beam Monte Carlo simulation Bh y ing the x-m attribute the discrepancies seen in Figures 2-2A, 2-3A, and 2-4A to pho Figure 2-4. Comparison of measured and simulated tissue absorbed dose with deptwithin the heterogeneous soft tissue, bone tissue, and lung tissue phantomTwo orientations of the MOSFET dosimeters were considered: (A) Epoxbubble facing the x-ray beam, and (B) Flat side of the dosimeter facray beam. A more modest improvement was noted for the heterogeneous SBLLBSS phantoat 0 to 2 cm depth. We ton attenuation within the epoxy coating of the dosimeters. This epoxy

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31 attenuation effect was subsequently verified through additional MCNP5 modeling (dnot shown). Another feature of note is the slight departure of measured and simulated valuestissue absorbed dose at depth within the homogeneous soft tissue phantom. This departure is potentially attributed to a lower MOSFET sensitivity with depth as fewer fewer low-energy photons interact within the dosimeters at inc ata of and reasing depth in the phant 2-ents) for each measurement depth would be warranted. d at the epoxy om. Dosimeter sensitivity may decrease by as much as 15% at a depth of 6 cm relative to its value at the surface. A less sensitive dosimeter would have a greatercalibration factor associated with it, which explains the under-response seen in Figure2A at greater depths in the phantom. Nevertheless, these errors associated with the sensitivity decrease are on the same order of magnitude as the experimental errors associated with the MOSFET dosimeters themselves (at these depths in tissue), and therefore we do not think that separate calibration factors (which would prove cumbersome for in vivo or experimental measurem Even when the epoxy attenuation effect is minimized through suitable MOSFETdosimeter orientation (flat side toward the beam), discrepancies between simulated anmeasured point doses at depth are still evident within the heterogeneous phantoms of Figures 2-3B and 2-4B. We believe that these differences are due to the fact th bubble (now faced away from the incident x-ray beam) attenuates low-energy photons and electrons scattered from the underlying bone layers (a secondary epoxy attenuation effect). In particular, the discrepancy between measured and simulated

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32 surface doses is noted to be higher in Figure 2-4B (bone layer 1 cm from the surfacthan seen in Figure 2-3B (bone layer 2 cm from the surface). A final feature of note is evident in Figures 2-3 and 2-4 in which slight increases in measurement variability are noted for those MOSFET locations within and at the boundaries of the bone discs of the two heterogeneous phantoms. We attribute theobservations to a loss of charge-particle equilibrium (CPE) at the boundary of the bone and soft tissue (or lung tissue) layers of the phantom. The absence of CPE will induce large dose gradients within these regions of the phantom, and any slight variation indepth-positioning of the MOSFET dosimeters w e) se the ithin the phantom will result in corresponding variations in measured response. Conclusions It is evident from the results presented in this study that the MOSFET dosimetry system and tissue-equivalent substitutes can be used to accurately measure radiation absorbed dose as a function of depth within a simple phantom. However, we do make several recommendations regarding the use of a MOSFET dosimetry system. The TN-1002RD dosimeter has been used throughout this experiment. The epoxy attenuation effects described in this paper could be reduced by using the TN-1002RDM dosimeter, a micro-MOSFET dosimeter. However, the added cost of these dosimeters makes this option less desirable, and the epoxy attenuation effect would still exist, albeit at a smaller magnitude. Similar (or better) reduction of these attenuation effects can be achieved through careful placement of the MOSFET dosimeters within the phantom. When making surface dose measurements in projection radiography, one must ensure that the flat side of the dosimeter is oriented towards the x-ray beam. However, in our tomographic newborn phantom, more than 95% of the dose measurement locations

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33 are at depths greater than 2 cm, the depth at which the epoxy attenuation effect is insignificant. For measurement locations at depths shallower than 2 cm, the MOSFET dosimeter should be oriented so that the flat side of the dosimeter faces the x-ray beam. Inmanufacturers protocol at diagnos all cases, these recommendations are valid for calibrations performed as per the tic energies.

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CHAP TER 3 A THILD WITH (1) ual ic cylinders) and examined the effect of patient size on CTonatal phantom constructed by Jones et al.29 from eight 1-cm thick acrylic sheets with air spaces OMOGRAPHIC PHYSICAL PHANTOM OF THE NEWBORN CREAL-TIME DOSIMETRY. I. METHODS AND TECHNIQUES FOR CONSTRUCTION Introduction Phantoms used in diagnostic radiology can be divided into two general classes: those used to assess image quality, and (2) those used to assess the radiation absorbed dose delivered to patients during diagnostic procedures. Those used to assess image quality are fairly general (i.e., not patient-specific in regard to age, sex, or physical stature), while those used to assess patient dose are more tailored to these same individpatient characteristics. In addition, dosimetry phantoms range in complexity from simple shapes using a single material to anthropomorphic phantoms utilizing several different tissue-equivalent materials. The most common pediatric dosimetry phantom is a simple acrylic cylinder. Nickoloff et al. constructed a series of phantoms to represent patients of various ages (6,10, 16, 24, and 32 cm diameter acryl DI values. In this study, the authors found that CTDI values increase with decreasing patient size.17 Also, one of the more commonly used practices to measure radiation doses to pediatric patients is the use of the 16-cm adult head phantom from AAPM Report #3128 to represent a pediatric body. A second category of pediatric dosimetry phantoms involves unique geometries or a combination of several simple geometries. Phantoms in this class include a ne 34

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35 machined in each sheet to represent the lungs, and a block-style phantom constructed bDuggan et al.30 that included a head and torso made from water-equivalent materials and a lung-equivalent insert within the torso. A third class of pediatric dosimetry phantoms are characterized as anthropomorphic. These phantoms are intended to more closely represent the true external and internal anatomy of the human body. An example of phantoms in this classare those representing 0, 2, 6, and 12-year-old children constructed by Giacco et al.31 from various cylindrical shapes representing the head, torso, arms, legs, and lungspatients. These phantoms were constructed by forming an acrylic shell which was then filled with deionized water (the lungs remain air-filled). Also included in this class are stylized newborn and 1-year-old phantoms based on the MIRD geometry constructed using tissue-equivalent materials at the University of Florida (UF).32 Finally, perhaps thbest example of phantoms in this class are the those developed by Varchena et al.,33,34 which comprise a set of tissue-equivalent anthropomorphic phantoms of various ages: 0,1, 5, 10, and 15 years old. While these phantoms provide realistic modeling of pediatricpatients exterior, ske y of the e letal, and lung anatomy, they are not based on corresponding real patient CT or MR image data see ts. The objective of the present study was to construct a tomographic physical phantom of a newborn patient which would be uniquely matched to a corresponding tomographic computational phantom of the same patient anatomy, based on a CT image set. The combination of phantoms thus establishes a dosimetry system in which thadvantages of each are used in concert to fully characterize internal organ dosimetry and patient effective dose from diagnostic procedures ranging from radiography to

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36 interventional fluoroscopy and C T. A distinct advantage of physical phantoms is that explic F r retation of point-dose estimates made within the sk et etry ght manufacture automation. The construction process, along with many of the obstacles encountered along the way, is discussed in the following sections. it knowledge of the photon energy spectrum and patient irradiation geometry is notrequired for dose assessment. While traditional TLD dosimetry may be utilized, the Unewborn physical phantom was constructed for explicit use of either near real-time (MOSFET dosimeters)89 or real-time (gated fiber-optic-coupled or GFOC dosimeters)59 dose measurement. The companion computational phantom, on the other hand, allows one to average the absorbed dose over the full extent of soft-tissue organs or even over voxel sub-regions of those organs, a feature unattainable from limited point-dose measurements within the physical phantom. Finally, assessment of regional and whole-body absorbed dose to complex and distributed organs such as the active bone marrow, skeletal endosteum, and skin are uniquely suited to computational phantoms, and theiresults can thus be used to guide the interp eleton of the corresponding physical phantom. In the present paper, we present the construction of the UF newborn physical phantom. Its application in determining organdoses in pediatric projection radiography is given in the companion article by Statonal.90 (see Chapter 4). Materials and Methods The construction process involved in creating a tomographic physical dosimphantom is neither apparent nor straightforward when first undertaken. Much thouand creativity must be invested before construction begins. Situations arise that must betroubleshot, and many lessons are learned that are certain to make future phantom construction more efficient, including various methods for

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37 Data 2 _Contours was used to segment the CT images into four regions for the purpose of phantom construction (air, soft tissue, bone tissue, and lung tissue). Details of the segmentation procedure can be found in Nipper et al.91 In addition to image segmentation, a grid spaced in 32 pixel increments was overlaid onto the segmented images to facilitate proper alignment of phantom slices at the time of assembly. Next, the bitmap output from CT_Contours underwent a final manipulation before was used as the basis for phantom construction. Because 1-mm slice thicknesses are phantom at a slice thickness of 5 mm. Therefore, the 1-mm contour data was re-sampled tom-written MATLABa routine. Also, the decision was made prior to phantom construction that the arms of the phantom should be removable below the humeral head in order to facilitate correct positioning of the phantom for various types of simulated patient exams. Consequently, several different data sets were created using MATLAB, including a complete anatomical data set, a data set without arms, and a data set containing only arm data. Formatting and Output Phantom construction began with the formatting and outputting of the CT data set that was used to create the phantom. The CT data used to construct the UF newborn phantom consisted of 485 CT slices of a 6-day-old female cadaver, which was imaged within 24 hours of death. A helical CT scan was used for data collection, resulting in 51x 512 images with an in-plane resolution of 0.586 mm and a z-axis resolution of 1 mm. The cadaver mass was recorded at 3.83 kg.91 The IDL-based routine CT it fragile and difficult to manufacture, a decision was made to construct the newborn to a 5-mm slice thickness using a cus a The Mathworks, Inc. 3 Apple Hill Drive, Natick, MA 01760-2098

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38 Following the data manipulation described above, one final step was necessarytransfer the data into a format from which it could be easily transferred to the soft blanks used for phantom construction. Each of the individual slices was printed onto transparencies using a laser printer. The transparencies could then be cut with a hobby knife to make a stencil which was then be used to transfer the outline of each slice to a soft tissue blank. Figure 3-1 displays an example of one of the bitmap images that wosubsequently be printed onto a to tissue uld transparency for phantom construction. Figure 3-1. Typical bitmap image demonstrating the four regions (soft tissue, bone tissue, lung tissue, and air) used in this phantom. This image corresponds to Slice 28. Production of Soft Tissue Blanks The beginning point for each slice of the phantom was a soft tissue blank, ranging in size from approximately 7.5 cm x 15 cm to 15 cm x 25 cm, depending upon the region of the body being constructed at the time. These blanks were created by pouring STES-NB,27,b a newborn soft tissue-equivalent material developed at UF, into molds formed from Teflon and clay. Teflon was chosen due to its non-reactive properties, allowing for easy removal of the blank with no potential for contamination of the STES-NB. The b Information about the composition and manufacturing of STES-NB can be found in Appendix A.

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39 method for mixing and pouring STES-NB, as well as the corresponding methods of producing BTES-NB and LTES (newborn bone tissue-equivalent and lung tissue-equivalent materials, respectively) are discussed in Jones et al.27 (this information can also be found in Appendix A). The molds were filled to depths of slightly greater than 5 mm, as some bubble formation was inevitable due to the mechanical stirring and curing processes involved in manufacturing the tissue-equivalent materials. A typical filled mold is shown in Figure 3-2. Figure 3-2. Example of the Teflon and clay mold used to form the raw soft tissue blanks. After the molds had cured, the final stages of blank formation were finished. First, the blank was machined using a jigsaw into the very basic outline of the current slice to be constructed. Second, the top portion containing any residual bubbles was sanded using a belt sander to form a smooth blank of a uniform 5-mm thickness. Figure 3-3 shows a sanded soft tissue blank ready to be used for slice formation. Also, at periodic intervals during the phantom construction process, samples of the soft tissue blanks were

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40 tested using Archimedes Principle (see Jones et al.27) to verify uniformity of the materialdensities. phantom. Formation of Slices The formation of each slice of the phantom began with the aforementioned soft tissue blanks. First, the outline of the slice to be constructed was transferred to the blank via the transparency method discussed previously. The regions representing bone tissue and air were removed from the transparency, and the outli Figure 3-3. Sanded blank (5 mm) used to create the soft tissue outline of each slice of the ne of the slice was cut from the transp were removed using both a jigsaw and a rotary tool. In addition, several regions of the anatomy, including the head and spine, contained regions of bone completely arency as well. In addition, if the slice contained lung regions, the lung portions ofthe transparency were carefully removed and saved for creation of the sections of lung tissue (described in the Lung Construction section). Next, the visible outline of the slice was cut using a jigsaw. Fine adjustments were made using a rotary tool with a sanding band. Following the shaping of the outline, regions containing bone, lung, or air

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41 surrounding a region of soft tissue. Therefore, it was important to label and save thessoft tissue islands, which were placed in their correct position in the corresponding slice before bone introduction. After all machining was finished to create the soft tissueoutline of each slice, the 32 pixel spaced grid was traced onto the top and sides of eacslice to facilitate correct alignment during phantom assembly. Figure 3-4 shows a grouof phantom slices at this point in construction. These slices are now ready for bone introduction, which will be described in the following section. e h p Figure 3-4. Slices after Step 3 of the phantom creation process. Soft tissue outlines have been created and grid has been transferred to the slice. Slices are now ready to be prepared for bone introduction. Bone Introduction Following the construction of a batch of soft-tissue slices (25-30), bone tissue-equivalent material (BTES-NB)27,c a homoge neous mixture of cortical, trabecular, and marrow tissues was introduced into the regions containing bone. This process began by using tape to mask the bottom of the individual slices to (1) prevent the BTES-NB c Information about the composition and manufacturing of BTES-NB can be found in Appendix A.

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42 from running under the slice, and (2) hold the BTES-NB in the void where it was poured while it cured. A second item of preparation included marking the regions containing air with a pencil mark so there was no confusion as to which regions were to be filled when the fresh BTES-NB was mixed and ready to be poured. After an appropriately sized batch of BTES-NB was mixed (usually 200-400 g), a syringe with a large bore needle was filled with BTES-NB, and the end of the syringe ake a small, narrow opening to allow for better control of the flow of ng bone were then systematically filled with refilling as needed. It is important to note here that regions smaller than approxde to ent. After leaving the BTES-NB to settle for a few med additional material to fill them completely. If regions needed additional BTES-NB, it was applied and allowed to cure. Next, the masking was removed from the slices, and a was clipped to m bone-equivalent material. Regions containi the syringe contents, imately 1 mm in diameter are very difficult to fill, and it is impossible to visually confirm that the BTES-NB has filled the void with no air spaces. Therefore, bone regions smaller than 1 mm in size were not created in this particular phantom. A wooden toothpick was found to be an excellent tool for prodding BTES-NB into the smaller voids in the slices and eliminating air spaces in bone-filled regions. Indeed, a toothpick was inserted into each region after the first pass with the syringe was maeliminate any air spaces that might be pres inutes, a second pass was made with the syringe, and each region was overfilled slightly. This was done because as the BTES-NB cures, it reduces in volume slightly, and a perfectly filled region will end up needing more BTES-NB material when cured. After the slices that had been filled with BTES-NB had been allowed to cure for several days, a visual inspection was performed to identify which regions, if any, need

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43 combination of detail sanding and block sanding was used to flatten the bone regions and remove any film of BTES-NB fro m the top or bottom of the slices. An important exception to the above processes involved the slices containing lung uced into t regions, which could not be constructed in a similar manner. BTES-NB was introd hese slices after the lung material (LTES) was inserted. Figure 3-5 shows a group of slices after the bone introduction process. Figure 3-5. Slices after bone introduction. Lung Construction The construction of the lungs was one of the more challenging tasks involved in the development of the UF newborn phantom. Because our data set came from a newborn CT image set, the lungs were very small, especially in the most superior and inferior extents of the organ. This fact, combined with the fact that the lung tissue-equivalent material (LTES)27,d used to construct the lungs expands to approximately three times its original volume during the foaming/curing process, made it necessary to consider alternative means for lung construction. d Information about the composition and manufacturing of LTES can be found in Appendix A.

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44 The approach used for lung construction was similar to the process used to form the original soft tissue slice outlines. LTES material was mixed and poured into sections of 3 PVC pipe to foam and cure. After curing, the sections of LTES were removed and cut into round 1-cm thick blanks. Then, just as in the construction of the soft tissue slices, the blanks were sanded until they reached a uniform thickness of 5 mm.e The blanks were then ready to be used for creation of the lungs. A set of these 1 cm blanks (prior to sanding) is shown in Figure 3-6. Lung tissue blanks cut into slices after removal from PVC pipe Figure 3-6. mold. oft the as The lung slices were formed by tracing on paper the outline of the void in the stissue slice that was to contain each particular slice of lung (remember, the voids would eventually contain both the lung and the bone tissue that belonged in them). Next, the outline of the lung portion of the transparency (which was saved) that corresponded tocurrent soft tissue slice was traced in the appropriate position on the outline already present on the paper. In this way, an outline of only the lung tissue (minus the bone tissue) could be created. This outline was then removed using a hobby knife, and tapedto a lung tissue blank using double-sided tape. Finally, a fine-toothed jigsaw blade wused to cut around the paper outline, creating a lung slice that would fit tightly within the Care must be taken with the fragile lung materiala fine grit sanding belt should be used. It is alnecessary to lightly tap the blanks against a firm surface several times on each side to ensure that a eso ll the dust created by the sanding process is removed.

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45 corresponding soft-tissue slice. A group of soft tissue slices, along with their corresponding saved portions of lung from the transparencies, is shown in Figure 3-7. Figure 3-7. Soft tissue outlines for phantom slices containing lung tissue and corresponding lung regions saved from bitmap transparencies. After all of the lung slices were created, it was necessary to find a method for securing them into their appropriate soft-tissue slices. Also, it was important to prothe edges of the lung slices that would be adjacent to freshly poured BTES-NB material (e.g., ribs). Therefore, the portions of the edges of the lung slices that would contact BTES-NB were m tect asked with strips of a very thin masking tape. The tape prevented the incursation ion of BTES-NB into the lung tissue while at the same time leaving the attenucharacteristics of the phantom virtually unchanged. After masking the edges, the lung slices were secured in their proper positions using a very small amount of STES-NB (STES-NB is much more viscous than either BTES-NB or any glue that could have been used, therefore there was no chance for the STES-NB to seep into the lung tissue).

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46 The final step in the lung construction process involved filling the remaining voAny small voids (other than bone-tissue containing voids) that remained after the insertion of the lung slices were filled with small amounts of STES-NB. Finally, the bottoms of the individual slices were masked with tape, and BTES-NB was introduced into the appropriate voids as described in the previous section. The slic ids. es were then TES-NB regions had cured. Figure 3-8 shows a lung-containing phant sanded flat after the B om slice after the insertion of the lung, and filling with bone tissue. View of a phantom slice containing lung after bone introduction, prsanding. Figure 3-8.ior to Dosimecalizationassembly of the phantom, cnnels thaindrs into theations designad for point dmachined within the individulices of the phantom. A list r d is given able 3-1. The anatomical dlisted it column, followed by the slice number of the dosim. The third column lists the number of dosimeters in cted ter Lo Prior to final ha t allow for the insertion of ividual dosimete loc te ose measurements were al s of the locations selected fo osimeter placement in T escription of the location is n the firs eter locations the sele location and slice, and the

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47 fourth column contains any pertinent informati concerninumber of dosimeters in a parans listed in utilized (for the purpose of callating effective doses), whnecessary. Channels for dosimeter placement were routed into individual slices prior to phantombly to ensure that each dosimetewould be llocation. Constructing the channels in this manner ensures that each dosimeter will be locateended location within the phant, and allowe chann in the correcce and does ndeviate in ee z-direction. This is critical in the assessment of organ and effective dose, especially when using the computational and physical phantoms together, as it is extremely important that the locations ofth the simulated dosimeter and the actual dosimeter are identical. Ten more critical at tissue interfaces, such as between bone and soft tissuere significant attenuation gradients may ermanent assembly of the phantom was selected for simplicity reasons. Also, ed to a reader system and are very small, allowing for minimt on ng the location of or the ticular site. Loc tio bold print are always cu ile other locations are used as assem r ocated exactly in its planned d at its int om s one to make sure that th el remains t sli ot ither the positive or negativ bo his is ev e regions, wh be present. Phantom Assembly P permanent assembly eliminates the possibility of losing or misplacing portions of the phantom. Permanent assembly was possible because the dosimeters intended for use with the phantom are attach al alteration to the phantom in order to facilitate their correct placement. A commercially available wood glue was selected as the adhesive for phantom assembly. Previous work with tissue-equivalent phantoms in the UF Pediatric Organ Dose (POD) Project has found that wood glue behaves most like soft tissue-equivalenmaterials in an x-ray beam and is virtually indistinguishable from soft tissue-equivalent

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48 Table 3-1. Locations selected for dosimeter placement in the tomographic physical measurements, while other locations may be included as required. phantom. Locations in bold print are always used in effective dose Dosimeter Site Slice Number No. of Dosimeters Notes Skin* Varies with field 1 Placed in the center of the x-ra y field Skull (Right + Left) 5 2 Centered in the antero-posterior direction terior and anterior Skull (Post. + Ant.) 11 Centered in the lateral direction L eye/lens* 15 Skin dosimeter used as surrogate Maxilla (Right + Left) 17 2 Thyroid 24 1 Centered laterally 1 Located 1/3 of the way through the lung 1 Located 1/3 of the way through the lung all Ribs 33 2 One dosimeter in each rib cage Left lung 35 1 Located 2/3 of the way through the lung required two dosimeters Gallbladder 42 0 Mean of liver dosimeters used as surrogate Pancreas 43 1 Gut 44 1 Small intestine location Left kidney 46 1 Also describes left adrenal gland Right kidney 48 1 Also describes right adrenal gland Gut 49 1 Large intestine location Gut 53 1 Large intestine location Lumbar vertebra (L3) 55 1 Positioned in the vertebral body Illium (Right + Left) 57 2 Ovary (Right + Left) 59 1 One dosimeter located between the ovaries Bladder 60 1 Rectum/sigmoid colon 61 1 Uterus 62 1 Femoral heads (Right+Left ) 64 2 Located in the proximal femur heads Brain 10 2 Pos 2 0 R eye/lens* 15 0 Skin dosimeter used as surrogate Cervical vertebra (C4) 24 1 Positioned in the vertebral body Thymus 30 1 Left lung 31 Right lung 31 Esophagus 32 1 Positioned in the esophageal wHeart 34 1 Right lung 35 1 Located 2/3 of the way through the lung Thoracic vertebra (T6) 37 1 Positioned in the vertebral body Spleen 41 1 Stomach 41 1 Liver 42 2 Large organ *These dosimeters will be positioned on the surface of the phantom

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49 m aterials in images.92 Also, it is much preferred to STES-NB or a plain epoxy-hardener combination due to its more rapid curing and ease of removal from unintended locations during phantom assembly. g by with a ding. assembled from the resulting large sections of assembled phantom. Results The manual construction process used to build this phantom produced excellent results. Figure 3-9 shows a bitmap image corresponding to slice 27 of the phantom, and also slice 27 following bone introduction, prior to final sanding. As one can see, the final product matches the template extremely well. Figures 3-10 through 3-12 provide various views of the completed phantom. Figure 3-10 is a front view of the completed phantom, without arms. Figure 3-11 is a profile view of the completed phantom, without arms. Figure 3-12 is a front view of the phantom, with arms. Note from these figures that the arms and legs were truncated at the feet and hands to prevent breakage during use and transport. Slices were glued together two-by-two, with the first pair of slices acting as a seed to which the subsequent pairs were attached. Slices were prepared for gluinsanding the surfaces to be glued with a fine grit sandpaper, and wiping them cleandamp cloth. A dry matching was attempted first, to make sure the slices would matecorrectly. If necessary, small corrections in the slices were then made by block sanGlue was applied sparingly to the surfaces to be attached, and the slices were aligned using the grid system described in previous sections. After alignment, the slices were clamped and allowed to cure for at least 36 hours. The body was then completely

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50 Figure 3-9. Slice 27. (A) Bitmap image corresponding to Slice 27. (B) Slice 27 after bone introduction. Discussion Some aspects of phantom construction underwent several iterations before we settled upon their final form. Several methods for making soft tissue blanks were examined before one was finally selected. The original plan was to construct the phantom using a slice thickness of 2 mm. Two methods of constructing these slice thicknesses were attempted; one utilizing thin Teflon molds, the other utilizing an aluminum slab coated with a Teflon spray with 2 mm high aluminum borders around the edges. Both methods produced satisfactory blanks, however upon examination of the blanks it was noticed that a small amount of settling of the epoxy base had occurred, A B

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51 Figure 3-10. Photograph of the completed phantom, without arms.

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52 Figure 3-11. Profile photograph of the completed phantom, with out arms.

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53 Figure 3-12. Photograph of the completed phantom, with arms.

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54 leadincreated, no such problem was observed, and thus a 5-mm slice thickness was selected as the mdescrapprothis dd not be in one piece. an apright proble undertaking. Also, anner would have prohibited us from using the dosimeter ral f rior, posterior, or either lateral s it prevents radiation streaming into g to a stratification of the blank. When the 5-mm blanks described previously were inimum slice thickness for future phantom construction. Several ideas were also entertained for lung construction before the previously ibed method was chosen. The simplest idea was to introduce the LTES into the priate voids in the soft tissue blanks, as was done with the BTES-NB. However, id not allow for adequate foaming of the lung material, and the correct density coul achieved. The second idea involved attempting to create the lungs First, the torso section of the phantom would be assembled after bone introduction, then propriately sized batch of LTES mixed and poured into the cavities for the left and lungs, where it would foam and cure. This method was rejected because if any ems occurred, such as the lung material failing to achieve the correct density, removing and replacing the cured lung material would be a larg constructing the lungs in this m placement strategy described previously. When deciding on dosimeter locations and a dosimeter placement strategy, seveproblem areas became apparent, necessitating the creation of a set of goals for dosimeter channel placement. Three goals that were established during the machining othe dosimeter channels are listed below. 1. All dosimeter channels should follow an oblique path, i.e. no dosimeter channel should proceed in a straight line from the antedirection in the phantom. This is important a the dosimeter channels, which may create artificially high dose readings. Oblique exams are far less common than AP, PA, and lateral exams in projection radiography. In CT, the contributions from all angles are averaged as the beam isconstantly moving, and thus radiation streaming into an individual dosimeter

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55 channel is of very little concern. There has been no evidence of radiation streamduring initial measurements using the phantom. Each dosimeter channel was machined in the correct side of the slicetop or bottom. This was done depending upon where the dosimeters were located in the contoured data set to be used for computational simulations (dosimeters were placed in the computational data set by the author). If a dosimeter was located in the superior two 1-mm CT data slices that formed the single 5 mm phantomthe corresponding channel in the physical p ing 2. slice, hantom was machined within the top of that slice. If a dosimeter was located in the inferior two 1 mm CT data slices that in the bottom of that slice. If a dosimeter was located in the middle 1 mm CT data slice that formed the single 5 mm phantom slice, the corresponding channel in the physical phantom was machined alternately in the top 3. The channels were machined to be just large enough to accommodate several types f dosimetry systems, such as optically cabled OSL should they become available. The channels will also accommodate traditional passive devices such as TLDs. Figure 3-13 shows some typical dosimeter channels in completed slices, and also illustrates the application of our dosimeter placement strategy. formed the single 5 mm phantom slice, the corresponding channel in the physical phantom was machined with or the bottom of the slice in each instance when this was the case. of dosimeters, including FOC dosimeters, MOSFET dosimeters, and other types oBy doing this, the dosimetry system in the phantom can be updated as necessary. Figure 3-13. Typical dosimeter channels in completed slices.

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56 Just as areas of concern became apparent during dosimeter placement, several issues were also encountered during phantom assembly. One issue common to all body sectio the trachea and nasal sinuses. This was possible because the head was glued were accessible after each addition. 2. Torso: Again, care was taken to remove glue from the trachea. Also, the lungs were masked before BTES-NB was poured around them. Masking the entire an option, so other options were considered, including using a smaller amount of a stronger adhesive. However, this did not provide an adequate bond between slices. Therefore, wood glue was used, but applied only to areas of the slice exclusive of lung tissue, and in very sparing quantities. Also, the glue was allowed to cure slightly before assembly and clamping to prevent migration of the glue as much as possible. 3. Abdomen/Lower body: No unique issues were encountered during construction of this section of the phantom. Conclusions The UF newborn phantom possesses several advantages over existing pediatric phantoms. First, this phantom is the only tomographic physical phantom in existence, created completely from a detailed patient CT image data set. This means the anatomy is real and not just realistic, and the anatomy used for phantom construction is well-documented, and close to standard reference values. Second, this phantom has a computational twin, a segmented computational phantom that was created from the exact same image data set including all internal soft-tissue organs, bones, and other structures of interest.91 This combined system of physical and computational phantoms ns was the filling of dosimeter channels with glue. This was easily prevented by using a straight piece of wire to clean the glue from inside the channels after the sliceswere clamped together. There were also some problems specific to certain areas of the body of the phantom. These are addressed below: 1. Head: Care was taken to remove any glue that made its way into air spaces such as together from the top down two slices at a time, so the trachea and sinus cavities presented a special problem in this section, for the same reasons that their edgessurface of the lung was not

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57 allows for the calculation of point-to-organ dose scaling factors (SFPOD)82,90 that will be used to calculate average organ doses delivered to the phantom during various diagnostic exams, and also enable the calculation during thslices of any materials and used to construct this combined ptry system have been extensively testedes of all o of of effective doses delivered to the phantom ese same exams. Next, this phantom utilizes a slice thickness of 5 mm, the thinnest pediatric physical phantom available. Also, the tissue-equivalent hantom/dosime (using MOSFET dosimeters) and compared to Monte Carlo simulations in an experiment which demonstrated their usefulness (see Chapter 2). Finally, we have developed a methodology for construction that can be extended to all sizes and agphantoms. The phantom itself will be used to measure effective and average organ doses intypes of diagnostic exams, and much of the data acquired will be combined with data such as image quality assessments and the like to form a comprehensive assessment of the current state of pediatric radiology. Also, the phantom has the potential to be used as a teaching tool for x-ray technology students if so desired. Finally, the techniques used tcreate this phantom and the pitfalls encountered during construction will allow for the phantom construction process to be streamlined, and the automation of the phantom construction process is currently underway, bringing the goal of creating a familyphysical phantoms within reach.

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CHAPTER 4 A TOMOGRAPHIC, PHYSICAL PHANTOM OF THE NEWBORN CHILD WITH REAL-TIME DOSIMETRY. II. SCALING FACTORS FOR CALCULATION OF AVERAGE ORGAN DOSE IN PEDIATR IC RADIOGRAPHY or dose re t due ators bsorbed dose are particularly problematic for distributed organs such as the active bone marrow and skelimeter locations are known in advanon rgan dose to the gan dose scaling factor, or SFPOD Introduction The currently used and accepted method for organ dosimetry in anthropomorphic physical phantoms involves the placement one or more thermoluminescent or other dosimeters within a body region or location. The measurement is then used as a surrogate value for the absorbed dose averaged across the entire organ as needed freconstruction studies in epidemiology or in the assignment of the effective dose for radiation protection.10 The measurement of a point dose may not be an accurate measuof the absorbed dose averaged across the entire organ when large dose gradients existo photon attenuation with depth and/or partial field coverage. Similarly, point estimof the average a etal endosteum. If the dos ce, and the irradiation geometry of the patient is fixed, computational simulatimodels of the patient phantom may be used calculate both quantities in the same anatomical representation of the patient. The ratio of the true average o point dose estimate is defined in this study as the point-to-or The SFPOD differs from the dose conversion coefficient currently used to calculate organ doses in computational phantoms. With conversion coefficients, Monte Carlo simulations are used to calculate individual organ doses based on free-in-air dose 58

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59 quantities, such as air kerma or exposure. Conversion coefficients can be used for radiology dose calculations, and are commonly used to calculate organ doses in computational phantoms. Veit and Zankl have used a different type of organ dose scaling factor in their studies.94 This factor is used to calculate organ doses in patients of various sizes based on the organ doses in a standard patient. The concept of the SFPOD was first proposed by Sessions et al. in their work with a stylized physical newborn phantom created at the University of Florida.82 The approach has resurfaced in the Pediatric Organ Dose (POD) Project with the recent completion of incorporateurpose of the present study was to create a ure 4-1) created in the POD Project at the niversity of Florida, and subsequently use the scaling factors to calculate accurate values for organ and effective doses delivered within various newborn radiographic Materials and Methods The following sections will describe in detail both the creation of the scaling factors using the computational phantom, and their use in calculating effective doses in the physical phantom for various radiographic newborn examinations. Modifications to the Newborn Computational Phantom The newborn tomographic computational phantom was constructed from emale cadaver as previously described by Nipper et the original publication, the newborn phantom has been modified to include onal organs that were not originally segmented, and revisions to exist construction of a state-of-the-art tomographic physical newborn phantom with an d dosimetry system. The p comprehensive set of SFPOD using the identical computational91 and physical (see Chapter 3) tomographic newborn phantoms (see Fig U examinations. segmented CT data of a 6-day-old f al.91 Since both add iti ing

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60 Figure 4-1. UF Newborn showing the internal organ structure (left) and exterior (middle) of the computational phantom along with the exterior of the corresponding m (right). igure 4-2). Table 4-1 gives a list of all segmented organs and their masses with physical phanto organs ( F identification of new and modified organs. Figure 42. Axial slices through the UF Newborn phantom showing modified aadditional organs and tissues. include the salivary glands, larynx, pharynx, escribed in this paragraph and the following paragraph olon, nd Organs that were added in this study and trachea (all modifications d were performed by Choonik Lee). The lower gastrointestinal tract that was originally segmented as gut was further segmented into the small intestine, left colon, right c

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61 Table 4-1. Organ and skeletal masses within the modified newborn computational Organ phantom Mass (g) Res p ir ator y Trac t Lar y nx + 1.30 Phar y nx + 0.25 Trachea + 0.60 Lun g s 32.52 Alimentar y Trac t Salivar y Glands + 5.99 Eso p ha g us 2.59 Stomach Wall 7.00 Small Intestines Contents 22.73 Ri Stomach Contents 6.14 Small Intestines Wall 33.97 g ht Colon Contents 5.21 Ri g ht Colon Wall 7.46 Rectosi Left Colon Contents 7.20 Left Colon Wall 7.35 g moid Colon Contents *2.99 Rectosi g moid Colon Wall Live 2.77 r 109.13 Gallb ladder ( wall + contents ) 2.19reas 1.33Circulator Panc y S y stem Hear t 21 Uro .13 g enital Sy stem Kidne y s 21.58 Urinar y Bladder Contents 6.48 Urinar y Bladder Wall 4.00 Ovaries 0.29 Uterus 3.52 S keletal S y stem Skull ( cranium + facial bones ) 107.38 Mandible 7.38 Le g bones ( femora tibiae fibulae p atellae ankles and feet ) 34.78 Arm bones ( humeri radii ulnae wrists and hands ) 2 4.16 Sca p ulae 7.303.08 Clavicles Ribs and sternu m 34.62 Pelvis 14.02 S p in e ( all vertebrae ) 47.99Inte g umentar y S y stem Skin 102.Other Or 10 g ans and Tissues Adrenals 3.01 Brain E 291.38 y es ( with lens ) 2.93 Remainder tissues ( with arms and le g s ) 2509.17 S p inal Cord 15.13 S p leen 7.64 Th y mus 10.00 Th y roid 0.56

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62 Table 4-1. Continued Total Bod y Mass ( k g) 3.54 Modi f born d n these locations can be found in Chapter 3). Each dosimeter regions fied organs, + Added organs and rectosigmoid colon following the ICRP 89 definition.14 For all regions within the lower gastrointestinal tract, wall and contents were defined separately. Identifiable regions of bowel gas were also segmented within the colon. Revisions were also made tothe stomach and bladder to include a delineation of their wall and contents. Though their relative anatomical locations were identifiable, these soft tissue organs were somewhat difficult to distinguish from surrounding tissues due to the lack ocontrast among soft tissues in the original cadaver CT data. During segmentation, attempts were made to match organ masses to those in the ICRP 89 reference newdata.14 The final revised newborn phantom includes a total of 80 segmented organs antissues. Newborn tissue compositions and densities published by Cristy and Eckerman85 that were used in the creation of the newborn phantom tissue substitutes were alsoemployed in the newborn computational phantom. Simulated dosimeter placements were also included (by the author) in the newborcomputational phantom to match those previously described in the corresponding physical phantom (a list of was modeled as a 4 4 3 (x, y, z) group of voxels with a resulting volume of 14.9 mm.91 This region is slightly larger than the actual volume of a physical dosimeter, but was required to ensure sufficient statistical precision in the Monte Carlo simulationof x-ray exposure. Each simulated dosimeter was assigned the same density and elemental composition as the surrounding tissue.

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63 The following sections describe, in detail, the development (computationally) of the point-to-organ dose scaling factors (SFPOD), work done in large part by Robert Staton.95 The author performed all measurements (including exposure measurements), inaddition to aiding in the tube characterization and development of the SFPOD. Monte Carlo Codes for Radiograph Simulation The Monte Carlo radiation transport code EGSnrc96 was selected for simulations due to its versatility in handling large voxel arrays. With a rectilinear array of 512 5 485 voxels, the newborn tomographic computational phantom is represented by a 12 matri e acquired to assess the absorbed -absorbed dose response functid down ctangular fields of view in the antero-posterior (AP), postero-anterior (PA), right lateral (RLAT), and left lateral x of over 127 million individual voxels. Each voxel has a tag (ID value) belongingto a given segmented organ, tissue, or dosimeter location. For each segmented region, energy deposition was tracked across all voxels to report absorbed dose. The tally withineach region was obtained in units of absorbed dose (MeV per g) per launched photon. For skeletal sites, volume-averaged energy fluences wer dose to the active marrow through the use of fluence-to ons.97 The absorbed dose to homogeneous bone was used as a surrogate for the absorbed dose to the skeletal endosteum for all bone sites.98 Photons were followeto an energy of 1 keV and secondary electrons with kinetic energy below 10 keV deposited their energy locally. All radiograph simulations were run with 500 million initial particles to achieve acceptable statistical errors for organs (<1%) and dosimeter locations (<1%) inside the field-of-view. Simulated fields of view For the development of a general set of scaling factors, four whole-body viewswere simulated for the tomographic computational phantom: re

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64 (LLAad to e ined to be 0.91. For organthe erate energy spectra: peak tube potential (kVp), le, and added filtration. T at Shands Hospital that was used was characterized and found to have a HVL of 3.74 mm of Al and a voltage ripple of 5.0% at 80 kVp. Values of total filtration were evaluated by iteration in a series of Monte Carlo T) directions. The whole-body field-of-view extended from the top of the hejust below the femoral heads and covered the entire lateral extent of the phantom. Whole-body fields-of-view were used to generate a comprehensive set of SFPOD values that can be used for any field that has partial coverage within the torso (i.e., chest, abdomen, pelvis, etc.). The whole-body fields were found to produce SFPOD values that were very similar to values for partial coverage fields for organs fully within thfield. For example, the SFPOD for the right lung for a chest field at 66 kVp was found to be 0.90, while the same value for a whole-body field was determ s outside of the field, minor differences in the SFPOD values are not important since the average organ doses themselves are very low in comparison to those within the x-ray field. For example, the average organ dose for the bladder wall for a chest field at 66 kVp is about 70 times smaller than the value for the right lung. For all lateral and chest views, the arms were removed (by deleting voxels of arms which were distal to the humeral epiphyses) to represent clinical positioning of the patient. For AP and PA projections, lateral field boundaries were chosen just outside of the most lateral extent of the trunk. All lateral views were restricted to the outermost extent of the anatomy in the antero-posterior direction. X-ray source modeling and beam characterization The x-ray energy spectra needed for Monte Carlo simulations of the projection radiographs were generated using the tungsten anode spectral model TASMIP.87 TASMIP requires three parameters to gen voltage ripphe x-ray tube

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65 simulations of filtered x-ray fields so that predicted values of HVL matched measured HVL values at 80 kVp. X-ray beam characteristics for input to TASMIP were set avoltage ripple of 5.0% and an added t a filtration of 2.35 mm Al. ton point source with angul The n whereby ons to y, ion chamber measurements of air kerma were e For all simulations, the x-ray unit was modeled as a pho ar sampling slightly larger than the field of view and forming a conical beam. beam was then reduced to the actual field of view through simulated collimatiophotons starting outside the rectangular field-of-view were terminated. To accurately simulate pediatric radiography, the source was positioned at a source-to-image distance (SID) of 100 cm. The SID was defined in the simulations to be the perpendicular distance from the source to the farthest extent of the phantom at the center of the field. Monte Carlo ionization chamber simulations To relate organ doses calculated within the Monte Carlo radiograph simulatithose a newborn patient would receive clinicall made for each radiographic view. Free-in-air measurements 95 cm from the focal spot of the x-ray tube were recorded experimentally for each field-of-view with a Keithley Model 96035B dual entrance window ion chamber and a Keithley Model 35050A electrometer. All measurements were recorded at clinically relevant technique factors for a patient with a mass equivalent to that of the newborn tomographic phantom(~3.54 kg). Next, the ion chamber was simulated within EGSnrc, applying the samdimensions and material compositions of the ion chamber used experimentally. The air kerma free-in-air within the modeled ion chamber was calculated in units of absorbed dose (MeV per g) per launched photon. Simulations were run to mimic the field size at the patients surface for each of the examinations studied. Values of organ/tissue absorbed dose DT are thus calculated as:

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66 ,TMCMCDDEAKEAK (4-1), where DT,MC is the Monte Carlo estimate TIC of tissue absorbed dose, EAKMC is the Monte arlo esttrance air kerma, and EAKIC is the ion chamber measuage e simulated dosimeter. For organs with multiple dosimeter locations, the average of all point doses within the organ was used in the SFPOD calculation. MC simulations of AP, PA, RLAT and LLAT whoich is the omogeneous bone absorbed dose and were indexed to each skeletal region of the The MDCF thus allows for the conversion of each homogeneous bone point dose measurement (DHB) to an active Cimate of the free-in-air en rement of that same quantity. Creation of Point-to-Organ Dose Scaling Factors (SFPOD) For each whole-body MC simulation, SFPOD values were calculated for all organs/tissues which contained dosimeter locations (see Chapter 3). For each organ/tissue, the SFPOD value was calculated as the ratio of (1) the MC calculated averorgan absorbed dose and (2) the MC calculated absorbed dose in th le-body fields were simulated at tube potentials of 66, 80, and 100. The results of these simulations were used to develop a comprehensive set of SFPOD for general application in pediatric newborn radiography. Methods were also developed to calculate skeletal-averaged red bone marrow and bone surface absorbed doses from point dose measurements within the skeleton (whcurrently modeled as a homogeneous mixture of active marrow, cortical, and trabecular bone tissue) of the newborn phantom. For active bone marrow, marrow dose conversionfactors (MDCF) were calculated as the ratio of the active marrow absorbed dose to h phantom. The active marrow dose in each bone site was calculated using fluence-to-dose response (FDR) functions published by Cristy and Eckerman.97

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67 marro w absorbed dose for a given skeletal region. Since the FDR functions are a function of the x-ray spectrum incident on the skeletal region, the corresponding MDCF values are indexed to the x-ray energy spectrum incident on the phantom and to the radiographic projection. The MDCF value for a given skeletal region, b, is thus calculated by the following expression: ,,, ,bEbEkVpProjEbbHBFDRdEMDCFD (4-2). The active marrow dose for a single skeletal region is calculated as the producthe dosimeter dose and the appropriate MDCF. The skeletal-averaged active marrow absorbed dose is then calculated as a mass-weighted average of the active marrow doseach skeletal region. Values of the marrow weighting fa t of e in ctor (MWF) for skeletal region b marrow ab() ()kVpProjkVpProjdosimeterbbbbDMDCFMWFmGy (4-3). used for calculating the skeletal-averaged bone surface absorbed dose is thus given as: for the newborn are used as published by Cristy.99 The skeletal-averaged active bone sorbed dose is thus given by the following expression: __ ()ActiveMarrowSkeletalAverageD ,, Similar methods were also used to calculate skeletal-averaged absorbed dose to thebone surfaces. However, the homogeneous bone dose has been found to be a better estimate of the bone surface dose than calculations involving FDRs.98 The point dose measurement in each skeletal region was used to approximate the bone surface absorbed dose for that region. These point doses were then mass weighted across the entire skeletal according to a bone weighting factor (BWF) defined as the fraction of total bonemass contributed by that skeletal region b in the newborn phantom. The equation

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68 b __b ()MOSFETBoneSurfacesbSkeletalAverage D DBWFmGy (4-4). For bre the skin report within Robert Staton and Aaron Kyle Jones at the University of Florida. This spreadsheet allows the user to measurements, and returns values for average organ doses and effective dose ng a a one sites with multiple dosimeter locations, the average of point dose measurements within the bone sites was used in both Equations 4-3 and 4-4. Methods were also developed to estimate skin and remainder doses since both aalso needed in the calculation of the effective dose. A dosimeter was placed onsurface at the center of each field in the phantom and in the MC simulations. In the MC simulations, a total skin dose can be calculated, which is not possible using the phantom. Using the MC simulations of each specific field, the ratios of the point dose measurements to total skin doses were used to calculate the SFPOD values needed tototal skin doses from a single dosimeter measurement on the physical phantom. The remainder tissue absorbed dose was taken as an average of all point doses given by dosimeters located within the soft tissues of the physical phantom (i.e., not located skeletal or lung regions of the phantom). Object 4-1 contains the Radiograph Organ Dose Calculator developed at the University of Florida. Object 4-1. Radiograph Organ Dose Calculator developed by input the type of exam, the tube potential used, and values for point dose(61.5 KB, Radiograph_Organ_Dose_Calculator.xls). Newborn Radiographic Exams The radiographic exams of the experimental portion of this study were performed on the UF tomographic physical newborn phantom. Dosimetry was performed usiMOSFET dosimetry system manufactured by Thomson and Nielsen Electronics, Ltd., a Thomson and Nielsen Electronics Ltd., 25E Northside Road, Nepean, Ontario, Canada, K2H 8S1.

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69 including the TN-RD-60 patient dosimetry system (which includes dual-sensitivity bias supplies used at the high se nsitivity setting for this experiment) in conjunction with the TN-1g P ch the t a tube potential of 66 kV, which was determined from the e hospital. A tube current of 200 mt effective dose values were then normalized to units of Sv/m35B limited toultaneous measurements. Dosimrs were plall availa 002RD dosimeter, the most current dosimeter in the isotropic, high-sensitivity line of dosimeters. The dosimetry system, along with the phantom components, were previously evaluated against Monte Carlo simulations to verify their performance in diagnostic radiology89 (see Chapter 2). All radiographic exams completed on the newborn phantom were performed usina diagnostic x-ray tube at Shands Hospital at the University of Florida. The radiographicviews selected were AP Skull, Lateral Skull, AP Chest, PA Chest, Lateral Chest, AAbdomen, and AP Pelvis. These views were not selected as a comprehensive representation of newborn radiography, but rather as a general set of exams on whiscaling factors could be tested. All exams were performed a technique charts used at th As was utilized throughout the study to give better statistics and shorten the experimental time. The resultan As. X-ray technicians at Shands were recruited to properly position the phantom and set the dimensions of the x-ray field for each individual exam. The MOSFET dosimeters used for dose measurement were calibrated against a Keithley Model 960dual entrance window pancake ionization chamber using the x-ray energies used throughout the experiment, as recommended by Jones et al.89 MOSFET dosimeter placement differed from exam to exam. The MOSFET dosimetry system used supports 20 dosimeters at any given time, and thus we were 20 sim ete ced in a ble

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70 dosimetes within the fif view, as well as locations bordering or in close proximity to the field edges. The large number of organs in somds (e.g., AAbdome that the bonimetry and issue dosimbe conduc separateasuade by placing a MOSFET in the center of each field. In several cases, MOSFET dosimeters werated just ou the x-ray fnd consequently they recorded very small absorbed dose values, priy from scattered radiationult, rules had set to deter which dosr locationsld be considered to have received a non-zero dose for purposes of calculating the effective urate estimResults and Discussion A comprehensive set of radiographic SF values calculated for the newborn phantom isIt can immediately be rge, walled,the livh wall, and FPOD valuesnificantly less than or greatepending ofic projectiolso be seen thatl organto id r location eld o e fiel P n) required e dos soft t etry ted as e mments. Also, a skin dose m easure rement was m e loc tside ield a maril As a res to be mine imete wou dose. The decision was made that any dosimeter that recorded two or fewer non-zero dose measurements out of seven total measurements would be disregarded. Also, along with dosimeters that recorded two or fewer non-zero readings, dosimeters whose measurement errors were equal to or greater in magnitude than the absorbed dose measurement itself were also disregarded. By taking these simple steps, acc ates of organ and effective doses could be calculated. POD given in Table 4-2. seen that la or widely distributed organs such as er, stomac skin have S that are sig er than 1.0, d n the speci n. It can a smal s such as the ovaries, uterus, and eyes have associated SFPOD values very close 1.0, as expected. Also of interest are SFPOD values associated with the rectum/sigmocolon. The reason for the large departure from unity for these scaling factors arises from

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71 Table 4-2. Radiographic SFPOD for the newborn phantom (valid over the range 60-100 Organ AP PA RLAT LLAT kVp). Brain 0.86 0.99 0.98 1.04 Left Eye 1.00 1.02 0.92 1.01 Right Eye 1.01 1.01 0.99 1.06 Thyroid 0.92 0.97 0.95 1.03 Thymus 1.06 1.08 0.99 1.03 Left Lung 0.98 0.95 0.84 0.98 Esophagus 1.05 0.97 0.84 0.85 Heart Spleen Right Lung 0.91 0.94 1.03 1.05 1.01 1.07 1.00 1.05 1.08 0.94 1.10 1.02 Stomach Wall 0.82 1.32 0.97 0.93 Liver 0.89 1.05 0.94 Pancreas 1.02 0.98 1.12 0.99 Left and Right Colon 1.01 1.13 1.00 1.28 97 Left Ovary 1.01 0.99 0.98 0.99 r W99 1.04 1.28 Gallbladder 0.93 1.15 0.96 1.07 Left Kidney 1.05 0.97 1.05 1.01 Right Kidney 1.01 1.01 0.95 1.09 Small Intestine 0.89 1.32 0.85 1.14 Right Ovary 0.99 0.99 1.03 0.Bladdeall 1.05 0.96 1.05 0.94 Rectum/Sigmoid Colon 1.71 0.70 0.95 1.67 Uterus 1.00 0.95 0. the fact that improvements and modifications are sometimes made to the computational (as is the case with the rectum/sigmoid colon), as new tools become available. While the physical phantom is not easily modified or changed, these modifications can in essence The results of effective dose calculations utilizing the newborn physical phantom lated rs SFPOD. Projection/Exam E with SF E without SF Percent Error (%) phantom, such as segmentation of organ walls or further segmentation of existing organs be transferred to the physical phantom through the use of scaling factors. are displayed in Table 4-3. Table 4-3. The effective dose per unit integrated tube current (Sv / mAs) as calcuwith and without the application of point-to-organ dose scaling factoPODPOD AP Skull 1.28 0.04 1.70 0.05 32.8 Lat Skull 1.44 0.06 1.92 0.08 33.3 AP Pelvis 8.72 0.55 8.01 0.51 -8.1 AP Chest 7.81 0.23 8.96 0.26 14.7 Lat Chest 5.67 0.24 6.22 0.26 9.7 PA Chest 5.80 0.33 5.82 0.34 0.3 AP Abdomen 13.0 0.44 13.1 0.44 -0.8

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72 Values calculated both with and without the use of the SFPOD values are liste(errors represent one st d andard deviation), as is the error associated with calculating effective doses without the aid of point-to-organ dose scaling factors. It is apparent that calculating effective doses using only point doses generally leads to an overestimation of the actual dose delivered to the phantom (the negative value for the AP Pelvis exam can be attributed mostly to the rectum/sigmoid colon modification discussed previously). Also, one can see that the error associated with using only point doses is magnified when there are relatively few organs of interest in the x-ray field, as demonstrated by the AP and lateral skull exams. Conclusions We have demonstrated that calculating doses, both effective and organ, using only point dose measurements in physical phantoms can lead to significant errors in certain projections. We have also demonstrated that by using an identical computational phantom, SFPOD values can be derived and used to calculate average organ absorbed doses within the corresponding physical phantoms. Computational phantoms alone have weaknessesas imaging technology becomes more and more sophisticated (e.g., helical 64-slice CT scanners), simulations will become more and more difficult and time-consuming than they already are, forcing more and more approximations to be made. However, with a physical phantom, it is a relatively simple matter to directly measure point absorbed doses delivered during various radiographic and CT examinations of the physical phantom. This being said, physical phantoms alone also have their shortcomings, namely the inability to accurately measure average organ doses, especially in large, walled, or widely distributed organs. Furthermore, physical phantoms are difficult or impossible to modify once constructed.

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73 However, as we have demonstrated in this work, a dosimetry system based upon a matched set of physical-computational tomographic phantoms is a powerful tool for determining the organ and effective doses ra delivered to patients during diagnostic diology procedures.

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CHAPTER 5 CHARACTERIZATION AND TESTING OF THE FIBER OPTIC-COUPLED (FDOSIMETER Introduction This chapter addresses the initial testing performed on the fiber optic-coupled (FOC) dosimeters used for computed tomography dose evaluation. Fiber-optic-c(FOC) radiation dosimeters are based on the detection of phosphorescence from a Cu1+-doped quartz fiber.59,62 This charge-trapping material is fabricated by doping fused-quartz glass with Cu1+ ions.60,61,63 The OC) oupled doped quartz perform is drawn into a fiber, and a short multimode optical fiber via a plasma fusion fiber splicer. The photoead Materials and Methods y of the testing performed on the FOC dosimeter was done using a singler length is attached to a ns released (due to prompt radioluminescence) during irradiation can thus be rpassively in real-time during the irradiation as they travel from the active area through the fiber optic cable and eventually impinge on some type of detector (photomultiplier tube (PMT), CCD, etc.). A more detailed description of the doped quartz and the fiber construction process can be found in references 59-63. The majorit FOC dosimeter read by a PMT. The dosimeter, reader, and software (simply scoring total counts) were supplied by Alan Houston and Paul Falkenstein from the Optical Sciences Division of the U.S. Naval Research Laboratory.a The single dosimete U.S. Naval Research Laboratory, Optical Physics Washington, D.C. 20375 aBranch, Code 5611 4555 Overlook Avenue, SW, 74

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75 consisted of an active ar ea 4 mm in length and 400 m in diameter. The active area was fusedfzel half-value layer of 2.35 mm of Al and 5% voltage ripple at 66 kVp. The sure measurements was a 15 cc Keithly Mode ive ents ations her tube current-time product values, ranging from 20 mAs ed to to a 1 m long fiber optic patch cable coated with an opaque, light-tight black Tecoating, and the active volume was coated first with a low refractive index clear polymer cladding, and then with an opaque black epoxy to ensure light-tightness. All tests described in this section were performed with the dosimeter resting on 2 cm of acrylic backscatter material. The x-ray source used was a clinical x-ray tube at Shands Hospital with a ionization chamber used for simultaneous expo l 96035B dual entrance window pancake ionization chamber, along with a KeithleyModel 35050A electrometer. Energy Dependence The energy dependence of the FOC dosimeter was tested by performing firradiations of the dosimeter at successively higher tube potentials, ranging in incremof 10 kVp from 40 to 120 kVp, while simultaneously irradiating the ionization chamber. A tube current-time product of 100 mAs was used for all irradiations. Using this data, values of the dosimeters sensitivity, in units of counts/mR, were calculated for each tubepotential. Dose Linearity The dose linearity of the FOC dosimeter was tested by performing five irradiof the dosimeter at successively hig to 200 mAs. A tube potential of 80 kVp was used for all irradiations. A linear fit was applied to the collected data, and the square of the correlation coefficient was usassess the dose linearity of the dosimeter.

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76 Angular Dependence Due to the active areas cylind rically symmetrical shape, only two orientations f the study. The FOC dosimeter was first i ents g using the same energies and the same tube. Four of the twenty-five 15 m fibers were selected for testing. Five measurement points were selected, and two irradiations of the four dosimeters were performed at each point. The first two irradiations were performed with the entire length of the fibers perfectly straight. Subsequent pairs of irradiations were performed while bending the fibers in circles of progressively smaller radii. The bends spanned one half of the circumference of each diameter circle, with the fibers exiting the bend 40 cm from the active area. The circle diameters used for the bends were 37 cm, 23 cm, 13 cm, and 6.7 cm. All irradiations were performed at a tube potential of 100 kVp and a tube current-time product of 500 mAs. The following sections will transition from characterization of the FOC dosimeter to the preparation of the 25 fiber FOC dosimetry system for use in in-phantom dose em will ensue, followed by were tested during the angular dependence portion o rradiated normally (x-ray beam incident perpendicularly on fiber) while restingflat on the acrylic backscatter media, and then irradiated tip-on. These measuremwere performed using a tube potential of 100 kVp and a tube current-time product of 100mAs. Dosimeter Response at Varying Bend Radii This test was performed using the FOC dosimetry system described below (and in Chapter 6), and using a clinical x-ray tube at the Orthopedic Institute at Shands Hospital. However, the dosimeters were calibrated prior to testin measurements. A description of the FOC dosimetry syst

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77 sectio e r polymer re ns addressing the sensitization of the FOC dosimeters, and finally a section on thecalibration of the FOC dosimeters. The Twenty-Five Fiber FOC Dosimetry System The system used in this work was constructed by Huston and Falkenstein in thOptical Sciences Division of the Naval Research Laboratory It consists of 25 dosimeters, each with an active length of approximately 4 mm, with each fiber having a 400 m core diameter. The doped-quartz fibers are individually coupled to 15 m long fiber-optic patch cables, which are coated with an opaque, light-tight black Tefzel coating, and the active volumes are coated first with a low refractive index cleacladding, and then with an opaque black rubber material to ensure light-tightness. Figu5-1 shows the coated active areas of the dosimeters. Figure 5-1. Coated active areas of the FOC dosimeters.

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78 The fibers were then permanently epoxied into a casing which houses the optics and the image sensor used to image each of the 25 fibers, a thermoelectrically cooled Hammamatsub linear CCD (model S7031-1007 with a model C7041 detector head). This CCD consists of a 1044 x 128 pixel matrix (1024 x 122 active pixels), with a pixel dimension of 24 x 24 m. The spectral response range is 200-1100 nm (90% quantum efficiency at peak sensitivity), with a typical dark current of 200 e-/pixel/s at 0 C and a typical readout noise of 8 erms (along with a readout noise of 20 erms for the detector head). However, the dark current is an exponential function of temperature, therefore, at our typical operating temperature of approximately -10 C, the dark current is significantly less. This information, plus a much more detailed analysis of the S7031-1007 and C7041, can be found in the PDF object located below. Figure 5-2 shows two Object 5-1. PDF document containing the C7041 detector head, including detailed graphs of dark current vs. operating temperature (237 KB, CCD_specs.pdf) Custom LabView software was written to interface with the FOC dosimetry system via a National Instruments AT-AI-16XE-10 16-bit, 16 analog input data acquisition card. The software not only allows for the reading of the dosimeters, but also contains a calibration routine that is used for binning the output corresponding to each fiber image, as well as calculating calibration factors (and their corresponding errors) in mR/counts for each dosimeter. In addition, both the calibration and reading routines automatically correct the raw data by subtracting background contribution to the data. photographic views of the system, with the components of the system labeled. complete specifications of the S7031-1007 CCD image sensor and c b Hamamatsu Corporation, Bridgewater, NJ c National Instruments Corporation, 11500 N Mopac Expwy, Austin, TX 78759

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79 Figure 5-2. (A) Schematic of the design of the FOC dosimetry system. (B) Detailed view of the CCD detector head and other components. A Detector assembly. See Figure 5-2B for more detailed description. Fiber optic patch cables Power supply for TE cooler and CCD B Optics for projecting fiber images onto CCD sensor (covered by cloth to exclude light) CCD detector head/image sensor

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80 Sensitizing the FOC dosimeters to use, the FOC dosimeters were sensitized by irradiating them in our Co-6hile monitoring the radioluminescenc Prior0 irradiator we signal via the LabView software. The signat d. Calibration of the FOC dosimeters se after their initial sensitization was calibration. Any dosimeter that is used to make point dose measurements must be calibrated against an accurate instrument (such as a calibrated ionization chamber) to determine the linear transformation needed (provided the dosimeter has a linear response to radiation) to convert the output of a dosimetry system (counts, mV, V, etc.) into a value (e.g., mR) that can then be used to calculate dose. All calibrations for the FOC dosimetry system were performed using a clinical x-ray tube at the Orthopedic Institute at Shands Hospital. The measured HVL was 3.25 mm Al, and the voltage ripple was 3% at 100 kV. The ionization chamber used for the calibration was a 15 cc Keithly Model 96035B dual entrance window pancake ionization alibration was performed for each of the tube potentials that were to be examined as part of the study l increased with time as described in reference 59, reaching a saturation level thawas approximately 170% of the initial level after 5 hours of irradiation and an accumulated dose of approximately 1400 kGy. Figure 5-3 shows the raw voltage signal from the CCD at the beginning of the irradiation, and at the end of the irradiation, showing the corresponding increase in dosimeter sensitivity over the irradiation perioAfter sensitization, the response of the dosimeters is expected to remain constant over many kGy of ionizing radiation.59 The final step to prepare the FOC dosimeters for u chamber, along with a Keithley Model 35050A electrometer. A separate c

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81 described in Chapter 6 (80, 100,onization chamber and FOC dosim and 120 kVp). Both the i eters were irradiated atop 5 cm of acrylic to provide a scatter contribution. A B Figure 5-3. Graphical depiction of the effects of the sensitization of the FOC dosimeters. the CCD at the beginning of the sensitization period. (B) Raw data output from the CCD at the end of the sensitization period (elapsed time of 5 hours). Note the difference in the y-axis scaling between the two graphs. Five calibration points were acquired at each energy, using a tube current-time product of 250 mAs at a source-to-detector distance of 60 cm each time. The ionization chamber was irradiated before and after each set of five calibration points, with the two exposure values being identical each time. The FOC dosimeters were spaced tightly (A) Raw data output (volts/sec) from

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82 together atop the acrylic, and oriented perpendicular to the anode-cathode axis. The difference between exposure values between the leftmost and rightmost dosimeter was negligible (approximately 2%). Figures 5-4 and 5-5 illustrate the calibration setup. Figure 5-4. Illustration of the entire calibration setup. Figure 5-5. Close-up of the FOC dosimeter alignment during calibration.

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83 Results and Discussion Figure 5-6 plots the response of the FOC dosimeter (in counts/mR) versus thepotential at which the dosimeter was irradiated. While this does not fully characteexact energy dependence (a monoenergetic source of radiation would be needed to do tube rize the this), it does provide the necessary data for assessing the dosimeters performance in diagn ostic x-ray beams. It can be seen from the graph that the dosimeter does display a positive energy dependence, however, in the range of tube potentials that we are interested in for pediatric computed tomography measurements (80-120 kVp), the dependence is relatively small, and calibrating at the energies used for dose measurements should minimize problems associated with the energy dependence. Tube Potential (kVp) 30405060708090100110120130 550600650700 4004500 50 Figure 5-6. Tube potential dependence of the FOC dosimeter. Figure 5-7 illustrates the dose linearity of the FOC dosimeter. It is a simple graph, plotting the raw response (counts) of the FOC dosimeter versus the tube current-time product at which the irradiations occurred. A linear fit was applied to the data points and the square of the correlation coefficient was found to be very close to one, proving that Counts/mR

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84 the FOC dosimeter response is exactly linear versus dose at the doses seen in diagnostic radiology. Tube current-time product (mAs) 050100150200 Counts 3e+54e+55e+56e+7e+5 5 01e+52e+5 Figure 5-7. Dose linearity of the FOC dosimeter. Figure 5-8 illustrates the response of the FOC dosimeters as a result of bending thfiber through various radii. e Bend Radius (cm) 05101520 Pernt of saight fber reonse cetrisp 75809095100 85 Figure 5-8. FOC dosimeter response versus fiber bend radius.

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85 It is apparent from this graph that there is a significant initial drop in response (approximately 10%) when th e fiber is bent through even a large radius, and only a small additie to the response of the dosimeter when irradiated in any direction perpeen er makes Conclusions h se t is a onal drop as the bend becomes tighter. Because it is virtually impossible to perform in-phantom (or even in-vivo) dose measurements without bending the fibers, it is recommended that the fibers be bent in a 20 cm radius when calibrating to introduce a similar effect, and, through the calibration, eliminate this effect. Due to the cylindrical symmetry of the active area of the FOC dosimeter, there is no angular dependenc ndicular to the fiber (normally). However, the response of the dosimeter whirradiated tip-on was 80% of the response when irradiated normally. This fact is not expected to produce any noticeable effects when the dosimeters are used in phantoms (scatter from all directions), and the geometry of the irradiation in a CT scannthis effect negligible because radiation is incident on the dosimeter from all angles equally, making the reduced response from tip-on irradiation inconsequential. In addition, phantoms can be designed to minimize these effects in projection radiography.89 The information presented in this chapter makes clear the fact that FOC dosimeters have potential for use for radiation dosimetry in diagnostic radiology. There are many advantages associated with FOC dosimeters (real-time output, small physical size, higsensitivity, reproducibility, excellent dose linearity, etc.) and few drawbacks (energy dependence and minimal angular dependence). And while there is evidence of a decreain response when the fiber optic patch cable is bent, the decrease is predictable and isimple matter to either account for the decrease by introducing a standard bend into all

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86 calibrations and measurements, or by using a correction factor to correct measubased on the bending of the fibers. Overall, FOC dosimeters have a very promising future in radiation dosimetry for diagnostic radiology, and as photon detecting technology advances, FOC dosimetry stands to enjoy all the benefits. rements

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CHAPTER 6 OPTIMIZATION OF DOSE WHILE MAINTAINING ADEQUATE IMAGE QUALITY IN PEDIATRIC COMPUTED TOMOGRAPHY IntroductPediatric patients in hospitals have benefited enormously from the many advanin medical care during the past few decades. As a result of these advances, pediatric patients are subjected to many diagnostic exams, and as technology continues to improvemany of these exams deliver higher and higher rof the most rapidly advancing technologies, and perhaps the technomost responsibfor the increase of favorable outcomes in premature infants and pediatric patients, is medical imaging. Computed tomography (CT), specifically, has undergone numerous transformations and upgrades, including helical scanning and the introduction of multiple-row detectors. This has led to a vast increase in the utilization of CT and thextension of CT to new types of clinical diagnoses,1,2,100 including the acute pediatric appendix,101-104 and the ability to avoid sedation of uncooperative patients through the useof helical CT.105-107 However, there is a trade-off involved in this situation.108 While provides unmatched diagnostic information, it is also inherently a high-dose ima ion ces adiation doses to pediatric patients. One logy le e CT ging moda lity. Both the National Academy of Sciences Biological Effects of Ionizing Radiations (BEIR) committee and the International Commission on Radiological Protection (ICRP) have published data that attest to the risks of low doses of ionizing radiation, and to the fact that risk increases substantially with decreasing age of the exposed person.10,13 It is also well known that the doses delivered in a typical helical CT 87

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88 scan are well within the range of doses that have the potential to cause significant in young populations.9,12,109 It has been reported that upwards of 40% of the total dose from medical imaging isa result of CT examinations,3,4 and in the U.S., due to the absence of limits on dose per scan, this number may approach 60%.5 Brenner reports that while CT examinations of patients less than 15 years of age account for only approximately 4% by number, they estimated to contribute approximately 20% of the total potential cancer mortality from CT examinations.12 There have been an enormous number of studies done regarding dose, image quality, or both in pediatri effects are c CT, encompassing all types of exams. Several studies have alreadtal he ical phantoms play a y found that a 30-40% reduction in dose is possible with comparable image quality, and perhaps even more dose reduction is possible with no loss of diagnostic information.6-8,110-115 Also, advances in technology,67,116 including automatic exposure control in CT,117-124 have the potential to greatly reduce the doses delivered to pediatric patients, and there has been some effort into tailoring CT techniques to pediatric/neonapatients.125-128 The problem of escalating pediatric CT doses brings another problem to lighttproblem of quantifying the doses delivered to pediatric patients as a result of CT exams. Computational phantoms have found great use in CT dosimetry,91,93,129-134 particularly when combined with detailed data about CT scanners to form a complete simulation tool.95 However, computational phantoms alone have their shortcomings. As imaging technology continues to advance rapidly, accurately and completely simulating the imaging process becomes virtually impossible. This is where phys

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89 key ro se index (CTDI) (or CTDI scaled using dose conversion coefficients135,136) is used using ms created for al ch in create t le in the dosimetry process. While anthropomorphic physical phantoms representing pediatric patients do exist,30,31,33,34 the majority of pediatric CT dosimetry is performed with cylindrical acrylic or water phantoms, and some form of the computedtomography do d to report dose.17,18,64,65,114,137-140 However, select studies have been performepoint dose measurements and dose conversion coefficients in anthropomorphic phantoms.35 The balance of the work on dose reduction simply reports the CTDI values indicated on the CT scanner console, which are unreliable predictors of effective and average organ doses delivered to patients. However, two tomographic physical phantodo exist, a newborn phantom (see Chapter 3) and a nine-month old phantom, bothat the University of Florida from tissue-equivalent materials developed specifically the purpose of constructing physical phantoms for diagnostic dosimetry.27 However, just as computational phantoms have their shortcomings, so do physicalphantoms; namely, the inability to accurately measure average organ doses. All physicphantoms are designed to measure point doses in lieu of average organ doses, whireality is the quantity we are interested in measuring. Due to this shortcoming, it is very difficult, if not impossible, to quantify organ doses using physical phantoms alone,82,90 especially in large organs (e.g., liver), organs that are widely distributed (e.g., skin), and walled organs (e.g., stomach, colon). It is for this reason that the UF newborn computational91 and physical phantoms are a powerful tool when used together topoint-to-organ dose scaling factors (SFPOD, see Chapter 4) that allow for the measuremenand calculation of average organ doses using a tomographic physical phantom.

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90 Even with a method for quantifying the doses to pediatric patients, the issue ofimage quality still remains. Reducing the radiation dose delivered to a patient durinsingle exam is of no consequence if the exam must be repeated or a diagnosis is missed due to inadequate image quality. Image quality assessment in CT is still in its infancy. Many image quality evaluations are qualitative,6,113,123,141-143 e.g., human readers scoring patient images on a scale of one to five. Quantitative image quality assessments are generally limited to calculating image noise as the standard deviation of pixel valuan image of a uniform phantom,118,123,143,144 however, some studies have used the contrast-to-noise ratio (CNR) to evaluate image quality,114,139 while others have used images ocustom or existing image quality phantoms for scoring by readers.126,138,140 In addsome effort has been made tow g a es in f ition, ards making image noise alone a better descriptor of image low-ing the structure of the background noise, and how that structure relates to and interacts of targets of interest. on the aforementioned problems with current methods of image quality assessROC) study. The basic premise of an ROC study is as follows: an observer (one of many) is quality.145 In some clinical applications, image noise, or the standard deviation ofpixel values in an image of a uniform phantom, has become synonymous with imagequality in CT. As mentioned previously, image quality assessments are frequently limited to calculating the standard deviation of pixel values in a uniform phantom region.However, this method fails to take into account several other factors that determinecontrast detectability, includ with the size and contrast Based ment in CT, it is desirable to use a more reliable quantitative method for image quality assessment in this study. Perhaps the most widely used (and widely accepted) quantitative measure of image quality is the receiver-operating characteristic (

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91 presented with a series of images that m ay or may not have a signal present. The obserle n e of is the two-alternative forced-choice (2-AFC) methos of t nown signal on the noisy background. An obserich of the er of images if using a 2-AFC technique versus an ROC technique.75 ver is then asked to rank his confidence about the presence of a signal on a scasuch as definitely not present, probably not present, not sure, probably present,or definitely present. Using this data, values for the observers sensitivity (true positive fraction, or TPF) and (1-specificity) (false positive fraction, or FPF) can be calculated at various decision thresholds. This, in essence, yields values for the performance of an imaging system (or alternatively, an observers performance) at several different decisiothresholds, which can then be plotted. The area under the ROC curve, Az, can then be used to quantify the performance of an imaging system, or to compare the performancseveral imaging systems. A close relative of the ROC study d. The meaning of the area under the ROC curve, Az, is actually given in termthe results of a 2-AFC technique.75 It can be shown that the expected fraction of correcdecisions in the 2-AFC experiment is equal to the expected area under the ROC curve that would be measured with the same images viewed one at a time in a conventional ROC experiment.75-78 The 2-AFC experiment utilizes pairs of images, one containing only noise and the other containing some k ver is then presented with various pairs of images, and asked to specify whtwo images contains the signal. Then, Az can be estimated by simply calculating thefraction of the pairs of images where the signal was identified correctly. However, the ROC technique has a distinct advantage over the 2-AFC technique. It can be shown that, in order to obtain a similar level of confidence, one would need almost twice the numb

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92 One final observer study is also related to the 2-AFC experiment. The M-alternative forced-choice (M-AFC) technique is a more general extension of the experiment. Aufrichtig provides an excellent demonstration of the use of an M-AFC experiment.80 A recent development in diagnostic radiology is the use of computationaobservers. Among the most popular computational observers is the channelized Hotelling observer (CHO), which seeks to predict and mimic human visual performance. Sim 2-AFC l ilar to a neural network, these observers are trained by giving them information about logists t of thresholding in orderining a in order to area in a reasonable amount of time.69 the signal for which they are searching. The Hotelling observer is also general enough to include all sources of randomness, including background and noise.68 However, the use of Hotelling observers has been confined to ideal situations (i.e., artificially generated images) for the most part, and is frequently examined for use in SPECT and PET imaging. Computer aided-diagnosis is also beginning to be examinedfor use in diagnostic radiology, mainly in mammography and CT (specifically in lung imaging). Various studies have examined computerized schemes,69 model-based detection,70 or computer-aided diagnosis (CAD)71-74 as possible surrogates for radioor to supplement radiologists. Many of these techniques use some sor to narrow the number of possibilities to a feasible range. However, the traof the computers used in these studies can require enormous amounts of datreach reasonable detection rates,70 and some of the techniques are meant only to supplement or aid radiologists73 or require a radiologist to identify a region containing a possible abnormality in order to narrow the ROI to allow for the computer to search the

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93 Hybrid methods for assessing image quality provide some of the advantages of both computational observers and human observers, while eliminating some of the drawbacks associated with e ach. While computational observers are ideal for computer-aidedhuman e quality ber of images to be read, and the associated time comm ine a threshold contrast-to-noise ratio (CNR) that couldction with a fiber-opticdiagnosis, they are not ideal for image quality studies, because ultimately a observer will be making decisions regarding clinical images. Along similar lines, it isoften not feasible to use human observers for research studies involving imag assessment due to the large num itment required from the professional staff. In addition, the strain on a clinical CTtube from performing hundreds of scans for a 2-AFC or M-AFC study must also be considered. This is where the advantages of a hybrid method for image quality assessment become apparent. The best, and perhaps only, example of an existing hybrid method is that used by Pitcher to examine image quality in pediatric computed radiography.81 This method utilized human observers to determ be detected in a phantom image, then applied this threshold in a software program that was used to automatically score contrast-detail phantom images based on the calculated threshold CNR. Materials and Methods Dosimetry A tomographic newborn physical phantom was used in conjun coupled dosimetry system to acquire the data used in the dosimetry portion of thiswork. The two components are described in detail in the following sections.

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94 UF Newborn tomographic phantom The dosimetry for this study was performed using the UF Newborn physical phantom, constructed by the author at the University of Florida (see Chapter 3). This phantom was constructed using tissue-equivalent materials developed at the University of Florida, and was constructed at a 5 mm slice thickness from contour data91 derived from a full-body helical CT scan of a 6-day-old female cadaver. The UF Newborn phantom has channels machined to accept various types of dosimeters, including, but not limited to, thermoluminescent dosimeters, MOSFET dosimeters, diodes, and fiber-optic coupled dosimeters. More information regarding the phantom, as well as photographs of the phantom, can be found in Chapter 3. is plasma The system used in this work was constructed by Alan Huston and Paul Falkenstein in the Optical Sciences Division of the Naval Research Laboratory.a It consists of 25 dosimeters, each with an active length of approximately 4 mm, with each fiber having a 400 m core diameter. The doped-quartz fibers are individually coupled to 15 m long fiber-optic patch cables, which are coated with an opaque, light-tight black Fiber optic-coupled dosimetry system Fiber-optic-coupled (FOC) radiation dosimeters are based on the detection of phosphorescence from a Cu1+-doped quartz fiber.59,62 This charge-trapping material is fabricated by doping fused-quartz glass with Cu1+ ions.60,61,63 The doped quartz preformdrawn into a fiber, and a short length is attached to a multimode optical fiber via a fusion splicer. A more detailed description of the doped quartz and the fiber constructionprocess can be found in references 59-63. a U.S. Naval Research Laboratory, Optical Physics Branch, Code 5611 4555 Overlook Avenue, SW, Washington, D.C. 20375

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95 Tefzel coating, and the active volumes are coated first with a low refractive index clear polymer cladding, and then with an opaque black rubber material to ensure light-tightness. The fibers were then permanently epoxied into a casing which houses the optics, including an electronically cooled Hammamatsu linear CCD. Figure 6-3 shows the coated active areas of the dosimeters, and Figure 6-4 is a schematic of the construction of the system. More details on the performance of FOC dosimeters in diagnostic radiology and the construction of the 25 fiber system can be found in Chapter 5. Custom LabViewb software was written to interface with the FOC dosimetry system (specifically the CCD image sensor) via a National Instrumentsb AT-AI-16XE-10 16-bit, 16 analog input data acquisition card. The software not only allows for the reading of the dosimeters, but also contains a calibration routine that is used for binning data by subtracting background contribution to the data. More information on the LabView software pendix E. Sensitizing the FOC dosimeters. Prior use, the Fsensitizeiating themour Co-60 irraator while moniradioluce signal via the LabView software. The sig describerence 59, reag a saturationvel that wahe inours of iration and an aumulated do the output corresponding to each fiber image, as well as calculating calibration factors (and their corresponding errors) in mR/counts for each dosimeter. In addition, both the calibration and reading routines automatically correct the raw designed to acquire data from the CCD can be found in Ap to OC dosimeters were d by irrad in di toring the minescen nal increased with time as ed in ref chin le s approximately 170% of t itial level after 5 h radi cc se of approximately 1400 b Nationents Corporation, N Mopac Exy, Austin, al Instrum 11500 pw TX 78759

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96 kGy. Graphical data from the sensitization produre can sensitization, the response of the dosimeters is pected tGy oe phantom atom 16 Hospital. The phantom was positioned on the table (at isocenter) for both head and body measurements, and raised 5.5 cm above the table using polyethylene foam blocks (this was done to avoid severe bending of the rformed for this study were helical scans. As can be seen from the illustrations, the arms were left in their natural position for head exams, and were removed to simulate the arms being raised above the head for the ody exams. The head exams were each performed in one scan, as the number of dosimeters available (25) exceeded the number of point dose measurement locations, which are listed (for the head) in Table 6-1. The head exam coverage began at the top of the head and ended just below the chin, as illustrated in Figure 6-3. The body exams, however, were performed in sections. Due to the small size of our neonate patient, individual chest, abdomen, or pelvis exams were not performed, ce be found in Chapter 5. After ex o remain constant over many k f ionizing radiation.59 Calibrating the FOC dosimeters. Prior to use, the FOC dosimeters were calibrated using a standard x-ray tube at the Orthopedic Institute at Shands Hospital. Details of this procedure, including photographs, can be found in Chapter 5. CT scanning of th All dose measurements were performed using a Siemensc Sensation SomCT scanner at the Orthopedic Institute at Shands dosimeters). Photographs of the general setup can be seen in Figure 6-1, and photographs of the individual head and body setups can be seen in Figure 6-2. All esams pe b ions Inc., 51 V c Siemens Medical SolutUSA, alley Stream Parkway, Malvern, PA 19355

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97 instead, a CAP exam (chest-abdomen-pelvis) was performed. H owever, individual organ doses are not expected to differ between individual exams a nd a full CAP exam. Figure 6-1. Illustrations of the general scanning setup. As mr the purpose of this study, only CAP exams were performed on the ewborn phantom. The CAP exams had to be divided into two parts due to the large r a few slices below the femoral heads. entioned, fo n numbe of point dose measurement locations in these regions of the body. They were split into two sections, one covering from just below the chin to the middle of the abdomen, with the beginning of the next section overlapping with the ending of the first section, and proceeding through to the legs, ending

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98 A B Figure 6-2. Photographs of the scanning setup for (A) head exams and (B) body exams. The point dose measurement locations for the CAP exams are listed in Table 6-2, while Figure 6-4 illustrates the aforemned scanning lengths. entio

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99 Table 6-1. Dose measurement locations for head exams. ter Site Slice Number No. of Dosimeters Notes in 14 1 Placed in the center of the DosimeSk x-ray field Skull (Rig BraPosterior and anterior 11 2 Centered in the lateral direction 15 0 Skin dosimeter used as surrogate Maxilla (Right + Left) 17 2 31 1 Located 1/3 of the way through the lung 31 1 Located 1/3 of the way through the lung Ribs 33 2 One dosimeter in each side of the rib cage ht + Left) 5 2 Centered in the antero-posterior directionin 10 2 Skull (Post. + Ant.) L eye/lens R eye/lens 15 0 Skin dosimeter used as surrogateCervical vertebra (C4) 24 1 Positioned in the vertebral body Thyroid 24 1 Centered laterally Thymus 30 1 Left lung Right lung Esophagus 32 1 Positioned in the esophageal wall As can be seen from Tables 6-1 and 6-2, some organs that are involved in effective dose calculations in both the head and CAP exams were omitted from the respective tables. This is because point doses in organs that were far removed from the scanning length were assumed to be zero, based on previous experience (see Chapter 4). Dose calculation Point estimates of tissue absorbed dose can be inferred from dosimeter measurements using the following relationship: entissuetissue-dosimeterexposure-to-kermaenairDmCFk (6-1), where m is the dosimeter response, CF is the energy-dependent calibration factor, k is the conversion factor from exposure to air kerma (mGy per C kg-1), and en air a nd en tissue are the mass energy-absorption coefficients for air and soft tissue, respectively, weighted over the x-ray energy spectrum incident upon the phantom.

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100 Figure 6-3. Approximate scanning coverage of the head exams. Following the calculation of point absorbed doses, the CT Organ Dose Calculatodeveloped in conjunction with Robert Staton95 was used to calculate (via point-tdose scaling factors, SFPOD) the average organ doses delivered to the physical phantomFinally, the average organ doses were also used to calculate the ef r o-organ fective doses delivered to the phantom as a result of the CT exams. Effective dose can be calculated as D (6-2), TTTEw

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101 Table 6-2. Point dose measurement locations for CAP exams. Dosimeter Site Slice Number No. of Dosimeters Notes Skin Varies with field 1 Placed in the center of the x-ray field Cervical vertebra (C4) 24 1 Positioned in the vertebral body Thyroid 24 1 Centered laterally Thymus 30 1 Left lung 31 1 Located 1/3 of the way through the lung Right lung 31 1 Located 1/3 of the way through the lung Esophagus 32 1 Positioned in the esophageal wall Ribs 33 2 One dosimeter in each side of the rib cage Heart 34 1 Left lung 35 1 Located 2/3 of the way through the lung Right lung 35 1 Located 2/3 of the way through the lung Thoracic vertebra (T6) 37 1 Positioned in the vertebral body Spleen 41 1 Stomach 41 1 Liver 42 2 Large organ required two dosimeters Gallbladder 42 0 Mean of liver dosimeters used as surrogate Pancreas 43 1 Gut 44 1 Small intestine location Left kidney 46 1 Also describes left adrenal gland Right kidney 48 1 Also describes right adrenal gland Gut 49 1 Large intestine location Gut 53 1 Large intestine location Lumbar vertebra (L3) 55 1 Positioned in the vertebral body Illium (Right + Left) 57 2 Ovary (Right + Left) 59 1 One dosimeter located between the ovaries Bladder 60 1 Rectum/sigmoid colon 61 1 Uterus 62 1 Femoral heads (Right+Left ) 64 2 Located in the proximal femur heads whereis the tissue weighting factor for tissue T andis the dose to tissue T (this form of the expression assumes a radiation weighting factor of 1). Tw TD 10 Table 6-3 shows a list of the currently used tissue weighting factors, and Object 6-1 contains the CT Organ Dose Calculator developed at the University of Florida.

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102 A B Section 1 and (B) Section 2. Table 6-3. Tissue weighting factors from ICRP 6010 Figure 6-4. Approximate scanning coverage of the CAP exams, broken down into (A) Tissue or organ Tissue weighting factor, wT Gonads 0.20 Bone marrow (active) 0.12 CLung 0.12 olon 0.12 Esophagus 0.05 Skin 0.01 Remainder 0.05 Stomach 0.12 Bladder 0.05 Breast 0.05 Liver 0.05 Thyroid 0.05 Bone surface 0.01

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103 Object 6-1. CT Organ Dose Calculator developed by Robert Staton and Aaron Kyle the type of exam, the tube potential used, and values for point dose (57 KB, CT_Organ_Dose_Calculator.xls). Image Quality Analysis The Catphand CTP515 module was used in conjunction with a custom written image evaluation tool developed at the University of Florida for image quality assessment in this study. The Jones at the University of Florida. This spreadsheet allows the user to input measurements, and returns values for average organ doses and effective dose se two components are described in detail in the following sections. Catp was its ts ts are f the phantom module, whileargets st level of han CTP515 module The Catphan CTP515 module (removed from its Catphan housing) was chosen as the phantom for use in image quality assessment as part of this work. This phantomchosen for several reasons, including its widespread acceptance and use, the nature oflow contrast objects, and its diameter of 15 cm, making it an acceptable surrogate for a pediatric body, similar to the 16 cm AAPM adult head phantom often used to represent a pediatric body. The CTP515 module contains two distinct types of low contrast objecthat make it ideal for use in objective image quality studies. The outer row of objecsupraslice objects, extending through the entire thickness o the inner row of objects are subslice objects of varying lengths. The outer tare 40 mm in length, range in diameter from 2 mm to 15 mm, and include three contrast levels of 1.0%, 0.5%, and 0.3%. The inner targets range have a nominal contra1.0%, range in diameter from 3 mm to 9 mm, and include lengths of 3 mm, 5 mm, and 7 mm. It is these subslice targets in particular that make the CTP515 module so appealing for an image quality study. The subslice objects provide an excellent target d The Phantom Laboratory, P.O. Box 511, Salem, NY 12865

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104 for the evaluation of the effects of pitch on image quality. Objects that span the entire thickness of a phantom are of little use when evaluating the contributions of pitch to image quality, especially at small collimated beam thicknesses. Phantom image scoring software Custom software was written in Matlabe to score phantom images based on threshold contrast-to-noise (CNR) ratios determined from radiologists evaluation of phantom images, which will be described in detail in the following section. The initial concept of the software was implemented by Chris Pitcher in his work with computed radiography (CR) images.81 The basic flow of data through the software is as follows. ere acquired in one session after position ent), and no rotation, translation, or other manipulation of the m A DICOM phantom image is loaded into the program, and the user is prompted to identify the largest (15 mm) 1.0% contrast object in the phantom image by clicking on it. The software then displays an ROI containing only the 15 mm 1.0% contrast object, and prompts the user to click on the center of the object. It has been demonstrated by Pitcher that this type of manual registration routine yields reproducible results that are not dependent upon the user input (i.e. no intra-user variability).81 Because all phantomimages that were to be scored by the computational observer w ing the CTP515 module, it was only necessary to manually identify one object, as the positions of the other objects relative to the identified anchor object were known (a scale drawing was provided by the manufacturer, The Phantom Laboratory, under a non-disclosure agreem odule was introduced during the scanning. e The Mathworks, 3 Apple Hill Drive, Natick, MA 01760

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105 Once the center of the anchor object is identified, the software then calculatpositions of the centers of all other objects in the phantom module, based on the knowand y offsets of each object from the anchor, derived from a scale drawing of the phantom, corrected for minor errors in object positions within the phantom. Calculating the CNR of low contrast objects requires three pieces of inform es the n x ation: BackgroundSignalBackgroundCNR (6-3). Signal is the average pixel value in the object of interest, background is the average pixel urrounding the object of interest, an value in the background (uniform phantom material) s d ackground B is th e stae pixel values in the background, i.e. a measure of the noise level in thge. Theref CNR of a ontrast object, several regions of interest (ROIs) are created for the object. First, a square ROI (ROISignal) whose size is based on the size of the object containing it is created within each low contrast object (due to slight errors in center positions, automatically selecting the entire object would introduce errors due to some background being included with the object ROI). Next, two identically-sized rectangular ROIs (ROIBackground1, ROIBackground2) are selected automatically from the background (uniform phantom material) surrounding each object. The signal value is given by the mean pixel value of ROISignal, the background value is given by the mean pixel value of the two background ROIs, and is given by the standard deviation of the pixel values within the two background ROIs. After CNRs for all low contrast objects have been calculated, the threshold CNR is used to determine whether each object is visible to the computational observer. An object is considered visible if it meets the following criterion: ndard deviation of th e ima ore, to calculate the low c

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106 LC ObjectThresholdCNRCNR (6-4). A report is then generated and written to disk, containing data including the total number of visible objects, total number of supraslice objects visible, and total number of subslice objects visible. Threshold contrast-to-noise ratio determination Image acquisition. The determination of the threshold CNR for the computational observer was carried out using radiologists scoring of images of the same phantom (Catphan module CTP515). Images of the phantom module were acquired on the same Siemens Sensation Somatom 16 CT scanner that was used for dose measurement at 100 kV, using default pitch, collimated beam thickness, reconstructed slice thickness, and gantry rotation time values. Tube current-time product values ranged from 30 to 170 mAs, with no tube current modulation. Images were acquired and reconstructed using both head and body filters/kernels (with generic head or body protocols) for the purpose of calculating threshold CNRs for images of the two different regions of the body. Also, e phantom module was rotated through 45 degree increments after each image was acquired to prevent the radiologists from developing any bias or seeing any ghost objects resulting from viewing the phantom in the same orientation in many different images. Radiologist image scoring. All image scoring was performed at a clinical reading workstation in a reading room in the Radiology Department at Shands Hospital. Fifteen radiologists, ranging in experience from first year residents to the Chief of Pediatric Radiology, were recruited for phantom image scoring. Each reader was given both a scoring sheet and an instruction sheet, which contained a schematic diagram of the th

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107 phantom, a brief description of how the images were acquired, a brief description of the detection task, and detailed instructions on how to score the images, including an example score sheet. These documents, along with the radiologists final tallied scores, can be found in Appendix F. After reading the instructions, each reader was given the opportunity to ask any questions he might have regarding the process. All scoring sheets and instruction sheets were identical, and the reading conditions were reproduced exactly for each reader. The previously acquired phantom images were ordered randomly and presented one by one to the readers. The images were displayed using a baby brain (75/30 W/L) and a allowed to m contrast objects in the outer row of the CTP515 module in each image, and then the next image was presented. This process was repeated until all images had been scored, with a total of fifteen images being scored. Each of the fifteen readers was led through this same process. One reader was randomly selected to rescore the fifteen images in a different random order as a check of reader reproducibility. Threshold CNR calculation. Following the completion of the radiologists reading of the phantom images, the data from each reader was compiled into one Excel spreadsheet for analysis (one for head filter/reconstruction, the other for body filter/reconstruction). The data was sorted by both object groups and the mAs values at uired (i.e. exposure level) in order to identify a suitable object baby abdomen (200/40 W/L) window and level setting, which the readers were not anipulate. The reader recorded a score for each of the three sets of low which the image was acq for calculation of the threshold CNR. The goal was to identify the object that was most agreed upon by the readers as the last visible object in its group at a certain mAs value.

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108 Therefore, the mean alone was rejected as a method for identifying the aforementioneobject. However, simply using another descriptor such as the mode or median alone would also d not identify the appropriate object at a single mAs value. It became apparent that th e goal should be to evaluate the skewness and spread of the data sets, and thus use these characteristics to select the appropriate data set (minimize skewness) from which tocalculate the threshold object. The Pearson mode skewness is a simple measure of the skewness (i.e., similarity of mean and mode) of a data set and is defined as M eanModeSkewness (6-5). It can be seen from this expression that the goal is to choose the data set that possesses the mostandard deviation of the chosen data set, as a large standard deviation, or spread, in the data can lead to an artificially small (i.e. not due to similar mean and mode) skewness value. However, this being said, with small data sets such as ours (15 points corresponding to 15 readers), a data set with a similar mean and mode also, by The aforementioned process was carried out to establish two threshold CNR values, one for images acquired and reconstructed with head filters and kernels, and one for images acquired and reconstructed with body filters and kernels. Protocol Selection The purpose of this study was not to simply evaluate CT protocols used clinically, but to go further and investigate the effects of all components of a CT protocol on dose and image quality. These components include kVp, tube current, gantry cycle time, tube current-time product (mAs), pitch, and collimated beam width. The effect of various st similar (or equal) mean and mode. However, it is also important to examine the implication, has a minimal standard deviation associated with it.

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109 reconired ime productdose measurements were performed only at 150 mAs, and the results were scaled to derive values for 100 and 50 mAs. Table 6-4. Protocol element selection for evaluation. Tube potential (kVp) Tube current-time product (mAs) Collimated beam width (mm) Pitch structed slice thicknesses on image quality were not examined, as additional reconstructions can be done at will, provided the data was acquired at an appropriate collimated beam width and pitch. The values of each parameter and their combinations that were selected for evaluation are listed in Table 6-4. Three data points were acqufor image quality evaluation and four data points for dose measurement for each combination of values listed in Table 6-4. The exception was that of tube current-t 80 50 12 mm (16 x 0.75) 0.75 100 100 24 mm (16 x 1.5) 1.0 120 150 -1.25 Results Due to the sheer amount of data created during this study, only a few representative graphs and tables will be displayed in the text. The full contents of each data set (image quality and dose) will be available via the object references at the end of this section. Figure 6-5 displays effective dose plots at 80 kV for both head and CAP protocols, while Figuct score (TOS) and subslice object score (SOS) in the phantom images acquired using the same protocols. One will notice that for head protocols at 80 kV, a collimated beam width of 12 mm (16 x 0.75) yields slightly lower doses than does a collimated beam width of 24 mm (16 x 1.5) across the entire range of tube current-time product and pitch values. This trend exists at a tube potential of 100 kV, while the more intuitive result is seen at 120 re 6-6 displays the plots corresponding to object scores (total obje

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110 kVa collimated beam width of 12 mm leads to higher doses at pitches less than 1.0, while leading to lower doses at pitches greater than or equal to 1.0. Also of interest is the fact that the opposite is true for CAP protocolsa collimated beam width of 24 mm consistently yields lower effective dose values than does a beam width of 12 mm, and, this trend persists throughout the entire tube potential range. 00. 50.751.01.250.751.01.250.751.01.25 1 1.522.5 50 mAs 100 mAs150 mAs Ee Dose (mSv) 16 x 0.75 16 x 1.5 ffectiv 00.511.522.533.544.555.566.570.751.01.250.751.01.250.751.01.25 50 mAs 100 mAs150 mAs Effective Dose (mSv) 16 x 0.75 16 x 1.5 B Figure 6-5. Effective doses corresponding to (A) Head 80 kV and (B) CAP 80 kV. A

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111 A glance at the corresponding object score graphs in Figure 6-6 reveals more interesting information. Neither collimated beam width value yields a definitive advantage in TOS for head protocols. A collimated beam width of 12 mm and a pitch of 0.75 yields superior SOS, as would be expected of a smaller collimated beam width combined with a 00.751.01.250.751.01.250.751.01.25 5101520253035404550 50 mAs 100 mAs 150 mAs Total Object Score 16 x 0.75 16 x 1.5 02468 1012140.751.01.250.751.01.250.751.01.25 50 mAs 100 mAs150 mAs Subse Obje Score licct 16 x 0.75 16 x 1.5 Figure 6-6. Object scores corresponding to (A) Head 80 kV, TOS, (B) Head 80 kV, SOS, A B (C) CAP 80 kV, TOS, (D) CAP 80 kV, SOS.

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112 05 25303540 1051.01.250.751.01.250.751.01.25 15200.7 50 mAs 100 mAs150 mAs Toe tal Objct Score 16 x 0.75 16 x 1.5 012 9 31.01.250.751.01.250.751.01.25 456780.75 slice Object Score 16 x 0.75 16 x 1.5D Sub 50 mAs 100 mAs150 mAs Figure 6-6. Continued. pitch value less than one. This is reversed for pitch values greater than or equal to one. No differe seen using eitheed beam width at 100 kV, while at 120 kV, a 12 mm c beam width yirior TOS and SOS, especially at low tube current-tucts. Howeverly is a tube potential this high used in pediatric imagingC nce is r collimat ollimated elds supe ime prod very rare

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113 As for the object scores corresponding to CAP protocols, it is apparent from Figure 6-6 parts C and D that TOS are generally slightly higher for a collimated beam width of 24 mm than for a width of 12 mm at 80 kV. Exceptions include pitch values greater than one, where a collimated beam width of 12 mm yields better TOS and SOS at 50 and 100 mAs. TOS are generally equal or a collimam width of 24 mm throughe potential raned, while SOS are generally inferior, esurrent-timt values. Figure 6-7 shows the sameat was shown in Figure 6-6, but arranged differeesentation pect score differences associated with pitch to be ore easily identified, while the style presented in Figure 6-6 more readily highlights SOS scores, with the exception occurring again at 50 mAs with a collimated beam width of 24 mm, were a pitch of OSP exams, TOS are again equivalent for pitch values less than or equal to one, and are markedly inferior at a pitch ofr for a 5 at higalues, while a pitch of 1.0 provides better SOS at low mAs Figpresents both ob (i.e., imlity) and effective dose data in the samer CAP exams at 80 kV and 120 kV in this case) This alternate way of displayination reveals in that might otherwise be lost in trying to look r better fo ated be out the tub ge examin pecially at high tube c e produc information th ntly. This pr rmits obje m differences associated with collimated beam width more easily. It can be seen from Figure 6-7 that TOS are generally equivalent across pitch values, with a pitch of 0.75 yielding slightly higher scores with a collimated beam width of 24 mm at high mAs values. It is also interesting to note that a pitch of 1.0 yields superior TOS with a collimated beam width of 24 mm at 50 mAs. A pitch of 0.75 generally provides higher h 1.0 yields superior S For CA 1.25. Se superio OS ar p itch of 0.7 h mAs v values. ure 6-8 ject score age qua graph (fo g inform formation

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114 at separate object score and effecplots. It seen from Figure 6-8 that pitches 0, while alwayective doses, do not necessarily provide superior objeat any tubnt-time product value. tive dose can be less than 1. s delivering the highest eff ct scores e curre 05101520253035 4550 A 40 16 x 0.7516 x 1.516 x 0.7516 x 1.516 x 0.7516 x 1.5 50 mAs 100 mAs150 mAs Total Object Score 0.75 1 1.25 02468101214 16 x 0.7516 x 1.516 x 0.7516 x 1.516 x 0.7516 x 1.5 50 mAs 100 mAs150 mAs Subsce ObjScore liect 0.75 1 1.25 Head 80 kV, SOS, (C) CAP 80 kV, TOS, (D) CAP 80 kV, SOS. B Figure 6-7. Object scores (sorted by pitch) corresponding to (A) Head 80 kV, TOS, (B)

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115 3540 051520250 10316 x 0.7516 x 1.516 x 0.7516 x 1.516 x 0.7516 x 1.5 50 mAs 100 mAs150 mAs Tol Obje Score tact 0.75 1 1.25 0345789 12616 x 0.7516 x 1.516 x 0.7516 x 1.516 x 0.7516 x 1.5 50 mAs 100 mAs150 mAs Sulice Object Sco bsre 0.75 1 1.25 Figure 6-7. Continued. dose tabad and CAP ex. These taontain inform regarding boctive dose and vera of 24 C D Finaly, Tables 6-5 andow samples l 6-6 sh of organ les for he a ms, respectively bles c ation th effe a ge organ doses for each protocol examined. Table 6-5 corresponds to a head protocol at a tube potential of 120 kV, mAs value of 150, a collimated beam width

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116 0 1234 5 6 16 x 0.7516 x 1.516 x 0.7516 x 1.516 x 0.7516 x 1.5 50 mAs 100 mAs150 mAs Effecte Do (mSv101520253035Total Objectcore ivse)0540 S 0.75 1 1.25 A 0.75 TOS 1.0 TOS 1.25 TOS 02461416 8101216 x 0.7516 x 1.516 x 0.7516 x 1.516 x 0.7516 x 1.5 50 mAs 100 mAs150 mAs Efectise (mSv)01020405060Tal Obect Scoe fve Do30otjr 0.75 1 1.25 0.75 TOS 1.0 TOS 1.25 TOS 80 kV and (B) CAP 120 kV. B Figure 6-8. Object score and effective dose data presented on the same plot for (A) CAP

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117 mm (16 x 1.5), and a pitch of 1.0, while Table 6-6 corresponds to a CAP exam using thsame parameters. Table 6-5 demonstrates that brain, e and bone marrow/surface doses, alongaphs ll discussion of the discoveries of this study that follows in the ned by both protocol/tube potential (e.g., Head 80 kV, CAP 80 kV, etc.) and potential data page is preceded by two plots, one with data arranged as in As page is preceded by only one plot, with the data arranged as in Figure 6-6 (217 by both protocol/tube potential (e.g., Head 80 kV, CAP 80 kV, etc.) and potential data page is preceded by six plots, three (total object score (TOS), arranged as in Figure 6-5, the other three with data arranged as in Figure 6-6. the data arranged as in Figure 6-6 (255 KB, Autoscore_Scores.xls). with doses to the thyroid and thymus, drive the effective dose values in head exams. Effective doses in CAP exams are most influenced by the dose to the ovaries. While the dose to the ovaries is not significantly higher than the doses to other organs during the CAP exam, the high tissue weighting factor associated with the ovaries leads to somewhat artificially high values for effective dose in CAP exams. As mentioned previously, the large amount of data generated in a study such as this, in addition to the poor display quality of these graphs in the text, preclude the inclusion of the majority of the graphs and tables in the text. However, all of the grand tables generated as a result of this study are included in the Objects 6-2 through 6-10. Objects 6-5 through 6-10 contain organ dose tables formatted as in Table 6-5. It is suggested that the reader open the necessary objects for review while reading the text, especially when reading the fu xt section. Object 6-2. Effective dose plots generated as a result of this study. Results are organizeprotocol/mAs (e.g., Head 50 mAs, CAP 50 mAs, etc.). Each protocol/tube Figure 6-5, the other with data arranged as in Figure 6-6. Each protocol/mKB, Effective_Dose_Plots.xls). Object 6-3. Object score plots generated as a result of this study. Results are organized protocol/mAs (e.g., Head 50 mAs, CAP 50 mAs, etc.). Each protocol/tube subslice object score (SOS), and supraslice object score (SpOS)) with data Each protocol/mAs page is preceded by three plots (TOS, SOS, SpOS), with

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118 Object 6-4. Effective dose plots with object scores (as in Figure 6-8) generated as a (e.g., Head 80 kV, CAP 80 kV, etc.) and protocol/mAs (e.g., Head 50 mAs, is preceded by one plot, with data arranged as in Figure 6-8 (229 KB, result of this study. Results are organized by both protocol/tube potential CAP 50 mAs, etc.). Each protocol/tube potential and protocol/mAs data page Effective_Dose_Plots_with_Image_Quality_Data.xls). Table 6-5. Sample organ dose table for head exams. Effective Dose (mSv) % Stdev 3.74E+00 2.40% Selected Organs/Tissues Dose (mGy) % Stdev Brain 1.97E+01 14.89% Left Eye 1.36E+01 12.74% Right Eye 1.36E+01 12.74% Thyroid 1.41E+01 6.62% Thymus 1.77E+01 4.73% Right Lung 7.31E+00 4.69% Left Lung 6.83E+00 17.12% Esophagus 9.18E+00 4.43% Spleen 0.00E+00 0.00% Liver 0.00E+00 0.00% Gallbladder 0.00E+00 0.00% Right Kidney 0.00E+00 0.00% Large Intestine 0.00E+00 0.00% Left Ovary 0.00E+00 0.00% Stomach Wall 0.00E+00 0.00% Pancreas 0.00E+00 0.00% Left Kidney 0.00E+00 0.00% Small Intestine 0.00E+00 0.00% Right Ovary 0.00E+00 0.00% Bladder Wall 0.00E+00 0.00% U0.00E+00 0.00% Bone Ma.01E+01 2.77Bone Sur2.73RemE+00 3.49% Rectum/Sigmoid Colon 0.00E+00 0.00% terus Skin 3.08E+00 12.74% rrow 1 % face 2.35E+01 % ainder 4.88 bject 6-5. Organ dose tables for Head 80 kV (50 KB, Head_80_kV_Summary.xls). Object 6-6. Organ dose tables for Head 100 kV (52 KB, Head_100_kV_Summary.xls). Object 6-7. Organ dose tables for Head 120 kV (52 KB, Head_120_kV_Summary.xls). O Object 6-8. Organ dose tables for CAP 80 kV (58 KB, CAP_80_kV_Summary.xls).

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119 Object 6-9. Organ dose tables for CA P 100 kV (58 KB, CAP_100_kV_Summary.xls).Object 6-10. Organ dose tables for CAP 120 kV (58 KB, CAP_120_kV_Summary.xls). Table 6-6. Sample organ dose table for CAP exams. Effective Dose (mSv) % Stdev 9.82E+00 2.29% Selected Organs/Tissues Dose (mGy) % Stdev Brain 0.00E+00 0.00% Left Eye 0.00E+00 0.00% Thyroid 1.15E+01 10.02% Right Lung 1.30E+01 8.38% Esophagus 1.09E+01 10.06% Stomach Wall 1.06E+01 10.49% Pancreas 1.08E+01 8.99% Left Kidney 9.69E+00 12.71% Small Intestine 1.13E+01 10.07% Right Ovary 1.10E+01 5.95% Left Ovary 1.10E+01 5.95% Bladder Wall 1.21E+01 10.39% Rectum/Sigmoid Colon 1.21E+01 3.77% Right Eye 0.00E+00 0.00% Thymus 1.15E+01 26.92% Left Lung 1.21E+01 10.10% Spleen 9.68E+00 15.25% Liver 1.23E+01 2.70% Gallbladder 1.23E+01 3.82% Right Kidney 9.69E+00 12.71% Large Intestine 1.13E+01 10.07% Uterus 1.19E57% Skin 3.74E+0.40% Bone Surface 1.01E+01 2.18% +01 4.0 46 Bone Marrow 4.70E+00 1.96% Remainder 8.93E+00 2.99% Discussion Note : The discussion that follows with regards to collimated beam width in the Discussion and Conclusions sections apply omanufactured by Siemens, but may be exten nly to 16 slice CT scanners ded and extrapolated, as appropriate, to other manufacturers and detector configurations.

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120 First, a word on the calibration of any type of dosimeter for use in dose measurements in computed tomography. It is well-known that CT scanners typically utilize much more filtration to harden the x-ray beam than do conventional x-ray tubesorder to both reduce dose and accurately calculate attenuation (i.e. Hounsfield Units) for in the re 0 kV. However, large differences in simulation and experimental data, especially for CAP exams, prompted a closer look at the effects of large amounts of beam-hardening filtration on the calibration factors. C system and Keithley 15 izatiober) to calculate calibration factorthree tube pls with several different amounts of filtration in the beam, ay tubeation purposes. Five measurements were performed at four f5 mm Al t in Al. construction of images. Previous work with MOSFET dosimeters by the author haddemonstrated that differences in tube potential must be taken into account, while the effects of the addition of up to 5.5 mm Al filtration on calibration factors were somewhatambiguous. Therefore, as mentioned previously in Chapter 5, the FOC dosimeters used in this work were calibrated using a standard x-ray tube at the three tube potentials that were used as protocol elements, 100, and 12 An experiment was designed (using the single-fiber FO cc pancake ion n cham s at all otentia using the same x-r described in Chapter 5 for calibr iltration values, including no added filtration, 3 mm Al added filtration, 6.3added filtration, and 10.3 mm Al added filtration. Also, Monte Carlo simulations in MCNP586 were performed using the measured half-value layer (HVL) of 3.25 mm Al a80 kV and a raw (unfiltered) 80 kV spectrum generated using TASMIP87 (3% ripple) order to determine the total inherent filtration present in the x-ray tube used for calibration. The value thus calculated for the total inherent filtration was 1.725 mmA linear least-squares fit was performed on the data, generating an equation to determine

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121 the expected calibration factor (mR/counts) at a certain filtra tion level. The quotients of calibration factors with no added filtration and calibration factor s at filtration levels of 5.5 mm Al and 11 mm Al (head and body filters fo r CT, respectively) were used to correct the organ and effective dose data collected fo r this work. All data presented in this dissertation has been appr opriately corrected with these factors. A list of these factors is presented in Table 6-7. Table 6-7. Calibration fact or correction factors. 80 kV 100 kV 120 kV Head (5.5 mm) 0.93 0.92 0.92 Body (11 mm) 0.79 0.76 0.76 As can be seen from Table 6-7, the differe nce in tube potential matters very little with the FOC dosimeters, while the difference in filtration amounts makes a very significant difference, due to the increasing magnitude of the ener gy dependence of the dosimeter with decreasing photon en ergy, as discussed in Chapter 5. Generally, individual dose and image quality trends behave as e xpected. Increasing mAs results in increasing dose (in a linear fa shion), increasing kV results in increasing dose, and decreasing pitch resu lts in increasing dose (in a fairly linear relationship). Obvious image quality trends are fewer, and include increasing object scores with increasing mAs, increasing object scores with increasing kV, and in creasing object scores when images are reconstructed with a head kernel (possibly due to higher beam output). As mentioned previously, for head protocols at 80 kV, a collimated beam width of 24 mm (16 x 1.5) yields slightly higher doses than a collimated beam width of 12 mm (16 x 0.75) across the entire range of tube current-time product and pitch values, but this difference is minimal. This is a somewhat unexpected effect, as one would expect higher doses from a 12 mm beam width due to th e additional contribution from overbeaming.

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122 This trend persists throughout the tube potential range examined (80-120 kV). Also of interest is the fact that the opposite is true for CAP protocolsa collimated beam width of 24 mm consistently yields lower effective dose values than does a beam width of 12 mm (as expected), and, once again, this trend persists throughout the entire tube potential range, with these difference being somewhat greater than those seen between the two collimated beam widths in head protocols. A glance at the corresponding object score graphs reveals more interesting information. Neither collimated beam width value yields an advantage in TOS for head protocols, and no definitive winner can be identified between the two collimated beam widths for SOS. However, using a protocol of 80 kV, 50 mAs, and pitch of 1.0, a collimated beam width of 24 mm (16 x 1.5) yields superior low-contrast resolution when compared to a 12 mm beam width, and the scan will be completed faster given the same gantry cycle time. re exists between collimated beam widths of 24 mmt e s d results As for the object scores corresponding to CAP protocols, it is apparent from Figu 6-6 parts C and D that a significant difference and 12 mm at 80 kV, with a width of 24 mm yielding superior TOS throughouthe tube current-time product range, and yielding better SOS at low mAs values (with thexception of a pitch value of 1.25 for both TOS and SOS). Both collimated beam widthare fairly equivalent at higher tube potentials in terms of both TOS and SOS, with the exception of 100 kV, where a collimated beam width of 12 mm provides superior SOS at low mAs values. The superior low contrast detectability associated with a 24 mm collimated beam width could be a result of the larger contiguous sections of data acquireduring each tube rotation, requiring less processing during reconstruction. These

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123 suggest the use of a 24 mm (16 x 0.75) collim ated beam width for both head and CAP exam calculated from wo-rences, an increase from 80 to 100 kV resulted in a dose increase by a f exams. s. than ws r s of pediatric patients. Another general principle that can be gleaned from these results is the dose relationship of kV and mAs. Because dose values for 50 and 100 mAs were measurements at 150 mAs, the corresponding values are exactly one-third and tthirds of the 150 mAs value, respectively. However, this result was verified via test measurements. As mentioned, doses for the same protocols at 100 mAs and 150 mAs were two times and three times the value for the same protocol at 50 mAs, and the doses for the 150 mAs protocol were 1.5 times the doses of the 100 mAs protocol. However, for tube potential diffe actor of (approximately) 2 for head exams, and a factor of (approximately) 2.3 for CAP exams. An increase from 100 to 120 kV resulted in an increase by a factor of (approximately) 1.2 for head exams, and a factor of (approximately) 1.3 for CAPOverall, the increase from 80 kV to 120 kV resulted in a dose increase by a factor of (approximately) 2.4 for head exams, and a factor of (approximately) 2.9 for CAP examWhile the overall increase in dose is higher when increasing mAs from 50 to 150 hen increasing kV from 80 to 120, the resultant improvement in image quality wahigher for an increase in tube potential from 80 to 120 kV than for an increase in tube current-time product from 50 to 150 for head exams. Image quality gains corresponding to the same increases in tube current-time product and tube potential were very similafor CAP exams. However, increasing the tube potential just one step can have a tremendous effect on image quality at the low mAs values encountered in pediatric radiology. Tube potential and tube current-time product are the two protocol elements

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124 that hold the most significant influence on both dose and image quality, and therefore those seeking major reduction in dose should first seek to minimize these values in theirprotocols. Table 6-9 lists the magnitude of the average expected dose reduction when decreasing tube potential or tube current-time produc t in clinical scanning. Table protocols. 6-8. Magnitudes of average expected dose reductions when adjusting scanningAdjustment (old value new value) Expected Dose Reduction (%)* 120 kV 100 kV Head: 20, CAP: 23 100 kV 80 kV Head: 51, CAP: 57 100 mAs 50 mAs Head, CAP: 50 150 mAs 100 mAs Head, CAP: 33 *Notedose reductions vary with pitch and collimated beam width, these are average values The relationship of pitch to image quality and dose is less obvious and less straightforward than that of tube potential and mAs. One fact that is immediately apparent is that there is a large dose penalty for lowering pitch values, particularly when the pitch value becomes less than 1.0. Dose values increase by a factor of (approximately) 32 percent when decreasing the pitch from 1.0 to 0.75, and a decrease from 1.25 to 0.75 results in an increase in dose by a factor of almost 1.6. The effect of pitch on image quality is very hard to discern, but several trends can be observed in the data gathered during this work. In general, pitch values less than 1.0 provide little, if any improvement in TOS and SOS at all tube potential and tube current-time product values examined, with the exception of head exams at 80 kV, where a pitch of 0.75 yields superior SOS (a similar but weaker effect occurs at 100 kV, as well). Tube potential and tube current-time product values have far greater effects on image quality than does pitch. Pitch values greater than 1.0 generally yield inferior TOS and SOS for all protocol combinations, with the difference being most severe in CAP exams. A 15 to 20 percent dose reduction can be achieved by raising pitch values from 1.0 to 1.25, but the resultant

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125 change in image quality when making this change must be considered. Also, the minimgain in image quality that would be realized by reducing the pitch to 0.75 from 1.not justify the 30 percent increase in dose that would result. Conclusions One question looms large al 0 does after reading the Discussion sectionamidst all the sented above, what evidence produced by this study can be considered definiudy ed n the n parameters. e made. g AP scanning are listed in Table 6-9. correlations pre tive? The following paragraphs summarize the evidence gleaned from this stthat demonstrates dose and image quality trends and characteristics previously predictor intuitively expected for pediatric CT scanning. Several facts must be kept in mind when discussing CT scanning protocols. First, many imaging tasks utilize specially designed protocols, including high resolutioprotocols for extremity and inner ear imaging, and protocols designed for extracting maximum information from injected contrast. Second, many radiologists were trained in a certain fashion, and are accustomed to seeing images acquired using certaiLast, and most importantly, one must remember that the ultimate goal of any imagingstudy is to generate images of a quality that will allow an accurate diagnosis to bThese conclusions will be most useful for institutions that wish to modify existinCT protocols to lower radiation doses, because it is difficult to establish an absolute standard without a series of similar clinical images, which are already difficult to obtainfor pediatric patients; and the testing of alternate protocols on pediatric patients is likely to be frowned upon. As an example, consider Shands Hospital. The default protocols for head and C

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126 Table 6-9. Default pediatric pr otocols at Shands Hospital. Body region mAs/rotation time Tube potential (kV) Collimated beam width (mm) Pitch Head 90/0.75 s 120 24 mm (16 x 1.5) 0.58 Body 30/0.5 s 80 24 mm (16 x 1.5) 1.0 CAP 70/0.5 s 120 24 mm (16 x 1.5) 1.0 *Note: Head protocol was sequential As an example, consider the default h ead protocol. While it is difficult to determine whether the tube potential can be reduced to 100 kV for head exams while maintaining acceptable image quality, it is appa rent from Figure 6-9 that changing the collimated beam width to 12 mm (16 x 0.75) and using a pitch of 1.0 instead of 0.75 yields superior TOS and equivalent SOS, with a dose reduction of greater than 42% (keeping in mind that the default pitch was 0.58, therefore TOS and SOS may vary slightly. The compromise associated with reducing the collimated beam width to 12 mm is a doubling of the scan time, possibly leng thening the scan into the 30 second range, unacceptably long for pediatric imaging. Reduc ing the gantry cycle time (rotation time) to 0.5 second would partially alleviate this problem, and the time could be further shortened by changing to a helical scanning mode. The individual body protocol s (chest, abdomen, pelvis ) all utilized the same acquisition parameters, and were all very low dose imaging protocols. The CAP protocol could potentially benefit from a reduction in tube potential fr om 120 kV to 100 kV, which would be expected to result in no apparent difference in image quality and potentially a large decrease in dose, as seen in Figure 6-10.

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127 00 1405016 x 0.7516 x 1.516 x 0.7516 x 1.516 x 0.7516 x 1.5 203060 A Tol ObjeScore tact 0.75 1 1.25 80 kV100 kV120 kV 00.511.5233.54.550.751.01.250.751.01.250.751.01.25 2.54 50 mAs 100 mAs150 mAs Ective De (mS ffeosv) 16 x 0.75 16 x 1.5B Figuroland thicknesses for head, body, and CAP protocols were 4 mm, 3 mm, and 5 mm, respectively, and communication with the personnel at Shands revealed that additional reconstructions are often performed, down to e 6-9. (A) Total object scores (TOS) for head exams at 100 mAs, (B) effective doses for head exams at 120 kV. One final item to mention regarding these default protocs is the role of radiologists the role of reconstructed slice thicknesses. Default slice

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128 slice thicknesses of 1.5 mm. The role this might play in image quality and its relation toscan protocols requires further investigation. 0510152025 35404550 A 30 16 x 0.75 16 x 1.516 x 1.516 x 0.755 0.7516 x 16 x 1. 80 kV120 100 kV kV Total Object Score 0.75 1 1.25 2348910 016 x 0.7516 x 1.516 x 0.7516 x 1.516 x 0.7516 x 1.5 1 5 6 7 80 kV100 kV120 kV EffectiveSv) Dos e (m 0.75 1 1.25 ective doses for CAP exams at 100 mAs. B Figure 6-10. (A) Total object scores (TOS) for CAP exams at 100 mAs, (B) eff

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129 Calibration of the FOC Dosimeter In addition to the conclusions regarding pediatric CT doses, this work has also served to demonstrate that short exposure times using conventional x-ray tubes can be used to calibrate FOC dosi meters. While it is feasible to calibrate the dosimeters at the o the inside of the gantry along with some backscatter material, this approach still contains some geometrical error, ane-coconvenient process, ableting thesing a conventional x-ray tubhen correcting tfiltration t in CT scannersst accurate and enient solution. General Trends in Pediatric CT Protocols In this section, I will discuss general guidelines for pediatric CT protocols. However, I will stop short of proposing default protocols, as a baseline for acceptable ima established e protocol adjustms attempted. ny pediatriotocol should be to use as low a tube potential as possible. As mentioned previously, the large increases in dose associated with increasing the tube potential often do not justify the correspondingly small gain in age quality, while lowering the tube potential results in large dose savings, and similar image quality can often be achieved via other protocol adjustments, and in some cases, with no adjustment. Optimal tube current-time product values must be determined via phantom studies or some other methodology for each scanner at an imaging institution. Age-based, or more appropriately, mass-based, protocols should be developed that specify the appropriate tube current-time product values to use for patients falling into the various CT scanner, possibly by fixing the dosimeters, along with a small ionization chamber, t d would be a very tim nsuming and in nd especially unaccepta clinically. Calibra dosimeters u e, and t he calibration factors for the effects of the added presen is the mo conv ge e quality must b befor ent i Theoal of a first g c imaging pr im

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130 age or mass ranges. Following these determinations, adjustments can be made in the scanning protocols for newborns according to the information presented in this chapter.Default pitch values in pediatric CT imaging should be 1.0, unless (1) the goal of the imaging study is very challenging, such as the identification of very small targets in the patient, or (2) the pitch value is being reduced to offset changes in image quality due realized whh values below 1.0 are selected, and thus the image quality benefit of doing this mst be necessahe acAnother important fat protoco institutions (adult protocols, which are frequently used to scan pediatric patients, in particular) use a pitch value mu than 1.0 by defa as low as 0.5 to 0.6. On the basis of dose and image quality considerations, it would be difficult to justify the use of pitch values this low in pediatric CT, and it is imperative to ensure that dedd pediatric CT protocols are insppropriatult pitches. arent from thidence presented in this work that the bulk of pediatricaging should be peed using a collimated beam width of 24 mm (16 x 1.5) and a gantry cycle time of 0.5 second. Exceptions t 24 mm collimated beam width include the aforementioned protocol changes that provide a large decrease in dose nt or ined to reduction of tube current-time product or tube potential. A severe dose penalty is en pitc u ry, and apparent, in t quired images. act to mention here is th many installed CT ls at imaging ch less ult, some icate tatilize a lled that u e defa Finally, it is app e ev CT im rform o the d ue to reduction of tube current-time product or tube potential. It was observed that the use of a 24 mm collimated beam width results in the lowest dose and yields equivalesuperior low-contrast resolution in a majority of scanning protocols, and when combwith a 0.5 second rotation time, offers the added benefit of faster scanning, resulting in

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131 fewer motion artifacts and a reduced need for sedation of uncooperative neonatal patients. this studdetaneouslyg image quality. While the dose measurement portion of this study was very straightforward, image quality assessment presented some presentation of assessment performed in this study. While the relative comparisons made in this study are sufficien for the purpodifficult to extend these results to an ad. cation overage/necessary detection task (e.g., low contrast detection standards for ACR accreditation) would bring these typeage quality asseloser to an absolinical meaning. This being saiver, this study emely valuable clinically, as areas for improvem currently used ciatric CT imaging protocols can be identified, or pediatric CT imaging protocoeveloped and iented based on the inf within. Fueral aspects oy that require fr investigation and refinemfuture. The imty assessment m used throughout this discovered. Also, a finer and broader investigation of pitch could prove to be very useful, ining pitch values from 0.5 to 2.0 or even higher, and in particular examining the pitch values around 1.0 in finer steps. For example, evaluating pitch values from 1.0 to 1.5 in one-tenth increments could yield useful results, as even a small It is clear from y that low dose pediatric CT i maging protocols can be vl eloped while simu optimizin unique challenges. The 15 cm Catphan CTP515 module is not an ideal re a pediatric body, however, it is the most suitable tool currently available for the type of t se at hand, it is very bsolute clinical standar Perhaps the identifi f a generic a s of im ssments c lute c d, howe is still extr ent in linical ped ls can be d mplem od rmation containe ture W ork There are sev f this stud urthe ent in the age quali ethod study is in its infancy, and there are certainly areas for improvement that remain to be such as exam

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132 increase from 1.0 to 1.1 in head imaging will lead to a reduction of dose by tely) ten pe (approximarcent. The effect of reconstructed slice thicknesses on image quality alsoeserves atteng lows and then reconstructing thin-slice imental ity. Experimental developmechnique chartsube current-timAs) to pediatric patient ma task that, once will provide another piece in the puzzle of pediatrictocols. Finally, that the need fre development will arise as tube current modulation techniques become more sophisticated and CT scanner design advan d tion, as scanning usin dose technique ages could be detrim to image qual ent of t relating t e product (m ass is completed CT pro it is likely or mo ces

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CHAPTER 7 DOSE COMPARISON BETWEEN PHYSICAL MEASUREMENTS AND COMPUTATIONAL SIMULATIONS The companion part of the Pediatric Organ Dose (POD) project at the UniversityFlorida is computational phantom development. Just as has been done in this work wthe physical tomographic newborn phantom, organ and effective doses have been calculated for the identical computational phantom developed by Nipper at the Univof Florida.91 The computed tomography simulations and calculations using the of ith ersity ll exams perfh experimulated, were helical scans. What follows here is a comparison of orges betorn physical and cGenerally speaking, measured effective doses in the physical phantom for head exams were very similar to the effective doses calculated from simulations using the computational phantom (up to a 20% difference), with organ doses showing more differences in some cases (generally ranging from 20-40%, and in a few cases higher, up to 80%t of the similfective doses be the two phantoms is un large numry small organ doses and organ doses equal to zero recorded during head exame relatively smrtion of anatomy scanned. Nonetreement betwured and simulated effective doses is viewed as gan and ffective dose values for head exams at a tube current-time product of 100 mAs, computational phantom were performed by Robert Staton as part of his dissertation.95 Aormed, bot ental and sim an and effective dos ween the newb omputational phantoms. ). Some par arity of ef tween doubtedly due to the ber of ve s, due to th a ll po heless, the ag een meas a success for the project. Tables 7-1 through 7-3 show comparisons of or e 133

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134 collimated beam width of 12 mm, pitch of 1.0, and tube potentials of 80, 100, and 120 kV, respectively. Data corresponding 4 mm collimated beams follonds as thnly slight variations in dosethese data cad in the tables iter 6 (physical phantom) orissertation 7-4 compares effective dose values for all also that measudata sets, to organ doses for 2 ws the same tre e 12 mm collimated beam width data, with o n be foun n Chap Table 95 in Robert Statons d head exams for which data is available from computational simulations. Several characteristics concerning the data presented in Tables 7-1 through 7-4 deserve mention. First, it can be seen that the trends present in the measured data are present in the simulated data. Perhaps the most prominent example of this is the fact a collimated beam width of 24 mm always produces higher effective doses in both red (as discussed previously in Chapter 6) and simulated data, as illustrated in Table 7-4. Also, increases in tube potential produce very similar effects in both with a change from 80 kV to 100 kV leading to a 100% increase in dose, and increases from 100 kV to 120 kV causing a 30-40% increase in dose. Second, organs that were considered to have received zero dose (without measurement) during head exams in the physical phantom due to their location being well outside the primary beam are noted to have received very insignificant doses in the computational phantom. These data prove that this technique is indeed valid and does not lead to significant errors. Finally, it is noted that the doses to extended organs (e.g., bone marrow, bone surface, etc.) within the head are very similar between measured data and simulated data, proving that our point-to-organ dose scaling factors (SFPOD) are an effective tool for calculating accurate organ doses in physical phantoms.

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135 Organ doses for CAP exams, however, tell a different story. Simulated effectivedoses for CAP exams a re 60-75% higher than measured doses in the physical phantom, with omGy) for head exams at 80 kV, 100 mAs, 12 mm collimated beam width, 1.0 Measured Simulated rgan doses differing by as much as 120% (or as low as 5% for remainder doses), the simulated doses again being higher than measured doses. Tables 7-5 through 7-7 show comparisons of average organ and effective dose values for CAP exams at a Table 7-1. Comparison of simulated and measured effective and organ doses (mSv or pitch. Effective Dose (%SD) Effective Dose* 0.87 (2.1) 0.84 Selected Organs/Tissues Organ Dose (%SD) Organ Dose* Brain 5.48 (12.2) 7.82 Thyroid 5.08 (5.3) 3.39 ymus Th2.36 (26.4) 0.62 ng (11.7) 0.41 (15.0) 0.99 .00 0.15 l 0.00 0.14 Rectum/Sigmoid Colon 0.00 0.05 Skin 1.42 (15.7) 1.82 Lu 0.76 Esophagus 1.130 Spleen Stomach Wal Liver 0.00 0.15 Pancreas 0.00 0.11 Kidney 0.00 0.06 Small Intestine 0.00 0.05 Gonads (Ovaries) 0.00 0.01 Urinary Bladder Wall 0.00 0.01 Uterus 0.00 0.01 Bone Marrow 2.59 (1.8) 2.90 Bone Surface 7.47 (1.8) 8.70 Remainder 1.46 (5.0) 1.49 *Simulated dose data received from Robert Statonno standard error values provided. tube current-time product of 100 mAs, collimated beam width of 12 mm, pitch of 1.0and tube potentials of 80, 100, and 120 kV, respectively. Table 7-8 compares effective dose values for all CAP exams for which data is avai lable from computational simulations. Although one might expect the agreement in CAP effective doses between the physical and computational phantom to be somewhat less than for head exams (again,

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136 because relatively few organs recei ve non-zero doses as a result of head exams, while a major in mGy) for head exams at 100 kV, 100 mAs, 12 mm collimated beam width, 1.0 ity of the organs receive non-zero doses as a result of CAP exams), differences ofthe observed magnitude are certainly surprising. These differences will be addressedthe following discussion. Table 7-2. Comparison of simulated and measured effective and organ doses (mSv or pitch. Measured Simulated Effective Dose (%SD) Effective Dose* 1.76 (4.3) 1.56 Selected Organs/Tissues Organ Dose (%SD) Organ Dose* Brain 11.0 (18.5) 14.24 Thyroid 9.63 (15.3) 6.16 Thymus 3.76 (9.5) 1.18 Lung 1.57 (4.8) 0.77 Spleen 0.00 0.29 Liver 0.00 0.30 0.00 Kidney 0.00 0.14 Small Intestine 0.00 0.10 Gonads (Ovaries) 0.00 0.02 Urinary Bladder Wall 0.00 0.03 Rectum/Sigmoid Colon 0.00 0.11 Uterus 0.00 0.02 Bone Marrow 5.55 (3.4) 5.54 Remainder 2.81 (9.4) 2.69 Esophagus 2.24 (8.7) 1.90 Stomach Wall 0.00 0.28 Pancreas 0.23 Skin 2.73 (29.7) 3.20 Bone Surface 14.3 (3.3) 14.84 *Simulated dose data received from Robert Statonno standard error values provided. data are also present in the simulated data. A collimated beam width of 24 mm always previously in Chapter 6) and simulated data. Also, increases in tube potential produce Again, as with head exams, it can be seen that the trends present in the measured produces lower effective doses for CAP exams, both in measured (as discussed very similar effects in both data sets. Another trend that is seen more clearly in CAP exams is the fact that when scanning a very small (i.e. neonatal) patient, the body, and its

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137 organs, are irradiated very uniformly. Organ doses are very similar through the entire body, and adjacent organs (e.g., urinary bladd er, rectum/sigmoid colon, and uterus; or stomach and spleen) recason that tube current ed to have a small, if detectable, effect on organ doses deliveated beam width, 1.0 eive almost identical doses. It is for this re modulation techniques are expect red to neonatal patients. The most revealing data with regards to the Table 7-3. Comparison of simulated and measured effective and organ doses (mSv or mGy) for head exams at 120 kV, 100 mAs, 12 mm collimpitch. Measured Simulated Effective Dose (%SD) Effective Dose* 2.26 (4.4) 2.28 Selected Organs/Tissues Organ Dose (%SD) Organ Dose* Brain 13.4 (15.4) 20.3 Thyroid 12.1 (16.0) 9.04 Thymus 5.55 (18.5) 1.74 Lung 2.14 (9.3) 1.14 Esophagus 3.05 (5.5) 2.69 Spleen 0.00 0.44 Liver 0.00 0.45 Kidney 0.00 0.21 0.00 Gonads (Ovaries) 0.00 0.04 Urinary Bladder Wall 0.00 0.05 Rectum/Sigmoid Colon 0.00 0.16 Uterus 0.00 0.04 Skin 2.89 (33.4) 4.48 Bone Marrow 7.35 (3.4) 8.19 Remainder 3.32 (10.2) 3.80 Stomach Wall 0.00 0.43 Pancreas 0.00 0.36 Small Intestine 0.16 Bone Surface 17.0 (3.3) 19.95 *Simulated dose data received from Robert Statonno standard error values provided. aforementioned disagreement between measured and simulated dose data is that of the doses to extended organs (e.g., bone surface, bone marrow, etc.). These values are calculated using identical methods (i.e., weighting factors, marrow dose conversion factors, etc.see Chapter 4 for details) for both measured and simulated exams, and throughout the head exams, values for these doses were very similar. This points to some

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138 Table 7-4. Comparison of simulated and measured effective doses for head exams at 100 mAs and a pitch of 1.0. Effective Dose (mSv) Protocol k V, beam width Measured (% SD) Simulated* 80, 12 mm (16 x 0.75) 0.87 (2.1) 0.84 80, 24 mm (16 x 1.5) 0.90 (6.8) 0.90 100, 12 mm (16 x 0.75) 1.76 (4.3) 1.56 100, 24 mm (16 x 1.5) 1.77 (7.6) 1.69 120, 12 mm (16 x 0.75) 2.26 (4.4) 2.28 120, 24 mm (16 x 1.5) 2.49 (2.4) 2.44 *Simulated dose data received from Robert Statonno standard error values provided. Table 7-5. Comparison of simulated and measured effective and organ doses (mSv or pitch. Measured mGy) for CAP exams at 80 kV, 100 mAs, 12 mm collimated beam width, 1.0 Simulated Effective Dose (%S D) Effective Dose* 2.50 (4.0) 4.44 Selec ted Organs/Tissues Organ Dose (%SD) Organ Dose* Brain 0.00 0.11 Thyroid 2.5 (17.0) 1.24 Thymus 3.41 (7.6) 5.24 Lung 3.48 (8.9) 5.58 Spleen 2.46 (13.8) 5.27 Liver 2.47 (11.2) 2.76 (8.1) Esophagus 2.82 (6.6) 4.25 Stomach Wall 2.64 (15.5) 5.37 5.45 Pancreas 5.31 Skin 1.26 (17.0) 2.13 Bone Surface 2.69 (1.9) 5.24 Kidney 2.53 (17.6) 5.54 Small Intestine 3.15 (7.66) 5.66 Gonads (Ovaries) 3.30 (15.2) 5.48 Urinary Bladder Wall 3.03 (17.3) 5.65 Rectum/Sigmoid Colon 3.03 (9.3) 5.63 Uterus 3.56 (5.3) 5.11 Bone Marrow 0.96 (1.7) 2.05 Remainder 2.24 (3.4) 2.71 *Simulated dose data received from Robert Statonno standard error values provided sort of systematic error as the cause for the disagreement between simulated and measured doses.

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139 Table 7-6. Comparison of simulated and measured effective and organ doses (mSv or th, mGy) for CAP exams at 100 kV, 100 mAs, 12 mm collimated beam wid1.0 pitch. Measured Simulated Effective Dose (%SD) Effective Dose* 5.56 (2.9) 8.92 Selected Organs/Tissues Organ Dose (%SD) Organ Dose* Brain 0.00 0.23 Thyroid 5.53 (6.1) 2.60 Lung 7.47 (6.8) 10.93 Thymus 7.46 (8.5) 10.54 Esophagus 5.99 (12.3) 8.65 Stomach Wall 6.28 (9.9) 10.78 Pancreas 10.83 Skin 2.72 (14.9) 4.19 Bone Surface 5.79 (1.6) 9.88 Spleen 5.99 (11.8) 10.69 Liver 6.74 (2.0) 10.93 5.78 (11.5) Kidney 5.95 (10.9) 11.19 Small Intestine 7.29 (6.5) 11.37 Gonads (Ovaries) 7.72 (12.5) 10.91 Urinary Bladder Wall 6.33 (13.7) 11.26 Rectum/Sigmoid Colon 6.33 (7.3) 11.27 Uterus 6.74 (7.3) 10.31 Bone Marrow 2.38 (1.5) 4.38 Remainder 5.16 (2.4) 5.41 *Simulated dose data received from Robert Statonno standard error values provided computationally derived effective and organ doses is not only important to the work with the newborn phantoms, it is vital for the ongoing work of the Pediatric Organ Dose (POD) group. The identification of the source(s) of disagreement will allow us to eliminate errors and learn how to most effectively use our computational and physical phantoms. The factors described above pointed to a source of systematic error. One possible source of error that was identified was the difference in energy fluence (after filtration) when using the body filter versus the head filter. The Siemens Sensation 16 CT scanners Possible Sources of Error Identifying the source of the disagreement between experimentally measured and

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140 employ two different amounts of filtration depending upon the type of exam that is being performed. Head exam protocols trigger the use of a filter of approximately 5.5 mm Al, while body exam protocols trigger the use of a filter of approximately 11 mm Al. This extra filtration makes the x-ray beam more penetrating for body exams, allowing for adequate image quality (via the beams ability to penetrate the thicker anatomy of the body) while delivering as little dose as possible. Also, the added filtration for body exams significantly reduces the energy fluence (at the table) for body exams as compared to head exams. This might seem counterintuitive at first, but the tube current-time product (or equivalent value if using modulation techniques) is set by the user on the mAs, 12 mm collimated beam width, Table 7-7. Comparison of simulated and measured effective and organ doses (mSv or mGy) for CAP exams at 120 kV, 100 1.0 pitch. Measured Simulated Effective Dose (%SD) Effective Dose* 7.16 (2.7) 12.46 Selected Organs/Tissues Organ Dos e (%SD) Organ Dose* Brain 0.00 0.35 Thyroid 7.57 (3.3) 6.30 Thymus 10.0 (13.2) 14.50 Lung 9.42 (6.5) 15.23 Esophagus 7.62 (8.0) 12.28 Spleen 6.72 (8.3) 14.52 Stomach Wall 7.48 (10.5) 14.55 Pancreas 7.56 (9.5) 14.32 Small Intestine 8.96 (6.0) 15.33 Urinary Bladder Wall 8.41 (4.3) 15.55 Rectum/Sigmoid Colon 8.41 (9.4) 15.36 Uterus 10.0 (2.9) 14.20 Skin 3.73 (17.0) 6.14 Bone Marrow 3.34 (1.7) 6.90 Bone Surface 7.33 (2.0) 14.97 Liver 8.71 (4.7) 14.79 Kidney 6.74 (8.4) 14.94 Gonads (Ovaries) 9.22 (10.4) 14.97 Remainder 6.57 (2.1) 7.86 *Simulated dose data received from Robert Statonno standard error values provided

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141 Table 7-8. Comparison of simulated and measured effective doses for CAP exams at 100 mAs and a pitch of 1.0. Effective Dose (mSv) Protocol kV, beam width Measured (% SD) Simulated* 80, 12 mm (16 x 0.75) 2.50 (4.0) 4.44 80, 24 mm (16 x 1.5) 2.14 (4.2) 4.10 100, 12 mm (16 x 0.75) 5.56 (2.9) 8.92 100, 24 mm (16 x 1.5) 4.88 (1.7) 8.17 120, 12 mm (16 x 0.75) 7.16 (2.7) 12.46 120, 24 mm (16 x 1.5) 6.55 (2.3) 12.42 *Simulated dose data received from Robert Statonno standard error values provided. console, and tube current adjusted appropriately by the system based on tube potential, gantry cycle time, tube loading, and other factors. No automatic adjustment is made when switching between head and body protocols to account for the reduced output. Baseepancy between measured and simulated organ and effective dose data 100100 organ doses in the computational phantom. The m100100100 Incorrect Use of Normalization Factors d on the information given above, the large discr could be caused by the use of incorrect normalization factors to calculate doses from simulation data. When calculating organ doses from simulation tallies (usually in energy/photon or some similar quantity), a factor must be calculated that will allow the conversion of the simulation tally (derived from a certain number of histories) to an organ dose that would be delivered during that exam on the CT scanner. This was accomplished (in this case) by making CTDI measurements using a 16 cm CTDI phantom and 3 cc pencil ionization chamber positioned at the central axis of the phantom. The same CTDI measurement is then predicted using the same simulation engine that was used to calculate easured CTDI values (along with the tube current-time products used) and simulated CTDI values (along with the number of photon histories run to simulate the CTDImeasurement) can then be used in conjunction with information on the number

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142 of source rotations during a given exam to normalize CT simulation results to yield meaningf ul dose data. In this case, using norm measured CTDI100 values) for th h exams. alization factors calculated (from e head to normalize body data would lead to the approximate errors detailed inTable 7-9. These probable errors were calculated by measuring the output from the CT scanner used at all three tube potentials used in this study using both the head and body filters. The source of error could also be incorrectly simulated CTDI values, in whiccase the magnitude of error expected cannot be calculated without detailed information regarding the computational simulations. Table 7-9. Approximate errors associated with using head normalization factors for CAP 80 kV 100 kV 120 kV Output Ratio 0.76 Body/Head 0.67 0.73 As exs for rs, slight positioning errors within the phantC pected, the largest errors are seen at lower tube potentials, due to the larger percentage of low energy photons present in the x-ray beam at lower kV values. Errors of this magnitude would reduce the disagreement between measured and simulated dosefrom almost 50% to approximately 10-15%, an error that is much more reasonablethese types of data. Differences of 10-15% are not unusual with these types of measurements and simulations. Sources for these types of errors could include bends in the dosimeter fibers, calibration geometry erro om, and integration of dose measurement over different volumes within the FOdosimeters than with simulated dosimeter locations.

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143 O ther Possible Sources of Error he aforementioned explanation for the differences seen between measured and simulated effective and organ dose data is only one possible explanation, and other sources for large errors are possible. There are many potential sources of error in both computational simulations and physical measurements. Other possible sources of error include inadvertent multiplication of data by a factor of 2, incorrectly designed computer simulations (e.g., incorrect programming of pitch), problems in the software used to read the CCD, slight variations in tissue-equivalent material composition, violations of cavity theory (including lack of charged particle equilibrium), dosimeter energy response, spectral coupling of the FOC dosimeter output and the CCD, dosimeter positioning errors, point-to-organ dose scaling factors, and fiber bending. However, every effort has been made to eliminate or prevent these types of errors, and the data and software used in this study has been examined repetitively and thoroughly to avoid and/or locate these errors, although possible error sources remain. One such possible source is the computer (and DAQ card, which requires an ISA bus slot) currently used to run the CCD7041 program. The processor speed of the computer is 600 MHz, and this fact, combined with the slower speed of the old DAQ card and an inefficient interface with the CCD, cause the loop time for execution of the code to approach one second, which could cause doses measured using the FOC dosimeters to be artificially low. However, this can only be corrected with the acquisition of a faster PCI DAQ card which would be installed in computer with a much faster processor. This would also require a modest remodeling of the CCD7041 software which would improve the efficiency of the CCD reading interface as well. The use of photomultiplier tubes instead of a CCD for fiber imaging would also T

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144 solve this problem, as well as eliminate the need for a DAQ card; however, this option is prohibitively expensive at this time. Investigation of Possible Error Sources Physical Measurement Errors The investigation of the abogoing. It is possible, but improta, when lso, the spectral coupling of the light output fro The light output from t as a sponse, and ven to its maxims of ve sources of error is on bable, that many small errors could combine to yield large errors in measured dacausing the discrepancy between measured and simulated data. Photon spectra at increasing depth in tissue in a broad-beam geometry were examined via Monte Carlo simulation, and the results demonstrated that the average energy of a CT spectrum (80-120 kV filtered through 5.5 or 11 mm Al) does not varysignificantly up to 7 cm deep in soft tissue. Experimental measurements also verified thisresult, showing that the response of the FOC dosimeter is not significantly affectedused at depth in tissue-equivalent material (and, in fact, the dosimeter response per unit dose actually increases slightly at greater depths in tissue). A m the FOC dosimeter and the CCD was investigated. the FOC dosimeter has a spectral range of approximately 550-650 nm, placing inear the peak efficiency of the CCD, and yielding an efficiency difference of only 6-7 percent across the spectral range. Fiber bending is not expected to be a problembend in all fibers was introduced during calibration to facilitate the alignment of the dosimeters, and this bend was tight enough to induce a 10% reduction in re only an additional 5% reduction is expected when the bend radius is reduced, e um. Tissue composition variations and dosimeter placement errors throughout the calibration and measurement process undoubtedly exist, but the effects of these type

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145 errors are known to be small compared to the large discrepancies between measured ansimulated dose data. Also, a few simple organ dose measurements using t d he single-fiber PMT system were scan is shown in Figure 7-1. urce of the spurious data. To test this MT, and the scan was repea performed in an attempt to determine if the previously mentioned software and interface problems were indeed responsible (in whole or in part) for the discrepancy between measured and simulated doses. The results of these measurements were inconclusive, and provided no real insight into the potential software problem; however, other interesting aspects of the FOC dosimeter, and the single-fiber PMT system, were revealed as a result of these measurements. Two main discoveries were made during the aforementioned measurements, one unique to systems using PMTs, the other a general problem affecting FOC dosimetry in general. First, while doing simple organ dose measurements, it was noted that the graphical data output from the fiber was not behaving as expected (temporally). The datafrom the first The expected raw data graph would increase as the dosimeter moved towards the center of the gantry (and therefore the x-ray source), peak, and then decrease as it began to move past the gantry. However, as can be seen from Figure 7-1, this is not the case. It quickly became apparent that while the active area of the dosimeter moved through the gantry, the steel box containing the PMT and associated electronics was moving closer to the source throughout the scan, and might be the so hypothesis, a lead vest was placed over the box containing the P ted. The raw data from this scan is shown in Figure 7-2, and behaves as one would expect. Therefore, it was determined that significant amounts of photocathode emissions

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146 0 160000018000002000000 200000400000800000100000000000ount rte (c 600000121400000Time (arbitrary units)Caounts/time) Figure 7-1. Raw data from original organ dose measurement scan (total scan time approximately 4 seconds). were being generated by scatter and leakage X-rays from the source. Future FOC systems using PMTs as detectors should include sufficient shielding around the PMTs so as to prevent these unwanted photocathode emissions. A lining of one-sixteenth inch lead sheet inside the system housing would be more than sufficient to solve this problem. ch cable was also tested. The active area of the dosimeter was placed well outside the gantry, and was shielded with a lead apron (0.5 mm Pb equivalent), as was the PMT housing. The fiber optic patch cable section that was in the gantry was scanned (single rotation) under both shielded and unshielded conditions. The results are illustrated in Figure 7-3. These graphs demonstrate that the fiber optic patch cable is acting as a scintillator, the unshielded response being an order of magnitude greater than the shielded response. This effect has also been observed by Houston at the Naval Research Laboratory, who also mentioned that the photons generated as a result of the irradiation Second, after verifying the spurious response from the PMT, the fiber optic pat

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147 0Time (arbitrary units)Figure 7-2. Raw data from dose measurement scan with PMT housing shielded (total scan time approximately 4 seconds). of the patch cable will be blue (the desired signal from the active area is green), and can therefore can be eliminated by including a filter in the optics of the system. Computational Simulation Errors While I can speak on possible errors and the investigation of possible error sourcein measurement data, I cannot do the same for computational errors. A few of the possible error sources have been mentioned previously, and with all of the elements thatcompose a simulation tool, more possibilities certainly exist. One facet that I will discuss is the point-to-organ dose scaling factors (SFPOD) developed for use in CT exams. The SFPOD values that were calculated by Staton95 and provided for use in these empirical CTexams were all equal to one, with the exception of skin scaling factors, which were lessthan one and varied depending on exam 10000050000060Cane) s type. Large, uniform organs, or small organs, such 2000003000004000000000ount rte (Couts/tim as the brain and ovaries, might be expected to have SFPOD values of one.

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148 0Time (arbitr 200000 40000060000080000010000001200000140000000000ary units)Count rate (counts/time) 16 02000040000 6000080000100000120000140000000Time (arbitrary units)Coun rate (cnts/ti) Note difference in vertical scale (counts/time). However, the locations of organs such as the thyroid, thymus, and esophagus are only partially contained in either head or CAP exam coverages. This fact, combined 160toumeFigure 7-3. Raw data from scanning of (A) unshielded and (B) shielded patch cable. with the dosimeter locations in the physical phantom, would seem to suggest that SFPOD values for these organs should be less than, and not equal to, one. Even large organs that are heavily shielded, such as the liver and the lungs, might be expected to have associated A B

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149 SFPOD values less than or greater than one, depending on dosimeter location in the physical phantom. This is unlikely to be a major source of error for CAP exams, but could be a large contributor to dose discrepancies between measured and simulated data for head exams. Also, a recent paper by Dixon et al. exploring improved analytical models for CT dose simulation presents data showing significantly decreasing output with increasioff-axis angles, especially when the body bowtie filter is used.146 This could also partially account for some of the differences seen between measured and simulated dose data, although this effect is expected to be minimal in small patients, and increase for larger patients. Conclusions and Future Work Several aspects of this work make the identification of error sources in either experimental or simulated data difficult. First, th ng ere are many elements that combine to ta, and isolating each of these elements for indivies nal phantoms created from the same data set. Fmany new with a static x-ray tube) for each of the energies examined (80, 100, 120 kV) during this produce both experimental and simulated da dual testing and verification is time consuming and challenging. Second, the Pediatric Organ Dose (POD) group is the first research team to attempt to quantify dosusing both tomographic physical and computatio inally, in attempting to quantify the doses delivered to pediatric patients, technologies and techniques have been utilized for the calculation and measurement ofdoses, making the troubleshooting process very challenging. A simple experiment was designed to examine the agreement or disagreement between measured CT doses and simulated CT doses. The single-fiber FOC system was calibrated (free-in-air) using a 3 cc pencil ionization chamber at the CT scanner (

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150 experiment, using both head and body protocols. Then, the calibrated FOC dosimeter was used to measure doses at the center of a 16 cm CTDI phantom during singl e rotation scans using the same energies and protocols. To complete the study, the same geometry and phantoms will be simulated by another member of the POD group, and doses at the center of the CTDI phantom will be computed and compared with the measured doses to examine agreement and attempt to identify sources of disagreement between the experimentally measured doses and the doses calculated using simulations.

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CHAPTER 8 CONCLUSION Results of this Work The goal of this work was the identification of low-dose imaging protocols that maintain adequate image quality in pediatric computed tomography. This is extremely important because the lifelong effects of the effective doses and organ doses delivered tpediatric patients as a result of CT exams are unknown, as the first patients who benefitefrom this technology are just now entering their late 20s and early 30s. Therefore,every possible step must be taken to reduce the radiation doses delivered in these until the effects of those doses are fully understood. This dissertation has laid the groundwork for future work with physical phantom dosimetry, in particular the areas ophantom construction and dosimetry system design and use. Opportunities for Futu o d exams f re Work and Development The process used to produce the tissue-equivalent materials (especially STES-NB) used in the construction of the newborn physical phantom resulted in a layer of air bubbles trapped near the surface of the cured tissue-equivalent material, which Throughout the course of this work, several opportunities for future work and development of the Pediatric Organ Dose (POD) group at the University of Florida have been identified. These areas include production of tissue-equivalent materials, automation of the physical phantom construction process, and construction of image quality phantoms, and they will be discussed in the following paragraphs. Improvement of the Production Process for Tissue-Equivalent Materials 151

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152 necessitated a time consuming sanding process for removal (a discussion of the tissue-equivalent material pouring process can be found in Appendix A, and a discussion of the pre-processing of the blanks can be found in Chapter 3). The simplestin a vacuum chamthe d e ve the ng and s rious ber. Autophysical phantom construction process in order to expedite the construction of a family of physical phantoms spanning the entire human age range. Also, as discussed previously in way to eliminate this problem is to mix the material ber. Therefore, a vacuum chamber large enough to allow for the stirring of the material was constructed. A 30 gallon steel drum with a gasketed lid was used as basis for the vacuum chamber. The lid contained all the necessary fittings for attachment of all hoses (2 in-line valves with hose barbs), simplifying the construction process. Asmall cordless 12 V electric drill/screwdriver was used to power the stirring process, ana large paint stirrer was used to perform the actual stirring. Two expandable rods with rubber feet on their ends, along with hose clamps, were used to support the electric drill in the drum, and to hold the trigger at the appropriate position to achieve the desired stirring speed. The apparatus is powered on and off by simply inserting or removing thbattery pack. A standard vacuum pump connected by a hose to one fitting is used to remoair from the chamber, while a flapper-type valve commonly used in air conditionirefrigeration systems was connected in a feedback circuit (designed by Luis Benevides apart of his Ph.D. dissertation) to both the other fitting and the power of the vacuum pumpitself in order to regulate the pressure in the vacuum chamber. Figure 8-1 shows vaviews of the newly constructed vacuum cham mation of the Physical Phantom Construction Process It has been a long-time goal of the POD at the University of Florida to automate the

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153 Chapter 3, the construction process involved in creating the newborn physical phantomwas both tedious and time consuming. Therefore, steps have been taken towards automating the physical phantom construction process. Several options were considered for automation of the construction process. Thefirst was Computerized Numerically Controlled (CNC) machining. However, as we approached several companies about performing this service, we were met with a number of reasons as to why it could not be done, such as the materials we were using, the thinness of the slices we wanted, the forma t of our data, etc. In the few cases where we A B C Figure 8-1. Various views of the completed vacuum chamber. The flapper used for pressure control can be seen in C.

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154 were told it might be possible, the work would have remained cost-prohibitive. We also considered using commercially available software to convert our bitmap (raster) dmore usable vector data, however, the other problems with CNC machining still hadaddressed. Another option suggested by a member of our group was a rotary routing/engraving machine, similar to the type used to make many of the signs seen around campus. Several manufacturers were contacted, but the problem of our data format remained. Finally, after months of searching, a company was located that ata to to be not only distributed rotary lows. l base under the blank, and the first cutting step will remove soft tissue-equivalent material (STEM) where anung tissue, or air regions are to be locate engraving machines (most have a maximum z-axis stroke of 1.5, sufficient for theslice thicknesses we use in phantom construction), but also a software package that included the ability to perform raster-to-vector conversion, which would transform our bitmap data to vector data that would be usable by the engraving machine. Subsequently, a Vision 1624 Engraving Systema was purchased for the purpose of automating the phantom construction process. The basic outline of the phantom construction process will now proceed as folFirst, soft-tissue equivalent material will be mixed and poured into a mold onto an aluminum slab to form a perfectly flat blank of the desired size. Next, the bitmap image of the current slice will be processed using the engraving systems accompanying software, and then sent to the automated engraving table controller. A sacrificiamaterial will be placed y bone tissue, l d within the slice. After these are removed, any necessary bone tissue-equivalent Engraving Systems Support, Inc., 37739 Robinson Ave., Dade City, FL 33523-3439 a

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155 material will be introduced into the slice, as well as any lung-tissue equivalent material needed. Following these additions, the unfinished slice will be clamped back onto the engraving table in the same position (again with a sacrificial base piece beneath it), and the slice (only areas included in the slice, and not the entire blank) will be machined to the correct thickness. Finally, the slice outline will be completely cut from the blank, resulting in a finished phantom slice. Photographs of the new phantom construction system are provided Figure 8-2. Figure 8-2. Photographs of the new phantom construction system. Image Qu ality Phantom Construction The final area identified for improvement as a result of this work is image quality phantoms, particularly for multi-detector computed tomography (MDCT). Current image quality phantoms are limited in their ability to accurately assess all aspects of image quality in the rapidly advancing field of diagnostic medical physics. The tool used in the work presented in this dissertation, the Catphan 500b, can be used to perform most, if not all, of the tests needed for acceptance testing of CT scanners and routine quality b The Phantom Laboratory, P.O. Box 511, Salem, NY 12865-0511

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156 assurance, but still has its shortcomings when the current state of CT technology is considered. As an example, new CT scanners from Siemensc now have the capability to modulate the tube current in not only the axial (z) direction, but also angularly within ecannot assess the effects of this type oodulation on image qualitya non-unifoan d. d ies ach tube rotation (CareDose 4D). Therefore, cylindrical image quality phantoms f tube current m rm phantom shape is necessary. It is for this reason that the construction of elliptical image quality phantom that can measure the spatial resolution (up to 20 line pairs per cm), the modulation transfer function (MTF), and the low contrast resolution of modern CT scanners, with or without the effects of tube current modulation, is proposeHowever, it would be foolish to neglect the tools that have already been researched andeveloped for image quality assessment in CT. Therefore, the proposed phantom wouldutilize the Catphan 500 image quality phantom as its base, with elliptical annuli of various major and minor axes constructed to simulate the average shape of human bodof various dimensions (adults corresponding to 10th, 50th, and 90th percentiles). Also, theelliptical annuli would be manufactured with removable adapter rings that would allow them to be used with either the Catphan 500 (complete phantom or individual modules) or the AAPM 32 cm and 16 cm body and head CTDI phantoms.28 An illustration of the proposed image quality phantom is provided in Figure 8-3, with the dimensions of the proposed elliptical annuli listed in Table 8-1. c Siemens Medical Solutions USA, Inc., 51 Valley Stream Parkway, Malvern, PA 19355

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157 Modifications to FOC Dosimetry System Several areas for improvement in the FOC dosimetry system were discovered whworking with the prototype system. These improvements will make the system more user-friendly, as well as more hardy and robust. The first, and most helpful, modification to be made to the system is the replacement of the data acquisition (DAQ) card. The DAQ card currently being used with the system (see Chapter 5) requires an ISA bus slot, which are only available in older desktop computers. This makes the system unnecessarily bulky (and possibly contributes to difference ile s in measured and simulated data, as discussed previously), requirments,d ing the user to carry a large CPU, a monitor, keyboard, mouse, and power accessories. A new PCI DAQ card, such as the PCI-6280 from National Instruwould allow for the use of a laptop computer, instead of a desktop, to perform Figure 8-3. Schematic of the proposed modifications to the Catphan 500. National Instruments Corporation, 11500 N Mopac Expwy, Austin, TX 78759 d Minor axis 32cm 32 Major axis D D D

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158 measurements, making the system much more portable. In addition, a newer DAQ cawould provide improved temporal resolution for sampling, and more input channels. Thinstallation of a new DAQ card would require some software modification, but improvements to make the code more efficient could be implemented simultaneously. Table 8-1. Data for construction of proposed modifications to the Catphan 500. rd e Adapter Rings Elliptical Annuli Outer Diameter Inner Diameter (D) Major Axis Minor Axis 32 cm 20 cm 38.7 cm 26 cm 32 cm 16 cm 31.8 cm 21.5 cm 32 cm 15 cm 44.9 cm 3 1.6 cm Note: Data for elliptical annuli dimensions derived using PeopleSizee Second, it is recommended that frequent users of the FOC dosimetry system have access to some type of fiber repair capability, such as a plasma fusion splicer, which is used to manufacture the fiber dosimeters. It is not difficult to damage the dosimthe most frequent type of damage being the separation of the tip from the patch cable. This type of damage is easily repaired if one has access to a plasma fusion splicer. Another beneficial addition to the system would be the replacement of one of the 4 59eters, th) for skin dose measud closely, l scan, the mm fiber tips used in this study with a longer fiber tip (2-3 cm in leng rements. When the organ dose data presented in Chapter 6 is examineone will notice that the skin doses frequently have large standard deviations associated with them. There are two factors that contribute to this effect. One is that the skin dosimeter is not surrounded by a scattering medium, as it is on the surface of the phantom. Second, the skin dosimeter may not be irradiated directly during a helicadepending on the tube position at which the x-ray beam is turned on. In this case e PeopleSize, Open Ergonomics Ltd., www.openerg.com, Last accessed 2/22/2006

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159 skin dosimeter would be irradiated only by scatter radiation, leading to inaccurately lowskin doses with high standard deviations. n the tips of the fibersal a to This material, as suggested by the author, is a type of shrink-wrapping that is desired. group Finally, it is recommended that a different type of coating be used o The coating currently used is a black epoxy-type material. However, this materidoes have a tendency to allow some light to reach the active area of the dosimeter, in addition to frequently being non-uniformly distributed on the tip. This could introduce slight angular dependence into the fiber response, and it makes insertion into small dosimeter channels difficult, occasionally resulting in broken fiber tips. In response inquiries and suggestions regarding this issue, both Alan Huston (U.S. Naval Laboratory)and Toshihide Ushinof claimed to have identified a superior coating material to replace the epoxy. completely light tight, and will lend more structural support to the fiber tip, as The Future of the Pediatric Organ Dose Project The future of the Pediatric Organ Dose (POD) project at the University of Florida is bright. Much attention is being focused on quantifying and reducing the doses delivered to patients of all ages during CT exams. The obvious focal point for the should be the development of a tool to replace CTDI values with organ and effective doses on the CT scanning console. CTDI values have little meaning in regards to the actual organ doses received by patients undergoing exams, and have virtually no meaningto the technologists (and one would argue to radiologists and medical physicists as well)Displaying effective dose and organ dose values would paint a clearer picture of what doses were actually being delivered, what effects one might expect, thereby providing f Toshihide Ushino, Research Director, Global Dosimetry Solutions, Inc. 2652 McGaw Avenue, Irvin92614 e, CA

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160 more encouragement to reduce techniques and search for lower dose protocols. Thebuilding blocks for these tools are available, including families of computational phantoms, and software programs to allow the morphing of th ese phantoms to ize and age. These, combined with fast (photon only) Monte Carlo es with or ed -ray 2. Fluence map at isocenter 4. Tube current modulation data Item number three, tube output, is easily obtained via single rotation exposure measuremanuals distributed with the equipment in the form of mm Al equivalent. With this represent patients of any s simulations, could provide detailed dose data for any exam and any patient beforethe exam, especially when using the computational power (dual or quad processors, memory) available on the CT workstations. At the same time, physical phantom development must proceed as well. However, the time may be right for a shift from the development of physical dosimetry phantoms tothe development of innovative image quality phantoms, as many currently used image quality phantoms are not sophisticated enough to identify image quality differenccurrent technology. It is unwise to focus completely on only one aspect of the pediatric radiology picture while entirely neglecting the other equally important aspect. As fdosimetry, the limitations of computational dosimetry were mainly the result of the nefor proprietary data from equipment manufacturers in order to properly simulate the xsource and the scanner geometry. The four basic pieces of information needed for computational simulations are as follows: 1. X-ray spectrum of source 3. Tube output ments in acrylic phantoms or free in air, or even measurements made using a static tube. Item number one, the x-ray spectrum of the source, can often be found in the

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161 information, spectra are easily generated via TASMIP87. If this information cannot be found, other techniques are available, including Compton spectroscopy. Item number two, fluence map at isocenter, has previously been difficult to determine without information on the shape of the filters used in the scanner, however, new technologiesuch as long OSL strip detectors with very high spatial resolution, provide a waydetermine fluence maps at isocenter, from which the shape of the filters used can be s, to derivede current modulation data, is necessary to assess ation e lems, r ns onsidered, the FOC dosimeter is When calibrated properly, they can be uslso used for point dose measurements as part of the characterization of CT scanners, and possibly the eventual replacement of CTDI. Finally, item number 4, tub doses to patients scanned using tube current modulation. In some cases, the moduldata can be extracted from the raw data generated by the scanner, however, special software tools are sometimes required, which may or may not require assistance from thmanufacturer. The modulation strategies used by the different manufacturers are generally well-known, and rewriting a tube current modulation algorithm that is sufficiently similar to those used in the CT scanner should not pose significant proband these algorithms can be tested using simple asymmetrical phantoms. These foupieces of information, once collected, allow for accurate computational dose simulatiothat require no proprietary data from the manufacturer, allowing dose calculation via computational simulations for all types and models of CT scanners. Even if a move away from physical phantoms is c still an extremely useful tool in diagnostic radiology. ed for tube output measurements to allow for normalization of computational simulations, and perhaps in lieu of OSL to measure fluence maps at isocenter. They ahave the potential to be

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162 Fd to meet the new challenges facing diagnostic with not only cardiac CT, but with traditional ot be known for many years, it is vital that every possibe r e hy, and hope to continue working towards the development of an equally powerful tool for image inally, our group must expan imaging today. These new challenges include, but are not limited to, the extension of CT to more and more interventional cases, and cardiac CT. While the high doses associatedwith cardiac CT are often frowned upon, the whole picture must be examined, including the risks and benefits associated angiography as well. Final Words While the effects, and magnitude of effects, of imaging doses on pediatric patients as they progress through life will n le step be taken to reduce these doses to the lowest point possible without compromising the resultant image quality. Two things must exist for these goals to bachieved: accurate dose assessment tools, and accurate image quality assessment tools. This work has provided the most accurate and technologically advanced dose assessment tool available to date for the measurement of effective and average organ doses at the CTscanner. In addition, the combined use of identical tomographic physical phantoms and computational phantoms provides unlimited potential for dose assessment and the development of dose assessment tools that will one day replace CTDI values on scanneconsoles with effective and organ dose data for the exam selected, and will provide thability to track lifetime radiation doses to individual patients (and the ability to track totaldoses when combined with the same tools in radiation therapy). A very basic imagequality assessment tool for CT has also been presented. Objective, quantitative image quality assessment in CT is still in its infancy, however, and this tool is only the beginning. I look forward to great advances in this aspect of computed tomograp

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163 quality assessment in CT to use in conjunction with tomographic phantom dosimetry systems.

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APPENDIX A TISSUE-EQUIVALENT MATERIALS FOR CONSTRUCTION OF TOMOGRAPHDOSIMETRY PHANTOMS IN PEDIATRIC RADIOLOGY Introduction The search for materials to represent human tissue has been ongoing since Keinbck in 1906 first proposed water as being muscle-equivalent.147 Since that time, refinements and new developments have occurred in tissue-equivalent (TE) materialsuse in both diagnostic and therapeutic radiology.26 These materials play a vital role in activities ranging from specialized dosimetry research to daily quality assurance and radiation treatment planning. While inexpensive and easily obtained materials such as acrylic and aluminum are suitable for quality assurance in diagnostic radiology, they not be suitable for research dosimetry pur IC for may poses, especially at the low energies (<120 keV) used in pediatric radiologyconstruct a series of pediatric r research team has developed tissue-equivalent substitu tissues in o As part of our efforts to computational models and physical phantoms based upon CT imaging data,91 ou tes that are radiographically representative of the soft, skeletal, and lung tissues of the newborn patient. Further refinements to these materials have been made so that they represent these samelder patients (1-year through the adult). Targeted reference tissue compositions for this work were taken as defined by Cristy and Eckerman for the Oak Ridge National Laboratory (ORNL) stylized model series.85 It is noted that more extensive and organ-specific reference elemental compositions have been published such as given in Publication 46 of the International Commission on 164

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165 Radiological Units and Measurements (ICRU).148 However, methods of phantom construction at the University of Florida (UF) are based upon image segmentation and s of the tomogr al. for our newborn stylized dosimetry phantnalose estimates for soft tissue organs ardetermined via internal dosimeter placement within regions constructed of average soft tissue-equivalent materiahermoeuld most likely mspecific (e.g., liver) soft tissue-equivalent substitutes utified. All tissubstitutelopede UF arened to mple to macture, and to yield a rigid yet mable pc when cIn the pesent study,carisons are made to a variety of other materials currently used in quality assurance menicryg.,S1s adstratiotissue-ealency, clations ople exponential attenuation and se-ollisbsorbed at deptperformd at diagnostic energies under narrow-beam geometry for the UF TE substitutes, other TE materials in current use, as conste oxy t the mass density. A more use of phenolic microspheres in tissue substitutes, can be found in papers by White and material differentiation of only the soft tissue, lung tissue, and skeletal tissue regionaphic images. As described previously by Sessions et om,82 inter d e l. Furt re, experim ntal uncertainties in dosim etry wo ake efforts to develop organnjus sue es dev at th desig be si anuf achine lasti ured. r omp easurem ts and med al dosimet studies (e. acrylic, M 1, etc.). A final emon n of quiv alcu f sim inglc ion a dose h are e w ell as the ORNL reference tissues. The long-term objective of our studies is the ruction of a physical tomographic model of the newborn following the CT imagsegmentation previously described by Nipper et al.91 Materials and Methods The tissue-equivalent materials discussed here are all manufactured using an epresin base in which phenolic microspheres are used to adjus complete discussion of epoxy resin systems, their use in tissue substitutes, as well as the

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166 his colleagrepresent a homogeneous mixture osa (bone trabeculae and bone ma. The mns of eisstituteasted to mh valuesass dity, (2) mergy-abtion coefficients, and (3) mass attenuation coefficients for the proper refertissues o diagnostic photon energy range applicable to pediaiologil examinatiSoft Tissue-Equivalent Substitute fo NewboES-NThe sossue-equt subst STES-N develo to be radiographically equivalent to reference newborn soft tissue as defined by Cristy and Eckerman.85STES-NB iufactur using a base of Araldite GY-6010, an epoxy resin, with Jeffamine T-403, a hardener. The proportions used are roughly similar to those originally proposed for use at thapeuticphon enegies y White andcgues,25djustedur resear tissue equivalency at diagnostic energies. Faterials include phylene, silicon dioxide, and magnesium oxide. Phenolic mheres are also incorporated to produce the desired mass density. ned t-tissue counterpart STES-NB, is manucarbonate which are added in proportions needed to match values of mass density, mass ues.23,25 All bone-equivalent materials developed at UF are constructed to of cortical and trabecular spongi rrow) material co positio ach of the t sue sub s was dju atc of (1) m ens ass en sorp ORNL ence ver the tric rad ca ons. r the rn (ST B) ft ti ivalen itute B was ped s man ed er to r b ollea but a in o rch fo iller m olyet icrosp Bone Tissue-Equivalent Substitute for the Newborn (BTES-NB) A bone tissue-equivalent substitute BTES-NB was developed to be radiographically equivalent to reference homogeneous newborn skeletal tissue as defiby Cristy and Eckerman.85 BTES-NB, like its sof factured using a base of Araldite GY-6010 with a Jeffamine T-403 hardener, in proportions similar to those originally proposed by White and colleagues.25 Filler materials for BTES-NB include polyvinyl chloride, silicon dioxide, and calcium

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167 attenuation coefficients, and mass energy-absorption coefficients for ORNL reference one over the diagnostic energy range. newborn b tissue ar-ue justments to filler material propo A lung tissue-equivalent substitute LTES was manufactured to be radiographically equivalent to reference lung tissue as defined by Cristy and Eckerman85 in the diagnostic energy range. In the ORNL model series, the elemental composition and mass density of the lungs are kept constant across all ages, and thus only one reference material is prescribed. As with all the other tissue-equivalent materials, LTES is manufactured using a base of Araldite GY-6010 and Jeffamine T-403. The proportions used were roughly similar to those originally proposed for lung-tissue substitutes for use in the therapeutic photon energy range by White and colleagues,24,25 but adjusted in this study for tissue equivalency at diagnostic energies. In addition to the epoxy resin base, aterials (polyethylene, silicon dioxide, and magnesium oxide) are used to further energy-abs Soft and Bone TE Substitutes for the Child/Adult (STES and BTES) In the ORNL model series, reference soft tissue and homogeneous skeletalare defined differently for all other ages of the model series, beginning with the 1-yeold and continuing through to the adult. Only the newborn model is assigned a uniqsoft tissue and bone elemental composition. Consequently, ad rtions used in STES-NB and BTES-NB were made to create more generic TE substitutes for use in phantom construction in this older age range. In the present study, these TE materials are given the acronyms STES and BTES without the newborn (NB) modifier. Lung Tissue-Equivalent Substitute for the Newborn/Child/Adult (LTES) filler m adjust the mass density, and energy-dependent values of mass attenuation and mass orption coefficients. Phenolic microspheres are incorporated to reduce the

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168 materC r o is is Mechanical mixing is performed with an electric drill using a paint agitator attachment. Soft tissue formulations are mixed three times for five minutes each, and between ixings are placed under vacuum for two minutes to evacuate any trapped air. Bone formulation viscous mixture. Lung formulations are subjected to the same mixing process as the soft tissue formulations (without vacuum), after which the surfactant and foaming agent are added. Additional mechanical mixing distributes these agents. The mixture is then poured into release-treated moulds and allowed to foam undisturbed until cured. Further details regarding phantom construction are given in Chapter 3. Measurement of TE Material Mass Density The UF tissue-equivalent substitutes were first compared to ORNL reference tissues on the basis of mass density. While radiation interaction coefficients for a mixture are simple to calculate and easily adjusted by changing the amount of each ials mass density. Final adjustments to mass density are accomplished via a foaming process that employs both a foaming agent, DC 1107, and a surfactant, D 200/50. This procedure is described in detail by White et al.24 Manufacturing Process A manufacturing process similar to that used by White et al.24,25 is employed fothese tissue substitutes. The ingredients are weighed and added in a specific rder tofacilitate proper mixing of the ingredients as they are combined. First the epoxy resinmeasured, followed by the additions of dry ingredients, phenolic microspheres (if used), and finally the hardener. After all ingredients are added, the mixture is manually stirred until the ingredients have all been incorporated, forming a doughy mixture. Only at thpoint is mechanical mixing begun, in order to minimize loss of dry ingredients. m s are not subjected to a vacuum, as air escapes more easily from this less

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169 a dditive in the mixture, mass densities do not behave as intuitively as do the interaction coefficient the chemical changes that occur during the curing and mixing of the material. Therefore, it was necessary to measure the mass densities of each of the new tissue-equivalent materials. This was done for all tissues with a density greater than that of water (=0.9975-0.9980 g/cm3, depending upon the temperature) using Archimedes Principle, s. A simple weighted average of component densities cannot be taken due to materialwaterB (A-1), buoyancy of that material in water (given by the difference in the materials dry m mwhere m is the mass of the material whose density is being measured and B is the ass, m, n its mass when submerged in water, equal to the mass of the water displaced by the was doe of water was measured in a graduated cylinder and subtramass of values for the ORNL reference tissues in terms of both / and en/as given by the following expressions: a d material when submerged). The density of LTES, being less than any liquid available, etermined in the following manner. A batch of LTES was allowed to cure in a graduated bucket. The LTES took the exact shape of the bucket, but the top of the material was slightly convex. Water was poured on top of the material until it just covered the tp, and that volum cted from the volume measured on the bucket for the LTES. Finally, the the bucket was subtracted from the total of the LTES material and bucket, and divided bythe measured volume to calculate the density of LTES. Comparison of Radiation Interaction Coefficients The UF tissue-equivalent substitutes were first compared to their corresponding

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170 iiTEMaterialiw (A-2) and eneniiTEMaterialiw (A-3), where wi is the mass fraction of element i in the TE substitute or ORNL reference tissue. Elemental values of both (/i and (en/iwere taken from Hubbell and Seltzer149 and Seltzer,150 respectively. As discussed in Attix,151 the weighting factors in Equation (A-2) are more properly expressed as (A-4), where gi is, values of gi are essentially zero in the photon energy range of interest in this study (<150 It is ahite et 24,2525tissue-equivalent) was not considered as it is seldom used in diagnostic radiology except in scatter measurements.152,153 Our calculations also show that it does not match the ORNL reference soft tissues as closely as does acrylic across the energy range of interest (1)iigw the radiation yield fraction for element i in the TE material. Nevertheless k eV) for all elements considered. lso useful to compare the various UF tissue substitutes with existing TE materials in terms of their interaction coefficients over the diagnostic energy range. Several existing tissue substitutes were selected for comparison, including acrylic (also commonly referred to as Plexiglass, Lucite, or PMMA), aluminum, air, MS11, IB1, SB5, and LN 10/75. The latter four materials represent TE substitutes developed by W al. MS11 is a muscle-equivalent material. IB1 is constructed to represent an average mixture of osseous bone trabeculae and red marrow defining the interior spongiosa of cancellous bone (22.4% osseous tissue to 77.6% soft tissue). SB5 is a cortical-bone equivalent material, while LN 10/75 is a lung-equivalent material. Polystyrene (soft

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171 (20 to 80 keV). Copper, a bone-tissue equivalent material, was excluded from the comparisons as it never matches ORNL reference bone tissu e as closely as does aluminum (20 to 80 keV). Acrylic and aluminum were selected for comparison because: (1) they are commonly used in quality assurance measurements performed on diagnostic equipment, including the construction of patient equivalent phantoms (PEP)154,155; (2) they are both frequently used in phantom construction for computed and digital radiography;156-158 and (3) acrylic is the standard material used for QA measurements on CT scanners.28,140,159 Air was chosen as it is frequently used in combination with copper, aluminum, and acrylic for construction of diagnostic chest phantoms.160 Calculations of X-ray Attenuation and Absorbed Dose at Depth Additional calculations were performed to examine the tissue-equivalency of the both (1) simple exponential attenuation of the x-ray fluence rate under narrow-beam geometry, and (2) the single-collision absorbed dose at a depth x = 4 cm in the UF tissue substitutes, the other TE materials discussed previously, and the corresponding ORNL at depth are given as: UF tissue substitutes in predictions of absorbed dose at depth. Estimates were made of reference tissues. Expressions for fluence rate attenuation and single-collision point dose 10 expxi iNE x (A-5) and ,1ienxixEiEDE iN (A-6), where x and 0 are the fluence rate of x-rays at depth x = 4 cm and the surface, th respectively, and Ei is the x-ray energy in the i energy bin of the x-ray energy spectrum.

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172 In the Neonatal Intensive Care Unit (NICU) at Shands Hospital, radiographs for newborn patients are typically performed with a mobile x-ray unit using c omputed radiography (CR) technology. For this comparison, x-ray energy spectra were generated TASMIP ry spectra: the peak tube potential his simple compf-value layer (HVL) and voltage ripple at a tube potential of 66 kVp were made on a General Electric model 46-125686G8 x-ray unit commonly used for pediatric Shands NICU studies. Values of total filtration were evaluated iteratively in a series of Monte Carlo simulations of filtered x-ray fields until predicted values of HVL matched measured HVLs at each kVp setting. A tube potential of 66 kVp yielded x-ray tube characteristics of 25.6% voltage ripple, with a total filtration of 1.05 mm Al. Further experimental details are given in Staton et al.93 Results and Discussion uTableborn 85PE152Cunningham161 as a more representative quantity for photoelectric absorption. During the using the tungsten anode spectral model TASMIP developed by Boone and Sievert.87 equires three parameters to generate energ (kVp), voltage ripple, and total filtration (inherent plus added). For t arison, a tube potential of 66 kVp was selected based upon the total mass of the ORNL newborn model and patient mass-dependent technique factors developed in the Department of Radiology for imaging newborn patients. Measurements of the hal C omparisons of UF Tisse Substitutes to Reference Tissue Compositions A-1 gives the elemental compositions and mass densities of the new tissue substitutes (STES-NB, BTES-NB, LTES) along with their reference elemental compositions and mass densities (ORNL newborn model). At the bottom of Table A-1, two values of effective atomic number are given: (1) Zeff, defined as a mass-weighted average of the elemental atomic numbers, and Zeff defined in Attix and in Johns and

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173 development of the tissue substitutes, it was noted that Zeff and ZeffPE were good predictors of how well the mass attenua tion coefficients and mass-energy absorption coefficients, respectively, of the tissueuld match those of the ORNL referenewborn tissue-equivalent substitutes and their corresponding reference tissue Elemental Composition (% by mass) substitutes wo nce tissues. Table A-1. Elemental composition and effective atomic numbers for both the UF compositions given for the ORNL newborn model.85 Element STES-NB Reference Soft Tissue BTES-NB Reference Bone Tissue LTES Reference Lung Tissue H 7.0 10.625 5.1 7.995 7.0 10.134 Mg Si 0.006 0.179 0.240 314 0.079 8 K 0.301 148 0.194 Ca 0.003 7.8 7.995 0.009 e 0.004 0.008 0.037 0.001 cm1.22 g/cm22 g/cm3 0 g/cm3 0.296 g/cm3 6.77 7.02 8.22 51 3 7.55 10.95 10.84 7 7.69 C 58.1 14.964 46.2 9.708 57.4 10.238 N 2.1 1.681 1.9 2.712 2.1 2.866 O 22.3 71.830 30.2 66.811 22.4 75.752 Na 0.075 0.314 0.184 9.4 0.019 0.143 9.3 0.007 1.0 7.0 1.7 P 3.712 0.0.1400. 0.1 0.080 0.225 0.266 S Cl 0.1 1. F Zn Rb 0.001 Density 1.04 g/cm3 1.04 g/ 3 3 1. 0.3 Zeffa 8. 6.8 7.14 ZeffPE a 7.65 7.7 aiiZ and effi Zw i iiPE3.53.5effiiiiiiiiwZAZaZanawZA, wnd Ai are the mass fraction, atic numbass numy, nt i. Table A-2 gives the corresponding values of elemental composition and mass density for the TE substitutes forn construction of dosimetry phantoms at older patient ages (1-year through the adult). Again, the reference tissues listed in Table A-2 are thosNL model series, exclusive of the newborn. Targeted mass densities were closely matched five TE substitutes. d here wi, Zi, a om er, and m ber, respectivel of eleme use i e used in the OR or all f

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174 Table A-2. Elemental composition and effective atomic numbers for the UF tissuesubstitutesed for phantom construction at ages of-year and older. Reference tissue compositions are taken from the ORNL model series 85 equivalent need 1 at similar ages. Elemental Composition (% by mass) Element STES Reference Soft Tissue BTES Reference Bone Tissue LTES Reference Lung Tissue H 7.2 10.454 4.0 7.337 7.0 10.134 N 2.2 2.490 1.5 3.057 2.1 2.866 O 21.8 63.525 35.3 47.893 22.4 75.752 F 0.025 Na 0.112 0.326 C 59.2 22.663 37.8 25.475 57.4 10.238 0.184 Mg 9.3 0.013 0.112 9.3 0.007 Si P K 0.208 0.153 0.194 Fe 0.005 0.008 0.037 Zr 0.001 Pb 0.001 0.2 0.030 11.9 0.002 1.7 0.006 0.134 5.095 0.080 S 0.204 0.173 0.225 Cl 0.1 0.133 0.1 0.143 0.1 0.266 Ca 0.024 9.4 10.190 0.009 Zn 0.003 0.005 0.001 Rb 0.001 0.002 0.001 Sr 0.003 Density 1.04 g/cm3 1.04 g/cm3 1.40 g/cm3 1.4 g/cm3 0.30 g/cm3 0.296 g/cm3 Zeffa 6.69 6.86 8.80 8.59 6.83 7.14 ZeffPE a 7.53 7.43 11.48 11.36 7.77 7.69 aeffiiiZwZ and iiiPE3.5iwZAectively, of element i. of -2.6% (140-150 keV) for BTES-NB, and by -0.3% (15 keV) to -2.9% (120-150 keV) for 3.5effiiiiiiiZaZandawZA, where wi, Zi, and Ai are the mass fraction, atomic number, and mass number, resp Figure A-1 shows the ratio of /MAC mass attenuation coefficient) for thevarious newborn TE substitutes to the corresponding values for the ORNL reference tissues at photon energies from 10 to 150 keV. Values of /are noted to differ by -0.8% (15 keV) to 3.2% (110-150 keV) their reference values across this energy range for STES-NB, by -0.1% (15-20 keV) to

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175 Photon Energy (keV) 020406080100120140160 Ratio of MAC (TE Substitute / Referenc 0.9650.9700.9750.9800.9850.9900.991.000 1.005 e) 5 STES-NB BTES-NB LTES Figure A-1. Ratios of mass attenuation coefficients (MAC or /) for the STES-NB, BTES-NB, and LTES tissue substitutes to their corresponding reference values (ORNL newborn model) as a function of photon energy. LTES. It can be seen that all /values underestimate reference values. This is because more emphasis was placed on first matching the mass density and mass energy-absorption coefficient values of the tissue substitutes to those of the corresponding reference tissues. Corresponding ratios of en/ (MEAC mass energy-absorption coefficient) are given in Figure A-2 for these same materials. Values of en/ vary from +1.0% (30 keV) to .2% (150 keV) of their reference values across this energy range for STES-NB, from +0.8% (40 keV) to -2.2% (150 keV) for BTES-NB, and from +1.7% (30 keV) to -2.7% (140-150 keV) for LTES. Also note that in the energy range where a majority of photons are produced within diagnostic x-ray beams (20-80 keV), values of en/ for the three newborn tissue substitutes vary less than 1.7% from their reference values.

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176 Photon Energy (keV) 020406080160 100120140 Ratio of MEAC (TE Substitute / Reference) 0.960.970.980.991.001.011.02 STES-NB BTES-NB LTES ues over the energy range of 10 to 150 keV. Values of /for STES are noted to vary from +0.4% (15 keV) to .0% (100-150 keV) of their reference values for STES, from +2.4% (10 keV) to -2.7% (130-150 keV) for BTES, and from -0.3% (15 keV) to -2.9% (120-150 keV) for LTES. Corresponding ratios of en/ for these same materials are given in Figure A-4. Values of en/ for STES vary from +2.0% (30 keV) to .9% (150 keV) of their reference values, from +2.9% (10 keV) to -2.5% (150 keV) for BTES, and from +1.7% (30 keV) to -2.7% (140-150 keV) for LTES. In the primary energy range of interest in diagnostic imaging (20 to 80 keV), values of en/ for the tissue substitutes needed for phantom construction at these older ages vary less than 2-3% of the ORNL reference values. Figure A-2. Ratios of mass energy-absorption coefficients (MEAC or en/) for the STES-NB, BTES-NB, and LTES tissue substitutes to their corresponding reference values (ORNL newborn model) as a function of photon energy. Figure A-3 plots the ratio of / for STES, BTES, and LTES to their corresponding values for the ORNL reference tiss

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177 Photon Energy (keV) 020406080100120140160 Ratio of MAC (TE Substitute / Reference) 0.960.970.980.991.001.011.021.03 STES BTES LTES Figure A-3. Ratios of mass attenuation coefficients (MAC) for the STES, BTES, and LTES tissue substitutes to their corresponding reference values (ORNL child/adult models) as a function of photon energy. Photon Energy (keV) 020406080100120140160 Ratio of MEAC (TE Substitute / Reference) 0.960.970.980.991.001.011.021.031.04 STES BTES LTES Figure A-4. Ratios of mass energy-absorption coefficients (MEAC) for the STES, BTES, and LTES tissue substitutes to their corresponding reference values (ORNL child/adult models) as a function of photon energy. Comparison of UF Tissue Substitutes to Other TE Materials Figure A-5 plots the ratio of both / and en/for STES-NB and acrylic to the corresponding ORNL newborn reference tissues85 as a function of photon energy from 10

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178 to 150 keV. STES-NB is not compared to MS11 because the tissue substitutes developed by White et al. were not designed to simulate newborn tissues.25 Acrylic is shown to approach the tissue equivalency of STES-NB only at energies exceeding 80 keV in terms of /and at energies exceeding 130-140 keV in terms of en/ Photon Energy (keV) 020406080100120140160 Ratio of MAC and MEAC (TE Substitute / Reference) 0.50.60.70.80.91.01.1 STES-NB (MAC) Acrylic (MAC) STES-NB (MEAC) Acrylic (MEAC) Figure A-5. Ratios of both / and en/ for STES-NB and acrylic to their corresponding reference values (ORNL newborn model) as a function of photon energy. Figure A-6 gives a similar comparison between BTES-NB and aluminum over the same energy range. Comparable conclusions are drawn in that aluminum approaches the tissue equivalency of BTES-NB only at energies exceeding 80 and 130-140 keV in terms of /and en/respectively. Figure A-7 shows data demonstrating the tissue equivalency of LTES in comparison both to Whites LN 10/75 lung substitute24 and to air. Air is shown to have interaction coefficients that are -6% to -10% of the ORNL reference lung tissue. At the higher energies (> 100 keV), the agreement with reference lung tissue is slightly better for LN 10/75 (ratios of ~0.98) than for LTES (ratios of ~0.97). Values of en/ for

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179 Photon Energy (keV) 020406080100120140160 Ratio of MAC and MEAC (TE Substitute / Reference) 0.81.01.21.41.61.82.02.2 BTES-NB (MAC) Al (MAC) BTES-NB (MEAC) Al (MEAC) Figure A-6. Ratios of both / and en/ for BTES-NB and aluminum to their corresponding reference values (ORNL newborn model) as a function of photon energy. Photon Energy (keV) 020406080100120140160 Ratio of MAC and MEAC (TE Substitute / Reference) 0.900.920.940.960.981.001.021.04 LTES (MAC) LN 10/75 (MAC) Air (MAC) LTES (MEAC) LN 10/75 (MEAC) Air (MEAC) Figure A-7. Ratios of both / and en/ for LTES, LN10/75, and air to their corresponding reference values (ORNL newborn and child/adult models) as a function of photon energy. LTES, however, begin to exceed those ORNL reference lung tissue at lower energies (<60 keV), and thus a weighting of en/for LTES over a typical diagnostic energy

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180 spectrum would potentially show improved lung tissue-equivalency than seen for LN 10/75 in construction of either newborn or child/adult dosimetry phantoms. In Figure A-8, we compare the tissue equivalency of STES both with Whites muscle-equivalent substitute MS11 and with acrylic. While all materials show essentially equivalent agreement with interaction coefficients for reference child/adult soft tissue at energies exceeding ~100 keV, acrylic is shown to continually underestimate values of / and en/ at lower and lower photon energies. Values of / for both STES and MS11, relative to the ORNL reference soft tissue for the child/adult, are reasonably comparable at all photon energies. However, the agreement in values of en/are improved for STES over MS11 at energies below 100 keV. Photon Energy (keV) 020406080100120140160 Ratio of MAC and MEAC (TE Substitute / Reference) 0.60.70.80.91.01.11.2 STES (MAC) MS11 (MAC) Acrylic (MAC) STES (MEAC) MS11 (MEAC) Acrylic (MEAC) Figure A-8. Ratios of both /and en/ for STES, MS11, and acrylic to their corresponding reference values (ORNL child/adult models) as a function of photon energy. A final series of comparisons for bone-equivalent materials are given in Figures A-9 and A-10 for / and en/respectively. In addition to BTES, comparisons to ORNL reference bone tissue (child/adult) are given for Whites spongiosa substitute

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181 (IB1), Whites cortical bone substitute (SB5), and aluminum. Another set of plots is given in which the curves for IB1 and SB5 are weighted in proportions representative of cortical and trabecular bone in Reference Man.162,163 As shown in Figure A-9, agreement with reference bone tissue in values of /are shown to be comparable for all materials at photon energies of 100 keV and higher. The data of Figure A-10 indicate that similar agreement in terms of reference values of en/are not seen until the photon energies exceed perhaps 140 keV. Values of both /and en/for BTES are shown to closely match (within a few percent) those for the ORNL reference bone tissue for the child/adult across the full energy range of interest in pediatric radiology. Photon Energy (keV) 020406080100120140160 Ratio of MAC (TE Substitute / Reference) 0.60.81.01.21.41.61.82.0 BTES (MAC) IB1 Spongiosa (MAC) SB5 Cortical Bone (MAC) 20% IB1 80% SB5 (MAC) Al (MAC) Figure A-9. Ratios of / for BTES, IB1, SB5, weighted combination of IB1 and SB5, and aluminum to their corresponding reference values (ORNL child/adult models) as a function of photon energy. Calculations of X-ray Attenuation and Absorbed Dose at Depth Table A-3 gives results for both x-ray attenuation and point absorbed dose at 4 cm depth for each of the five reference tissues and other TE materials discussed previously. Percent differences are shown for each value relative to those determined for the

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182 Photon Energy (keV) 020406080100120140160 Ratio of MEAC (TE Substitute / Reference) 0.40.60.81.01.21.41.61.82.02.22.4 BTES (MEAC) IB1 Spongiosa (MEAC) SB5 Cortical Bone (MEAC) 20% IB1 80% SB5 (MAC) Al (MEAC) Figure A-10. Ratios of en/ for BTES, IB1, SB5, weighted combination of IB1 and SB5, and aluminum to their corresponding reference values (ORNL child/adult models) as a function of photon energy. corresponding ORNL reference tissues. Percent differences in absorbed dose are shown to be +3.6% for both the newborn and child/adult soft tissue-equivalent substitutes STES-NB and STES. Similarly, dosimetry errors under these idealistic conditions are noted to be slightly more than 3% of reference values for newborn skeletal tissue and -4.4% for child/adult skeletal tissue. Absorbed doses differ only slightly more than 1% for lung tissue (both newborn and child/adult). Conclusions Five tissue-equivalent substitutes are presented for use the in the construction of tomographic, or image-based, phantoms for use organ dose assessment in pediatric radiology.82,91 STES-NB, BTES-NB, and LTES are described and characterized as materials radiographically mimicking the soft tissue, skeletal tissues, and lung tissues of the newborn patient. In their development, the elemental compositions given by Cristy and Eckerman85 for the ORNL newborn stylized computational model are used as a

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183 Table A-3. Results for calculations of narrow-beam photon fluence-rate attenuation (66 kVp energy spectrum) through 4 cm of tissue-equivalent material and the resulting single-collision absorbed dose delivered at that depth. Tissue-Equivalent Material Percent Reduction in Photon Fluence Rate (%) % Difference from Reference Tissue Absorbed Dose at 4 cm depth (Gy) % Difference from Reference Tissue Soft Tissue STES-NB 27.7 +2.6% 0.145 +3.6% Acrylic 27.8 +3.0% 0.098 -30.0% Reference Soft Tissue (Newborn) 27.0 0.140 STES 28.1 +1.4% 0.143 +3.6% MS11 28.4 +2.5% 0.161 +16.7% Reference Soft Tissue (Child/Adult) 27.7 0.138 Bone Tissue BTES-NB 10.8 +1.9% 0.130 +3.2% Aluminum 0.6 -94.3% 0.008 -93.7% Reference Bone Tissue (Newborn) 10.6 0.126 BTES 6.6 -5.7% 0.086 -4.4% IB1 15.0 +114% 0.347 +285% SB5 1.3 -81.4% 0.025 -72.2% 20% IB1 + 80% SB5 2.1 -70.0% 0.037 -58.9% Reference Bone Tissue (Child/Adult) 7.0 0.090 Lung Tissue LTES 67.0 +0.1% 0.420 +1.2% Air 99.8 +49.2% 0.631 +52.0% LN 75/100 66.4 -0.7% 0.408 -1.7% Reference Lung Tissue 66.9 0.415 reference standard for their manufacture. In the ORNL model series, no changes are noted for lung tissues between the newborn and the models of older individuals (1-year-old through adult), and thus only a single lung-equivalent material has been developed. Additionally, STES and BTES are described and characterized as matching the soft tissue and skeletal tissues of the child (1-year to 15-year) and adult. Values of /for the newborn tissue substitutes STES-NB, BTES-NB, and LTES are noted to underestimate their values for the reference tissues by 3.2%, 2.2%, and 2.7%,

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184 respectively at photon energies exceeding 80 keV (see Figure A-1). As the photon energy decreases, the agreement improves to within 1-2% at 10-20 keV. Values of en/ for these same materials are shown to overestimate their values for reference tissues between 20 and 60-70 keV by 1-2%, and then underestimate their values for reference tissues at higher energies (see Figure A-2). At 120 keV, for example, STES-NB, BTES-NB, and LTES are noted to underestimate values of en/for reference tissues by 2.9%, 1.8%, and 2.5%, respectively. As shown in Figures A-3 and A-4, comparable comparisons are noted for the tissue substitutes STES and BTES. Estimates of point absorbed dose at 4-cm depth are given in Table A-3 which indicate dosimetry errors from reference tissues of between 3-4% for STES-NB, STES, and BTES-NB, slightly higher for BTES (-4.4%), and slightly above 1% for LTES. The good agreement seen for STES-NB, BTES-NB, and LTES in their comparisons to reference newborn tissues has allowed us to proceed with the construction of physical tomographic model of the newborn following the CT image segmentation previously described by Nipper et al.91

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185 APPENDIX B BITMAP IMAGES OF THE 5 MM SLICES USED AS TEMPLATES FOR PHANTOM CONSTRUCTION This appendix contains the bitmap images of each resampled 5 mm slice, which were used to print the templates used to construct the individual phantom slices. The images are ordered from the top of the head through the legs, so Page 1 will contain slice 1, Page 2 will contain slice 2, and so on through slice 84 on Page 84. 185

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APPENDIX C This appendix contains the MCNP code used to perform the simulations for the cylindrical phantom depth-dose comparisons, as described previously in Chapter 2. Code is included for three phantom configurations, and for the simulated ion chamber calculations used in conjunction with exposure measurements at the x-ray source for calibrating the simulations. MCNP CODE FOR CYLINDRICAL PHANTOM DEPTH-DOSE COMPARISONS 269

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270 MCNP Code for Homogeneous Soft Tissue Phantom C CELL CARDSC # Material, Density, GeometryC (-) density=g/cm3, (+) density=atoms/cm3C **********Tissue Layers**********1 3 -1.04 -9 -1 2 20 21 22 23 24 25 2 3 -1.04 -9 -2 3 23 24 25 26 27 28 3 4 -1.04 -9 -3 4 26 27 28 29 30 31 4 4 -1.04 -9 -4 5 29 30 31 32 33 34 5 3 -1.04 -9 -5 6 32 33 34 35 36 37 6 3 -1.04 -9 -6 7 35 36 37 38 39 407 3 -1.04 -9 -7 8 38 39 40 C **********MOSFETs***************100 3 -1.04 -20 200 3 -1.04 -21300 3 -1.04 -22101 3 -1.04 -23201 3 -1.04 -24301 3 -1.04 -25102 3 -1.04 -26202 3 -1.04 -27302 3 -1.04 -28103 4 -1.04 -29203 4 -1.04 -30303 4 -1.04 -31104 4 -1.04 -32 2 04 4 -1.04 -33205 3 -1305 3 106 206 3 -1.04 -39501 0 -66 -64 65 (-60:61:-62:63:-65:64)C *********Cylinder Boundary********00 2 -0.001205 -76 77 -78 (9:1:-8) #501 C SURFAC45****1*********2*********3*********4*********5*********6*********7*********8 **********Tissue Layers**********2 z -534 -75 -86 -97 -108 -119 2.54C**MOSFETs****C x r2-1.270 0. 304 4 -1.04 -34105 3 -1.04 -35.04 -36 -1.04 -37 3 -1.04 -38306 3 -1.04 -40C *********Collimators************* 6 601 0 (76:78:-77) E CARDS C 3C 1 pz -4 p pz -6 pz pz pz pz pz cz ******** *********** y z 0 s 0 -4.2 20

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271 21 s 0.00 0 -4.2 0 0.22 s 1.270 0.23 s -1.270 0.24 s 0.000 0.25 s 1.270 0.26 s -1.270 0.27 s 0.000 0.28 s 1.270 0.33 s 0.00 0 -8.20 0.20.20.2036 s 0.37 38 s -1.27 0 -10.20 0.2061 px 0.49266 cz 9.99C -1, Cos of angle, 1 (angle is 10deg)C Mev Probability (Room 1 at 66kv, 2.35 mm filt., 0.000 0.000000e+000 0.003 0.000000e+000 0.008 0.000000e+000 0.010 2.042302e-007 0.011 7.112777e-005 0.013 8.432046e-002 0.014 1.032475e+000 20 0 -4.2 20 0 -5.2 20 0 -5.2 20 0 -5.2 20 0 -6.2 20 0 -6.2 20 0 -6.2 0 -7.2 2020 29 s -1.270 0.30 s 0.00 0 -7.20 0.20 31 32 s -1.27 0 -8.20 0.2034 s 1.27 0 -8.20 035 s -1.27 0 -9.20 0 s 1.27 0 -7.20 0.20 00 0 -9.20 0.20 s 1.27 0 -9.20 0.2039 s 0.00 0 -10.20 0.2040 s 1.27 0 -10.20 0.20C *****Collimators*****************60 px -0.49262 py -0.49263 py 0.49264 pz 81.665 pz 80.6C *****cylinder encircling world***76 cz 1077 pz -15.078 pz 122SDEF DIR d1 POS 0 0 90.6 ERG d2 PAR 2 Vec 0 0 -1PTRAC cell=1, 2, 3, 4, 5, 6, 7 file asc nps 1,100000 type=pmode p eSi1 -1 0.984807753 1C 0, (1+costheta)/2, (1-costheta)/2Sp1 0 0 1# Si2 Sp2 5% C ripple) 0.001 0.000000e+000 0.002 0.000000e+000 0.004 0.000000e+000 0.005 0.000000e+000 0.006 0.000000e+000 0.007 0.000000e+000 0.009 0.000000e+000

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272 0.015 7.973585e+000 0.016 3.672567e+001 0.017 1.363344e+002 0.018 3.692806e+002 0.019 7.104789e+002 0 0.021 2.086428e+003 0.024 5.863582e+003 0.026 8.850591e+003 0.031 1.496385e+004 0.036 1.722739e+004.727420e+004.721612e+004 0.041 0.042 0.043 1 0.046 0.047 0.048 0.049 0.050 0.051 0.052 0.053 9.215779e+003 0.054 8.493984e+003 0.055 7.711229e+003 0.056 6.925663e+003 0.057 5.868695e+003 0.058 4.924823e+003 0.059 4.122944e+003 0.060 3.304443e+003 0.061 3.087121e+003 0.062 2.630850e+003 0.063 1.974043e+003 0.064 1.291109e+003 0.065 7.086478e+002 0.066 2.108752e+002 0.067 0.000000e+000 0.068 0.000000e+000 0.069 0.000000e+000 .020 1.338483e+003 0.022 3.217453e+003 0.023 4.359927e+003 0.025 7.396869e+003 0.027 1.038068e+004 0.028 1.198573e+004 0.029 1.303842e+004 0.030 1.418171e+004 0.032 1.583402e+004 0.033 1.628377e+004 0.034 1.673543e+004 0.035 1.710240e+004 0.037 1 0.038 1 0.039 1.703716e+004 0.040 1.680604e+0041.648014e+0041.612140e+004.543398e+004 0.044 1.475023e+004 0.045 1.440044e+0041.393275e+0041.316787e+0041.238623e+0041.187232e+0041.131932e+0041.068361e+0049.911334e+003

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273 C Talley CardsC Talley for Absorbed Dose in Mev/g*F8:p,e 100 200 300 101 201 301 102 202 302 103 203 303 104 204 304 105 205 305 106 206 306C Material CardsC 345****1*********2*********3*********4*** ******5*********6** *******7** *******8mic number000, (+) atomic fraction or (-) weight fractionEAD C AtoM1 82000 -1 $LM2 M3 -.223078 7000 -0.755 8000 -0.232 18000 -0.013 $DRY AIR 1000 -.070344 6000 -.5813825 7000 -.021158 8000 12000 -.0934805 14000 -.00935 17000 -.001207 $STES-NBnps 100000000

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274 MCNP Code for Heterogeneous Soft and Bone Tissue Phantom C CELL CARDSC # Material, Density, Geometry C C *****1 3 -1.04 -9 -1 2 20 21 22 23 24 25 4 4 -1.30 -9 -4 5 29 30 31 32 33 34 5 3 -1.04 -9 -5 6 32 33 34 35 36 37 6 3 -1.04 -9 -6 7 35 36 37 38 39 407 3 -1.04 -9 -7 8 38 39 40 C **********MOSFETs***************100 3 -1.04 -20 200 3 -1.04 -21300 3 -1.04 -22101 3 -1.04 -23201 3 -1.04 -24301 3 -1.04 -25102 3 -1.04 -26202 3 -1.04 -27302 3 -1.04 -28103 4 -1.30 -29203 4 -1.30 -30303 4 -1.30 -31104 4 -1.30 -32204 4 -1.30 -33304 4 -1.30 -34105 3 -1.04 -35205 3 -1.04 -36305 3 -1.04 -37106 3 -1.04 -38206 3 -1.04 -39306 3 -1.04 -40C *********Collimators*************501 0 -66 -64 65 (-60:61:-62:63:-65:64)C *********Cylinder Boundary********600 2 -0.001205 -76 77 -78 (9:1:-8) #501 601 0 (76:78:-77) C SURFACE CARDSC 345****1*********2*********3*********4*********5*********6*********7*********8C **********Tissue Layers**********1 pz -42 pz -53 pz -64 pz -75 pz -86 pz -97 pz -108 pz -119 cz 2.54C **********MOSFETs***************C x y z r20 s -1.27 0 -4.20 0.20 (-) density=g/cm3, (+) density=atoms/cm3*****Tissue Layers**********2 3 -1.04 -9 -2 3 23 24 25 26 27 28 3 4 -1.30 -9 -3 4 26 27 28 29 30 31

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275 21 s 0.00 0 -4.20 0.2022 s 1.27 0 -4.20 0.20 23 s -1.27 0 -5.20 0.20.00 0 -5.20 0.20.27 0 -5.20 0.20s -1.27 0 -8.20 0.20 s 0.00 0 -8.20 0.20 0 -8.20 0.20 s -1.27 0 -9.20 0.20circling world*** pz -15.0 pz 1220 0 -1C cell=1, 2, 3, 4, 5, 6, 7 10deg)5% ripple) 0.001 0.000000e+000 0.006 0.000000e+000e+000 24 s 02 s 1 526 s -1.27 0 -6.20 0.2027 s 0.00 0 -6.20 0.20 28 s 1.27 0 -6.20 0.20 29 s -1.27 0 -7.20 0.20 30 s 0.00 0 -7.20 0.20 31 s 1.27 0 -7.20 0.20 32 33 34 s 1.27 35 36 s 0.00 0 -9.20 0.20 37 s 1.27 0 -9.20 0.20 38 s -1.27 0 -10.20 0.20 0 -10.20 0.20 39 s 0.00 40 s 1.27 0 -10.20 0.20C *****Collimators***************** 60 px -0.49261 px 0.492 62 py -0.492 63 py 0.492 64 pz 81.6 65 pz 80.6 66 cz 9.99 C *****cylinder en 76 cz 10 77 78 SDEFRA DIR d1 POS 0 0 90.6 ERG d2 PAR 2 Vec PT file asc nps 1,100000 type=p mode p e(angle is C -1, Cos of angle, 1 Si1 -1 0.984807753 1C 0, (1+costheta)/2, (1 -costheta)/2Sp1 0 0 1 # Si2 Sp2 C Mev Pr obability (Room 1 at 66kv, 2.35 mm filt., 0.000 0.000000e+000 0.002 0.000000e+000 0.003 0.000000e+000 0.004 0.000000e+000 0.005 0.000000e+000 0.007 0.000000e+000000 0.008 0.000000e+ 0.009 0.000000e+000 0.010 2.042302e-007 0.011 7.112777e-005 0.013 8.432046e-002 0.014 1.032475

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276 0.015 7.973585e+0001218 3.692806e+00219 7.104789e+002e+003e+003 0.022 3.217453e+0033 0.024 5.863582e+003 0.031 1.496385e+004 1.583402e+004 0.033 1.628377e+004 0.039 1.703716e+004 0.041 1.648014e+00442 1.612140e+004 0.043 1.543398e+0043e+0044e+004004004 0.048 1.238623e+004 0.050 1.131932e+0043 7.711229e+003 6.925663e+003 0.057 5.868695e+003 0.059 4.122944e+003 7.086478e+00266 2.108752e+002 0.067 0.000000e+000.068 0.000000e+000 0.016 3.672567e+00 0.017 1.363344e+00 0.0 0.0 0.020 1.338483 0.021 2.086428 0.023 4.359927e+00 0.025 7.396869e+003 0.026 8.850591e+003 0.027 1.038068e+004 0.028 1.198573e+004 0.029 1.303842e+004 0.030 1.418171e+004 0.032 0.034 1.673543e+004 0.035 1.710240e+004 0.036 1.722739e+004 0.037 1.727420e+004 0.038 1.721612e+004 0.040 1.680604e+004 0.0 0.044 1.47502 0.045 1.44004 0.046 1.393275e+ 0.047 1.316787e+ 0.049 1.187232e+004 0.051 1.068361e+004 0.052 9.911334e+00 0.053 9.215779e+003 0.054 8.493984e+003 0.055 0.056 0.058 4.924823e+003 0.060 3.304443e+003 0.061 3.087121e+003 0.062 2.630850e+003 0.063 1.974043e+003 0.064 1.291109e+003 0.065 0.0 0 0.069 0.000000e+000

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277 C Tall ey Cardse 100 200 300 101 201 301 102 202 302 103 203 303 104 204 30406 206 306Material Cards****4*********5*********6*********7*********8 $DRY AIR0 -.021158 8000 -.223078 17000 -.001207 $STES-NB 7000 -.018625 8000 -.301950620000 -.07808635 $BTES-NB C Talley for Absorbed Dose in Mev/g *F8:p, 105 205 305 1 C C 345****1*********2*********3***** C Atomic number000, (+) atomic fraction or (-) weight fraction $LEAD M1 82000 -1 2 7000 -0.755 8000 -0.232 18000 -0.013 MM 3 1000 -.070344 6000 -.5813825 700 12000 -.0934805 14000 -.00935 M4 1000 -.0508267 600 0 -.4623076 14000 -.070125 17000 -.0180815 nps 100000000

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278 MCNP Code for Heterogeneous Soft, Bone, and Lung Tissue Phantom C CELL CARDS C # Material, Density, Geometry -1.30 -9 -2 3 23 24 25 26 27 28 -1.04 -9 -6 7 35 36 37 38 39 40 -9 -7 8 38 39 40 FETs*************** -20 2 4 -1.30 -27 4 -1.30 -36 (76:78:-77) RDS345****1*********2*********3*********4*********5*********6*********7*********8 C (-) density=g/cm3, (+) density=atoms/cm3C **********Tissue Layers********** 1 3 -1.04 -9 -1 2 20 21 22 23 24 25 2 4 3 5 -0.30 -9 -3 4 26 27 28 29 30 31 4 5 -0.30 -9 -4 5 29 30 31 32 33 34 5 4 -1.30 -9 -5 6 32 33 34 35 36 37 6 3 3 -1.04 7 C **********MOS10 3 -1.04 020 0 3 -1.04 -21300 3 -1.04 -22 101 3 -1.04 -23 201 3 -1.04 -24 301 3 -1.04 -25 102 4 -1.30 -26 20 302 4 -1.30 -28103 5 -0.30 -29 203 5 -0.30 -30303 5 -0.30 -31 104 5 -0.30 -32204 5 -0.30 -33 304 5 -0.30 -34105 4 -1.30 -355 20 305 4 -1.30 -37106 3 -1.04 -38 206 3 -1.04 -39306 3 -1.04 -40C *********Co llimators*************501 0 -66 -64 65 (-60:61:-62:63:-65:64)C *********Cylinder Boundary******** 600 2 -0.001205 -76 77 -78 (9:1:-8) #501 601 0 C SURFACE CA C C **********Tissue Layers********** 1 pz -4 2 pz -5 3 pz -6 4 pz -7 pz -8 5 6 pz -97 pz -108 pz -11 9 cz 2.54C ***** *****MOSFETs***************C x y z r20 s -1.27 0 -4.20 0.20

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279 21 s 0.00 0 -4.20 0.2022 s 1.27 0 -4.20 0.20 s -1.27 0 -5.20 0.207 0 -8.20 0.20 -8.20 0.20 -8.20 0.20 s -1.27 0 -9.20 0.20*****Collimators***************** cz 10 pz -15.0, 2, 3, 4, 5, 6, 74807753 1 0, (1+costheta)/2, (1-costheta)/2 0.005 0.000000e+0000.009 0.000000e+0000.014 1.032475e+000 23 24 s 0.00 0 -5.20 0.2025 s 1.27 0 -5.20 0.20 26 s -1.27 0 -6.20 0.2027 s 0.00 0 -6.20 0.20 28 s 1.27 0 -6.20 0.2029 s -1.27 0 -7.20 0.20 30 s 0.00 0 -7.20 0.2031 s 1.27 0 -7.20 0.203 s -1.2 233 s 0.00 034 s 1.27 0 35 36 s 0.00 0 -9.20 0.20 37 s 1.27 0 -9.20 0.20 38 s -1.27 0 -10.20 0.20 39 s 0.00 0 -10.20 0.20 40 s 1.27 0 -10.20 0.20 C 60 px -0.49261 px 0.492 62 py -0.49263 py 0.492 64 pz 81.665 pz 80.6 66 cz 9.99C *****cylinder encircling world***7 677 78 pz 122 SDEF DIR d1 POS 0 0 90.6 ERG d2 PAR 2 Vec 0 0 -1PTRAC cell=1 file asc nps 1,100000 type=pmode p e C -1, Cos of angle, 1 (angle is 10deg)Si1 -1 0.98 C Sp1 0 0 1 Si2 Sp2 # C Mev Probability (Room 1 at 66kv, 2.35 mm filt., 5% ripple) 0.000 0.000000e+000 0.001 0.000000e+000 0.002 0.000000e+000 0.003 0.000000e+000 0.004 0.000000e+000 0.006 0.000000e+000 0.007 0.000000e+000 0.008 0.000000e+000 0.010 2.042302e-007 0.011 7.112777e-005 0.013 8.432046e-002

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280 0.015 7.973585e+000.863582e+003303 0.027 1.038068e+004 0.033 1.628377e+0040.042 1.612140e+004.316787e+004.068361e+004 0.052 9.911334e+003033 0.061 3.087121e+0030.065 7.086478e+0020.069 0.000000e+000 0.016 3.672567e+001 0.017 1.363344e+002 0.018 3.692806e+002 0.019 7.104789e+002 0.020 1.338483e+003 0.021 2.086428e+003 0.022 3.217453e+003 0.023 4.359927e+003 0.024 5 0.025 7.396869e+00 0.026 8.850591e+0 0.028 1.198573e+004 0.029 1.303842e+004 0.030 1.418171e+004 0.031 1.496385e+004 0.032 1.583402e+004 0.034 1.673543e+004 0.035 1.710240e+004 0.036 1.722739e+004 0.037 1.727420e+004 0.038 1.721612e+004 0.039 1.703716e+004 0.040 1.680604e+004 0.041 1.648014e+004 0.043 1.543398e+004 0.044 1.475023e+004 0.045 1.440044e+004 0.046 1.393275e+004 0.047 1 0.048 1.238623e+004 0.049 1.187232e+004 0.050 1.131932e+004 0.051 1 0.053 9.215779e+0 0.054 8.493984e+00 0.055 7.711229e+003 0.056 6.925663e+003 0.057 5.868695e+003 0.058 4.924823e+003 0.059 4.122944e+003 0.060 3.304443e+003 0.062 2.630850e+003 0.063 1.974043e+003 0.064 1.291109e+003 0.066 2.108752e+002 0.067 0.000000e+000 0.068 0.000000e+000

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281 C Talley CardsC Talley for Absorbed Dose in Mev/g *F8:p,e 100 200 300 101 201 301 102 202 302 103 203 303 104 204 304 105 2 05 305 106 206 306*******6*********7*********8-0.755 8000 -0.232 18000 -0.013 $DRY AIR344 6000 -.5813825 7000 -.021158 8000 -.2230785 14000 -.00935 17000 -.001207 $STES-NB C Material CardsC 345****1*********2*********3*********4*********5** C Atomic number000, (+) atomic fraction or (-) weight fractionM1 82000 -1 $LEAD2 7000 M M3 1000 -.070 12000 -.093480 M 4 1000 -.0508267 6000 -.4623076 7000 -.018625 8000 -.3019506 14000 -.070125 17000 -.0180815 20000 -.07808635 $BTES-NB M5 1000 -.070303 6000 -.5739087 7000 -.02060968 8000 -.2235369 12000 -.0934805 14000 -.01698552 17000 -.00117572 $LTES nps 100000000

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282 MCNP Code for Ionization Chamber Simulation C 345****1*********2*********3*********4*********5*********6*********7*********8C CELL CARDSC # M, D ensity, Geometry (-) dens=g/cm3, (+) density=atoms/cm3tors************* 1 -11.34 -46 -44 45 (-40:41:-42:43:44:-45) pz 29.22/z 0 0 3.175py -0.492 py 0.492 cz 10P:P 1 2 2 1 1 0 C ityC *********Collima 2 C *********Ion Chambers************* 3 2 -00.001205 -38 2 -3 4 3 -1.17 -39 1 -4 #3 C **********Table****************** 30 4 -2.7 -50 54 -55*********cylinder boundry******** C 40 2 -00.001205 -66 67 -68 #2 #3 #4 #3041 0 66 : 68 : -67 C SURFACE CARDSC 345****1*********2*********3*********4*********5*********6*********7*********8C ****Ion Chambers***************** 1 pz 29.18 2 3 pz 30.444 pz 30.48 38 c/z 0.71 0 1.9839 c c *****Collimators*****************40 px -0.492 41 px 0.49242 43 44 pz 91.6 pz 90.6 45 46 cz 9.99 C *****Table***********************50 cz 9.9 54 pZ -6.55 55 pz 0.00 C *****cylinder encircling world*** 66 67 pz -7.068 pz 102 SDEF DIR d1 POS 0 0 101.6 ERG d2 PAR 2 Vec 0 0 -1 $ 40 inches SIDPTRAC cell=2,3,4,30 file asc nps 1,1100 type=p IM mode pC -1, Cos of angle, 1 (angle is 10deg) Si1 -1 0.984807753 1C 0, (1+costheta)/2, (1-costheta)/2 Sp1 0 0 1# Si2 Sp2 C Mev Probability (Room 1 at 66kv, 2.35 mm filt., 5% ripple)

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283 0.000 0.0000 00e+000 0.001 0.000000e+000 0.016 3.672567e+001020 1.338483e+003 7.396869e+003 8.850591e+003 0.036 1.722739e+004 9.911334e+003 0.053 9.215779e+00354 8.493984e+003 0.002 0.000000e+000 0.003 0.000000e+000 0.004 0.000000e+000 0.005 0.000000e+000 0.006 0.000000e+000 0.007 0.000000e+000 0.008 0.000000e+000 0.009 0.000000e+000 0.010 2.042302e-007 0.011 7.112777e-005 0.013 8.432046e-002 0.014 1.032475e+000 0.015 7.973585e+000 0.017 1.363344e+002 0.018 3.692806e+002 0.019 7.104789e+002 0. 0.021 2.086428e+003 0.022 3.217453e+003 0.023 4.359927e+003 0.024 5.863582e+003 0.025 0.026 0.027 1.038068e+004 0.028 1.198573e+004 0.029 1.303842e+004 0.030 1.418171e+004 0.031 1.496385e+004 0.032 1.583402e+004 0.033 1.628377e+004 0.034 1.673543e+004 0.035 1.710240e+004 0.037 1.727420e+004 0.038 1.721612e+004 0.039 1.703716e+004 0.040 1.680604e+004 0.041 1.648014e+004 0.042 1.612140e+004 0.043 1.543398e+004 0.044 1.475023e+004 0.045 1.440044e+004 0.046 1.393275e+004 0.047 1.316787e+004 0.048 1.238623e+004 0.049 1.187232e+004 0.050 1.131932e+004 0.051 1.068361e+004 0.052 0.0

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284 0.055 7.711229e+003 0.056 6.925663e+003 0.057 5.868695e+003 0.059 4.122944e+003 2.108752e+002.000000e+000 0.068 0.000000e+0000.069 0.000000e+000Tlley Cardsse in Mev/g/photon Atomic number000, (+) atomic fraction or (-) weight fractionM1 82000 1 $LEADM2 00 -0.000124 7000 -0.755267 8000 -0.231781 18000 -0.012827 $ DRY AIR(ATTIX 20DegC)M3 1000 -.08 6000 -.60 8000 -.32 $ Methly-Methacrylate (acrylic)M4 13000 -1 $ Alnps 100000000 0.058 4.924823e+003 0.060 3.304443e+003 0.061 3.087121e+003 0.062 2.630850e+003 1.974043e+003 0.063 0.064 1.291109e+003 0.065 7.086478e+002 0.066 0.067 0 a C C Talley for Do F6:p 3 Material Cards CC 60

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APPENDIX D LABVIEW CODE FOR READING OF LINEAR CCD ARRAY USED IN FIBER OPTIC-COUPLED DOSIMETRY SYSTEM The use of the fiber optic-coupled dosimetry system described in Chapters 5 and 6 required the design of a software program in LabViewa that could be used to extract, format, and compile the data ouput (i.e. voltages) from the linear CCD array. A previous LabView code began for a similar purpose was used as a template for the writing of this code. This previous code, containing the basic CCD reading routine and outlines for several other routines, was developed by Paul Falkensteinb at the U.S. Naval Laboratory. The code described here is significantly different from the template code. The code serves two main purposes, the first being calibration of the fiber dosimeters, and the second is reading the calibrated dosimeters during dose measurements. There are also several auxiliary functions that complement the main functions, including zeroing dose history, saving calibration files, opening calibration files, and saving data files. A brief description of the function of the code follows. Screenshots of various aspects of the program will be included in the descriptions, with a link to the actual LabView library file included at the end of the description for those who wish to examine the internal coding of the software. Opening the LabView file CCD7041.llb and running the program displays the main VI (virtual instrument) window (see Figure E-1), from which any other functions of the ational Instruments Corporation, 11500 N Mopac Expwy, Austin, TX 78759 Falkenstein, U.S. Naval Research Laboratory, Optical Physics Branch, Code 5611 a N b Paul 285

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286 p rogram can be accessed, either via the top menu or through various other controls on the front panel. F igure E-1. Screenshot of the front panel of the main VI. this window, the user can monitor the CCD temperature, view the calibration file 5 in the case of Fig the CCD during irradiation. Selecting Controls>Calibrate from the top menu opens the front panel of the calibration VI, where the user will see a screen similar to Figure E-2 (the graph will be blank when the calibration VI is originally opened). he user will first be prompted to accept a background measurement which will be subtracted from the acquired calibration data to eliminate thermal noise (dark current) and background radiation contributions to the data. Next, the user will be prompted to From and saved data file paths, input the number of dosimeters used in the system (2ure E-1), snap a background, or read T

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287 igure E-2. Screenshot of the front panel of the calibration V F I. e desired tion il ion of data for the current calibration point. to individual fiber data based on a peak detection/thresholding y r (mR/V) for enter the number of calibration points desired (see Figure E-3the reader will also notice the background reading displayed in the graph in Figure E-3). After entering th number, the user will once again be prompted, this time to place the fibers in the radiabeam and click OK to begin collecting calibration point number one. When OK is clicked, the routine begins acquiring data from the CCD, summing each integration unthe user clicks the Stop button to end collect t Immediately after data collection ceases for the first point, the calibration routine will binhe CCD pixel data in t routine. After doing this, the user will be prompted to enter the actual dose (actuall exposure in mR recorded in an ionization chamber adjacent to the dosimeters in the x-rayieldsee Figure E-4). This dose is used to calculate a calibration facto f

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288 each dosimeter, and the process is repeated until the desired number of calibration points acquired. The calibration routine will then calculate an average calibration factor, user ill then be prompted to enter a filename under which to save the calibration data. eginning and end of the fiber bins. A sample calibration factor file is displayed in Table is along with the standard deviation of the calibration factor, for each dosimeter. The w Included in the file will be the calibration factors, associated standard deviations, and the b E-1. The entire process can be monitored visually by the user, as the raw data is displayed on the front panel, as are the calculated calibration factors. Figure E-3. Screenshot of calibration routine after background acceptance. User is prompted to ente r desired number of calibration points. d of re-calibrating the fibers. By selecting File>Open Calibration, the user can select the The user may also already have calibration files saved that he wishes to use instea

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289 Figure E-4. Screenshot of calibration VI after irradiation. User is promp ted to enter the exposure delivered to an ionization chamber during the irradiation. ple calibration factor file. CF Fiber Bin Fiber Bin Table E-1. Sam (mV/R) Std. Dev. Start End 33.599 1.636 177.000 194.000 26.514 1.041 277.000 294.000 31.107 1.838 312.000 329.000 32.147 1.468 344.000 361.000 31.025 1.579 414.000 431.000 25.516 0.929 447.000 464.000 15.998 0.748 516.000 532.000 13.916 0.996 550.000 567.000 18.376 0.687 583.000 600.000 20.988 0.951 620.000 636.000 17.499 0.553 686.000 703.000 30.617 1.459 721.000 738.000 31.375 1.825 754.000 771.000 22.081 0.873 789.000 806.000 30.808 1.530 824.000 841.000 42.168 2.609 889.000 906.000 27.401 0.966 922.000 939.000 N ote: Headings added for descriptive purposes. Not present in actual file.

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290 appropriate calibration file, which will be loaded into memory, and the path displayed in the main front panel. Following the calibration of the fibers, the user can make actual dose measurements. The calibration routine will automatically close, and the main front panel d hite pull-down menu on the front panel, and click the green button. This will cause the ta uring the current session (which ends when the program is stopped/closed). Next, the ata>Zero Accumulated Dose to zero dose readings for all dosimeters. Then, the user n process is nd he CCD data. The corrected data is then passed alibration factors associated with each dosimeter are applied. This normalized data is ntil the green (and now oses as described previously, or save the data by selecting File>Save Data As, after isplayed in the main front panel. e actual the e engines) needed to run the code for the purpose of will again be displayed. First, the user will select Snap Background from the green an w program to acquire a background reading which will be subtracted from all acquired da d user will select Continuous CCD Read from the same pull-down menu, and also D can click the green button to begin acquiring data. The data acquisitio described briefly in the following. The CCD is read during each integration cycle, athe background data is subtracted from t through a normalization routine, where the pixel data is binned into fibers, and the current c then added to the running data total. This process will continue u lit) button is clicked again. After clicking the green button, the user can then clear the d which the user will be prompted to enter a file name and path, which will then be d Two objects are present at the end of this appendix. Object E-1 contains th LabView source code for the CCD reader program, while Object E-2 containsexecutable (and necessary run-tim

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291 calibrating and reading the fiber dosimeters. Please note that in order to perform m easurements using the software, a compatible data acquisition (DAQ) card must be nd Automation package, containing the necessary drivers for communication with the bject E-1. LabView code CCD7041. By opening this object, the reader can view the bject E-2. Executable file of the same code contained in Object E-1, CCD7041.llb, e to view the block diagrams. After extracting the files, double-click the setup.exe icon to install the necessary run-time engines, and ip). installed in the computer being used, along with the National Instruments Measurement a DAQ card. O block diagrams associated with each VI, however an install of LabView is needed to view this file (858 KB, CCD7041_code.llb). O along with the run-time engines necessary to run the program. It is not necessary to have an installation of LabView to run this program, however,the user will be unabl then double-click the CCD7041 application file to run the program (18 MB, CCD7041.z

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APPENDIX E M ges version printed here is using the threshold CNR that was applied to head images (i.e. acquired using the head filter, and reconstructed with a head filter and window/level settings), and is titled CTP515_autoscore_head. The corresponding threshold CNR for scoring body images (i.e. acquired using the body filter, and reconstructed with a body filter and window/level settings) was 0.95884. There are a few things that must be mentioned about the code. One element that might seem strange is the case structure. However, this was necessary for two reasons. First, the orientation (horizontal or vertical) of the background ROIs differed depending on the size and relative locations of the low-contrast objects. Second, small errors in the low-contrast objects positions (with respect to the scale drawing provided by the manufacturer), along with slight out-of-plane rotations of the phantom module while resting on the CT table, caused the ROIs corresponding to several objects to be partially outside the low-contrast object, leading to an inaccurate CNR calculation. The case structure allowed for the alteration of individual ROI placements where necessary. Another item that deserves mention is the code section in which the user clicks on the center of the 15 mm, 1.0% contrast object. After the initial locating of this object, MATLAB CODE FOR AUTOMATED SCORING OF IMAGE QUALITY PHANTOIMAGES This appendix contains the Matlab code written for automatic scoring of imaof the Catphan CTP515 low-contrast module, as discussed in Chapter 6. The code 292

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293 this section can be replaced by the recorded pixel location of the center of this object for the balance of the future scoring to ensure accuracy and precision. Finally, the same base code was used for calculation of the threshold CNR values, with slight modifications to print and save the correct data.

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294 CTP515_autoscore_head.m clear all; close all; % Read in CTP515 object position data CTP515_data = dlmread('CTP515_data.txt','\t'); threshold_CNR = 1.0025; resolution = 0.38 % resolution of Matlab image in mm/pixel CTP515_data = CTP515_data; k = 0; while k ~= 1 % Prompt user for file name prompt = {'Enter file name with extension:'}; dlgTitle = 'File Name Input'; lineNo = 1; phantom_image_filename = char(inputdlg(prompt,dlgTitle,lineNo)); % Read in and display image data info = dicominfo(phantom_image_filename); phantom_image = dicomread(info); phantom_image = phantom_image 1030; phantom_image_copy = phantom_image; imshow(phantom_image, 'DisplayRange', [0 100]) % axis image title('Click on the 15mm 1.0% object') % % Establish orientation and size of test object % % Select and display ROI containing 15mm 1.0% object [ROI_x1,ROI_y1] = ginput(1); ROI_x = fix(ROI_x1); ROI_y = fix(ROI_y1); ROI = phantom_image(ROI_y 50:ROI_y + 50, ROI_x 50:ROI_x + 50); imshow(ROI, 'DisplayRange', [0 100]); axis image title('Click on the center of the 15 mm 1.0% object') % Determine x,y coordinate for 15mm 1.0% object [o15_x1,o15_y1] = ginput(1); o15_x = fix(ROI_x 50 + o15_x1); o15_y = fix(ROI_y 50 + o15_y1); % Calculate object positions in image object_Position = []; object_Position(1,:) = [o15_x;o15_y]; r = sqrt((o15_x 256)^2 + (o15_y 256)^2); theta = atan2(-(o15_y 256),(o15_x 256)) (180/pi); if sign(theta) < 0

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295 theta = theta + 360; end theta_Values = []; for nObject = 2:18 new_Theta = theta + CTP515_data(nObject,3); if new_Theta >= 360 new_Theta = new_Theta 360; end theta_Values(nObject) = new_Theta; X = r cosd(new_Theta) + 256; Y = -(r sind(new_Theta)) + 256; object_Position(nObject,:) = [fix(X);fix(Y)]; end new_R = 0.5 r; for nObject = 19:27 new_Theta = theta + CTP515_data(nObject,3); if new_Theta >= 360 new_Theta = new_Theta 360; end theta_Values(nObject) = new_Theta; X = new_R cosd(new_Theta) + 256; Y = -(new_R sind(new_Theta)) + 256; object_Position(nObject,:) = [fix(X);fix(Y)]; end % % Check for the presence of each object %

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296 % % Identify each object in phantom % for nObject = 1:27 % Define the object ROI known_object_size = CTP515_data(nObject,1)/resolution; new_object_size = fix(known_object_size); dim_C = new_object_size/2 + 5; dim_D = dim_C + 10; dim_E = new_object_size/2 + 5; dim_F = dim_E + 10; dim_L = 7; % Calculate positions of objects in CPT515 module--not quite % constructed to specs in CAD drawing switch nObject case 1 ROI_start_y = fix(object_Position(nObject,2) new_object_size/2 + 7); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 7); ROI_start_x = fix(object_Position(nObject,1) new_object_size/2 + 7); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 7); ROI_data = phantom_image(ROI_start_y:ROI_end_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_L):fix(object_Position(nObject,2) + dim_L),... fix(object_Position(nObject,1) dim_F):fix(object_Position(nObject,1) dim_E)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) dim_L):fix(object_Position(nObject,2) + dim_L),... fix(object_Position(nObject,1) + dim_C):fix(object_Position(nObject,1) + dim_D)); phantom_image_copy(fix(object_Position(nObject,2) dim_L):fix(object_Position(nObject,2) + dim_L),... fix(object_Position(nObject,1) dim_F):fix(object_Position(nObject,1) dim_E)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) dim_L):fix(object_Position(nObject,2) + dim_L),... fix(object_Position(nObject,1) + dim_C):fix(object_Position(nObject,1) + dim_D)) = 1000; case 2 ROI_start_y = fix(object_Position(nObject,2) new_object_size/2 + 7); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 3); ROI_start_x = fix(object_Position(nObject,1) new_object_size/2 + 5); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 5); ROI_data = phantom_image(ROI_start_y:ROI_end_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_L + 2):fix(object_Position(nObject,2) + dim_L + 2),... fix(object_Position(nObject,1) dim_F):fix(object_Position(nObject,1) dim_E));

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297 background_ROI2 = phantom_image(fix(object_Position(nObject,2) dim_L + 2):fix(object_Position(nObject,2) + dim_L + 2),... fix(object_Position(nObject,1) + dim_C):fix(object_Position(nObject,1) + dim_D)); phantom_image_copy(fix(object_Position(nObject,2) dim_L + 2):fix(object_Position(nObject,2) + dim_L + 2),... fix(object_Position(nObject,1) dim_F):fix(object_Position(nObject,1) dim_E)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) dim_L + 2):fix(object_Position(nObject,2) + dim_L + 2),... fix(object_Position(nObject,1) + dim_C):fix(object_Position(nObject,1) + dim_D)) = 1000; case 7 ROI_start_y = fix(object_Position(nObject,2) new_object_size/2 + 7); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 7); ROI_start_x = fix(object_Position(nObject,1) new_object_size/2 + 10); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 4); ROI_data = phantom_image(ROI_start_y:ROI_end_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_F):fix(object_Position(nObject,2) dim_E),... fix(object_Position(nObject,1) dim_L + 3):fix(object_Position(nObject,1) + dim_L + 3)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) + dim_C):fix(object_Position(nObject,2) + dim_D),... fix(object_Position(nObject,1) dim_L + 3):fix(object_Position(nObject,1) + dim_L + 3)); phantom_image_copy(fix(object_Position(nObject,2) dim_F):fix(object_Position(nObject,2) dim_E),... fix(object_Position(nObject,1) dim_L + 3):fix(object_Position(nObject,1) + dim_L + 3)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) + dim_C):fix(object_Position(nObject,2) + dim_D),... fix(object_Position(nObject,1) dim_L + 3):fix(object_Position(nObject,1) + dim_L + 3)) = 1000; case 13 ROI_start_y = fix(object_Position(nObject,2) new_object_size/2 + 11); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 3); ROI_start_x = fix(object_Position(nObject,1) new_object_size/2 + 9); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 5); ROI_data = phantom_image(ROI_start_y:ROI_end_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) dim_F + 2):fix(object_Position(nObject,1) dim_E + 2)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),...

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298 fix(object_Position(nObject,1) + dim_C + 2):fix(object_Position(nObject,1) + dim_D + 2)); phantom_image_copy(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) dim_F + 2):fix(object_Position(nObject,1) dim_E + 2)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) + dim_C + 2):fix(object_Position(nObject,1) + dim_D + 2)) = 1000; case 6 ROI_start_y = fix(object_Position(nObject,2) new_object_size/2 + 9); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 1); ROI_start_x = fix(object_Position(nObject,1) new_object_size/2 + 3); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 7); ROI_data = phantom_image(ROI_start_y:ROI_end_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_F + 4):fix(object_Position(nObject,2) dim_E + 4),... fix(object_Position(nObject,1) dim_L 2):fix(object_Position(nObject,1) + dim_L 2)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) + dim_C + 4):fix(object_Position(nObject,2) + dim_D + 4),... fix(object_Position(nObject,1) dim_L 2):fix(object_Position(nObject,1) + dim_L 2)); phantom_image_copy(fix(object_Position(nObject,2) dim_F + 4):fix(object_Position(nObject,2) dim_E + 4),... fix(object_Position(nObject,1) dim_L 2):fix(object_Position(nObject,1) + dim_L 2)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) + dim_C + 4):fix(object_Position(nObject,2) + dim_D + 4),... fix(object_Position(nObject,1) dim_L 2):fix(object_Position(nObject,1) + dim_L 2)) = 1000; case {8,9} ROI_start_y = fix(object_Position(nObject,2) new_object_size/2 + 7); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 3); ROI_start_x = fix(object_Position(nObject,1) new_object_size/2 + 7); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 3); ROI_data = phantom_image(ROI_start_y:ROI_end_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_F + 2):fix(object_Position(nObject,2) dim_E + 2),... fix(object_Position(nObject,1) dim_L + 2):fix(object_Position(nObject,1) + dim_L + 2)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) + dim_C + 2):fix(object_Position(nObject,2) + dim_D + 2),... fix(object_Position(nObject,1) dim_L + 2):fix(object_Position(nObject,1) + dim_L + 2));

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299 phantom_image_copy(fix(object_Position(nObject,2) dim_F + 2):fix(object_Position(nObject,2) dim_E + 2),... fix(object_Position(nObject,1) dim_L + 2):fix(object_Position(nObject,1) + dim_L + 2)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) + dim_C + 2):fix(object_Position(nObject,2) + dim_D + 2),... fix(object_Position(nObject,1) dim_L + 2):fix(object_Position(nObject,1) + dim_L + 2)) = 1000; case {10,11,12} ROI_start_y = fix(object_Position(nObject,2) new_object_size/2 + 3); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 7); ROI_start_x = fix(object_Position(nObject,1) new_object_size/2 + 9); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 1); ROI_data = phantom_image(ROI_start_y:ROI_end_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_F 2):fix(object_Position(nObject,2) dim_E 2),... fix(object_Position(nObject,1) dim_L + 4):fix(object_Position(nObject,1) + dim_L + 4)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) + dim_C 2):fix(object_Position(nObject,2) + dim_D 2),... fix(object_Position(nObject,1) dim_L + 4):fix(object_Position(nObject,1) + dim_L + 4)); phantom_image_copy(fix(object_Position(nObject,2) dim_F 2):fix(object_Position(nObject,2) dim_E 2),... fix(object_Position(nObject,1) dim_L + 4):fix(object_Position(nObject,1) + dim_L + 4)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) + dim_C 2):fix(object_Position(nObject,2) + dim_D 2),... fix(object_Position(nObject,1) dim_L + 4):fix(object_Position(nObject,1) + dim_L + 4)) = 1000; case {14,15,16} ROI_start_y = fix(object_Position(nObject,2) new_object_size/2 + 9); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 1); ROI_start_x = fix(object_Position(nObject,1) new_object_size/2 + 8); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 2); ROI_data = phantom_image(ROI_start_y:ROI_end_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) dim_F + 3):fix(object_Position(nObject,1) dim_E + 3)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) + dim_C + 3):fix(object_Position(nObject,1) + dim_D + 3));

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300 phantom_image_copy(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) dim_F + 3):fix(object_Position(nObject,1) dim_E + 3)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) + dim_C + 3):fix(object_Position(nObject,1) + dim_D + 3)) = 1000; case 17 ROI_start_y = fix(object_Position(nObject,2) new_object_size/2 + 9); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 1); ROI_start_x = fix(object_Position(nObject,1) new_object_size/2 + 9); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 1); ROI_data = phantom_image(ROI_start_y:ROI_end_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) dim_F + 4):fix(object_Position(nObject,1) dim_E + 4)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) + dim_C + 4):fix(object_Position(nObject,1) + dim_D + 4)); phantom_image_copy(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) dim_F + 4):fix(object_Position(nObject,1) dim_E + 4)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) + dim_C + 4):fix(object_Position(nObject,1) + dim_D + 4)) = 1000; case 18 ROI_start_y = fix(object_Position(nObject,2) new_object_size/2 + 9); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 1); ROI_start_x = fix(object_Position(nObject,1) new_object_size/2 +10); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 0); ROI_data = phantom_image(ROI_start_y:ROI_end_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) dim_F + 5):fix(object_Position(nObject,1) dim_E + 5)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),... fix(object_Position(nObject,1) + dim_C + 5):fix(object_Position(nObject,1) + dim_D + 5)); phantom_image_copy(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4),...

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301 fix(object_Position(nO bject,1) dim_F + 5):fix(object_ Position(nObject,1) dim_E + 5)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) dim_L + 4):fix(object_Position(nObject,2) + dim_L + 4), ... fix(object_Position(nO bject,1) + dim_C + 5):fix(object_Position(nObject,1) + dim_D + 5)) = 1000; case 21 ROI_start_y = fix( object_Position(nObject,2) new_object_size/2 + 9); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 1); ROI_start_x = fix( object_Position(nObject,1) new_object_size/2 + 5); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 5); ROI_data = phantom_ image(ROI_start_y:ROI_e nd_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_F + 4):fix(object_Position(nObject,2) dim_E + 4), ... fix(object_Position( nObject,1) dim_L):fix(object_Position(nObject,1) + dim_L)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) + dim_C + 4):fix(object_Position(nOb ject,2) + dim_D + 4), ... fix(object_Position( nObject,1) dim_L):fix(object_Position(nObject,1) + dim_L)); phantom_image_copy(fix(object_Position(nObject,2) dim_F + 4):fix(object_Position(nObject,2) dim_E + 4), ... fix(object_Position( nObject,1) dim_L):fix(object_Position(nObject,1) + dim_L)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) + dim_C + 4):fix(object_Position(nOb ject,2) + dim_D + 4), ... fix(object_Position( nObject,1) dim_L):fix(object_Position(nObject,1) + dim_L)) = 1000; case {25,26,27} ROI_start_y = fix( object_Position(nObject,2) new_object_size/2 + 7); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 3); ROI_start_x = fix( object_Position(nObject,1) new_object_size/2 + 5); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 5); ROI_data = phantom_ image(ROI_start_y:ROI_e nd_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_L + 2):fix(object_Position(nObject,2) + dim_L + 2), ... fix(object_Position( nObject,1) dim_F):fix(object_Position(nObject,1) dim_E)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) dim_L + 2):fix(object_Position(nObject,2) + dim_L + 2), ... fix(object_Position( nObject,1) + dim_C):fix(object_Position(nObject,1) + dim_D)); phantom_image_copy(fix(object_Position(nObject,2) dim_L + 2):fix(object_Position(nObject,2) + dim_L + 2), ... fix(object_Position( nObject,1) dim_F):fix(object_Position(nObject,1) dim_E)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) dim_L + 2):fix(object_Position(nObject,2) + dim_L + 2), ... fix(object_Position( nObject,1) + dim_C):fix(object_Position(nObject,1) + dim_D)) = 1000;

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302 otherwise ROI_start_y = fix( object_Position(nObject,2) new_object_size/2 + 7); ROI_end_y = fix(object_Position(nObject,2) + new_object_size/2 3); ROI_start_x = fix( object_Position(nObject,1) new_object_size/2 + 5); ROI_end_x = fix(object_Position(nObject,1) + new_object_size/2 5); ROI_data = phantom_ image(ROI_start_y:ROI_e nd_y,ROI_start_x:ROI_end_x); background_ROI1 = phantom_image(fix(object_Position(nObject,2) dim_F + 2):fix(object_Position(nObject,2) dim_E + 2), ... fix(object_Position( nObject,1) dim_L):fix(object_Position(nObject,1) + dim_L)); background_ROI2 = phantom_image(fix(object_Position(nObject,2) + dim_C + 2):fix(object_Position(nOb ject,2) + dim_D + 2), ... fix(object_Position( nObject,1) dim_L):fix(object_Position(nObject,1) + dim_L)); phantom_image_copy(fix(object_Position(nObject,2) dim_F + 2):fix(object_Position(nObject,2) dim_E + 2), ... fix(object_Position( nObject,1) dim_L):fix(object_Position(nObject,1) + dim_L)) = 1000; phantom_image_copy(fix(object_Position(nObject,2) + dim_C + 2):fix(object_Position(nOb ject,2) + dim_D + 2), ... fix(object_Position( nObject,1) dim_L):fix(object_Position(nObject,1) + dim_L)) = 1000; end phantom_image_copy(ROI_sta rt_y:ROI_end_y,ROI_sta rt_x:ROI_end_x) = 1000; imshow(phantom_image_copy, 'DisplayRange' [0 100]); % Convert ROI and background data to vectors ROI_data = mat2vec(ROI_data); background_ROI1 = mat2vec(background_ROI1)'; background_ROI2 = mat2vec(background_ROI2)'; background_data = [background_ROI1 background_ROI2]; % Calculate the CNR of the ROI ROI_data = double(ROI_data); background_data = double(background_data); mean_ROI = mean(ROI_data); mean_background = mean(background_data); stdev_background = std(background_data); CNR = (mean_ROI mean_background)/stdev_background; CNR_summary(nObject) = CNR; % Determine the number of elements present if CNR > threshold_CNR presence(nObject) = 1; else presence(nObject) = 0; end end % Determine how many objects were found and then score for each row

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303 total_score = sum(presence); supraslice_score = sum(presence(1:18)); subslice_score = sum(presence(19:27)); score = [total_score supraslice_score subslice_score]; % Write CNR summary to a file dlmwrite( 'CNR_summary.txt' [CNR_summary], '-append' 'delimiter' '\t' 'newline' 'pc' ); dlmwrite( 'score.txt' [score], '-append' 'delimiter' '\t' 'newline' 'pc' ); k = waitforbuttonpress; close(1); end type score.txt % END OF PROGRAM

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304 APPENDIX F INSTRUCTION SHEET FOR RADIOLOGIST S SCORING OF PHANTOM IMAGES AND FINAL TALLY OF RADIOL OGISTS PHANTOM SCORES Above is a diagram of the Catpha n module that was scanned for use in this study. (Note: The im ages you see were not necessarily acquired with the phantom in this orientatio n). You will be scoring two sets of images, each set recons tructed with a different kernel. Both sets of images were scanne d at 100 kVp. Only the OUTER circle of objects should be coun ted for the purpose of this study ignore the inner circle of objects, do not count these in your total score.

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305 Scoring instructions: Your score will be the total number of objects visible in each group in the outer circle. An object shou ld be counted as visible if it meets the following criteria: 1) T he object is separate and distinct from the background and from other objects, and 2) The object is contained within the row of objects that comprise the current object group. The image is to be scored AS PRESENTED without the aid of window and level control and without the aid of zoom. Score Group 1 first, fo llowed by Group 2, and finally Group 3. You will be able to view each image for a maximum of one minute. After you have scored an image, I will ask you to look away from the screen for a moment while the next image is loaded. There are a total of 15 images to be scored. A sample score sheet is presented below. In the example, for the first image presented, I was able to see 6 objects in Group One, 4 objects in Group Two, and no objects in Group Three.

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306 Phantom Scoring Sheet Name: __ Kyle Jones Head reconstructionGroup 1Group 2Group 3 1640 2 3 4 5 6 7Body reconstructionGroup 1Group 2Group 3 1 2 3 4 5 6 7 8 Object F-1. Scoresheet of final tally of radiologist s phantom scores (28 KB, Radiologists Phantom Scores.xls)

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307 LIST OF REFERENCES 1D. P. Frush and L. F. Donnelly, "Helical CT in children: Technical considerations and body applications," Radiology 209, 37-48 (1998). 2L. L. Berland and J. K. Smith, "Multidet ector-array CT: Once again, technology creates new opportunities," Radiology 209, 327-9 (1998). 3P. C. Shrimpton and S. Edyvean, "CT scanner dosimetry," Br. J. Radiol. 71, 1-3 (1998). 4P. C. Shrimpton, and B. F. Wall, "CT--an increasingly important slice of the medical exposure of patients," Br. J. Radiol. 66, 1067-8 (1993). 5E. L. Nickoloff, and P. O. Alderson, "Radia tion exposures to patients from CT: Reality, public perception, and policy," Am. J. Roentgenol. 177, 285-7 (2001). 6C. Y. Chan, Y. C. Wong, L. F. Chau, S. K. Yu, and P. C. Lau, "Radiation dose reduction in paediatric cranial CT," Pediatr. Radiol. 29, 770-5 (1999). 7L. F. Donnelly, K. H. Emery, A. S. Brody, T. Laor, V. M. Gylys-Morin, C. G. Anton, S. R. Thomas, and D. P. Frush, "Minimizing ra diation dose for pediatric body applications of single-detector helical CT: Strategies at a large Children's Hospital," Am. J. Roentgenol. 176, 303-6 (2001). 8A. Vade, T. C. Demos, M. C. Olson, P. Subbaiah, R. C. Turbin, K. Vickery, and K. Corrigan, "Evaluation of image quality using 1 : 1 pitch and 1.5 : 1 pitch helical CT in children: A comparative study," Pediatr. Radiol. 26, 891-3 (1996). 9E. J. Hall, "Lessons we have learned from our children: Cancer risks from diagnostic radiology," Pediatr. Radiol. 32, 700-6 (2002). 10International Commission on Ra diological Protection (ICRP), 1990 Recommendations of the international commissi on on radiological protection. ICRP Report 60. (ICRP, Oxford, UK, 1991). 11D. A. Pierce and D. L. Preston, "Radiation-related cancer risks at low doses among atomic bomb survivors," Radiat. Res. 154, 178-86 (2000). 12D. Brenner, C. Elliston, E. Hall, and W. Be rdon, "Estimated risks of radiation-induced fatal cancer from pediatric CT," Am. J. Roentgenol. 176, 289-96 (2001).

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PAGE 339

320 BIOGRAPHICAL SKETCH Aaron Kyle Jones was born in Hendersonville, North Carolina, on July 26, 1979, to David Tomberlin and Lynnette Wright Jones. He is one of two children born to David and Lynnette, along with a sister, Allison. He attended high school in East Flat Rock, North Carolina, at East Henderson High School. He then went on to attend Furman University in Greenville, South Carolina, wh ere he earned a bachelors degree in physics, magna cum laude, in 2001. From there, it was on to the University of Florida in Gainesville, Florida, where he earned a ma sters degree in medical physics through the Department of Nuclear and Radi ological Engineering in 2003.


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Full Text












DOSE VERSUS IMAGE QUALITY IN PEDIATRIC RADIOLOGY: STUDIES
USING A TOMOGRAPHIC NEWBORN PHYSICAL PHANTOM WITH AN
INCORPORATED DOSIMETRY SYSTEM















By

AARON KYLE JONES


A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA


2006

































Copyright 2006

by

Aaron Kyle Jones

































This work is dedicated to my loving and devoted fiancee Marisa, and to my wonderful
parents, David and Lynnette Jones. Without their support, none of this would have been
possible.















ACKNOWLEDGMENTS

First and foremost, I would like to extend my deepest gratitude and thanks to my

advisor and the chairman of my supervisory committee, Dr. David Hintenlang. Without

his support, encouragement, friendship, and instruction, this would have been a much

longer road to travel. The amount I learned from him in this short time is unfathomable.

I also owe Dr. Wesley Bolch a large debt of gratitude, as he, too, has been instrumental in

guiding me to where I am today. And I thank Dr. Bolch for teaching me how to write a

sensational scientific paper. In addition, I would like to thank each of my other

committee members, including Dr. Manual Arreola, Dr. Jonathan Williams, and Dr. Hans

van Oostrom. My committee's constructive criticisms and helpful advice on not only

medical physics, but everything from career to life in general, has served to steer me in

the right direction for the last five years.

I would also like to thank my fellow students, past and present, in the Nuclear and

Radiological Engineering Department. Those who went before me, including

Christopher Pitcher and Luis Benevides, offered countless tips and invaluable advice, and

were always willing to answer the phone and my questions. Also, a special thank you

goes to Robert Staton, in whose office many a conversation was had concerning the

Pediatric Organ Dose project, and just as many concerning the Mighty Orange and Blue.

Another big thank you goes to the staff in the Nuclear and Radiological

Engineering Department, who were always there to straighten out whatever mess I

seemed to have entangled myself in at the time. Also, I appreciate Dr. Alireza









Haghighat's generosity in funding student travel to national meetings, where I had the

opportunity to present my work and see what others in the medical physics community

were working on.

Finally, I would like to thank my family and friends for their devotion, love, and

support. Mike, Bart, Bill, and Jeff, you were always there when I was discouraged and

needed someone to talk to, shoot hoops with, play video games with, or throw some

darts, and that is something I will never forget. Most importantly, I would like to thank

my parents, David and Lynnette Jones, for instilling in me the work ethic, morals and

values I needed to succeed in life, and my fiancee Marisa, whom I love with all my being,

for her undying devotion, support, and love.
















TABLE OF CONTENTS



A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES ........................... .. ......... ...... ............ ............. xi

LIST OF FIGU RE S ............................................. .. .... ....... .. .......... xiii

L IST O F O B JE C T S .... ...................................................... .. .. ....... .............. xvii

ABSTRACT ........ .............. ............. ...... ...................... xix

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

R radiation E effects and R isks............................................................................ ... .... 2
Pediatric R adiology ......................... ................................................ ....
Doses to Pediatric Patients ................................. ..................5
Strategies for Reducing Doses to Pediatric Patients............................................6
Patient Simulation in Diagnostic Radiology ............ ...........................................7
Tissue-equivalent M aterials.............................................................. ............... 7
P patient Phantom s ........................ .. ........................ .... ........ ................ .8
D osim etry in D iagnostic R adiology ........................................ ........................ 9
Dosim eters in Diagnostic Radiology ............... .............................................9
Dosimetric Quantities in Diagnostic Radiology...............................................12
Image Quality Assessment in Diagnostic Radiology ..................................14
Methods of Image Quality Assessment in Diagnostic Radiology.....................14
Computational Observers and Computer-Aided Diagnosis (CAD) ..................16
Hum an Observer Studies ................................. .......................... ...............17
Hybrid Methods for Assessing Image Quality .......................................... 18
Objectives of this R research ..................................................................... 19

2 MOSFET DOSIMETER DEPTH-DOSE MEASUREMENTS IN
HETEROGENEOUS TISSUE-EQUIVALENT PHANTOMS AT DIAGNOSTIC
X -R A Y E N E R G IE S ......................................................................... ....................2 1

In tro d u ctio n ................................................... .................. ................ 2 1
M materials and M methods ....................................................................... ..................22
Cylindrical Phantom s .................................. ................. ..... ....... 23









M O SFE T D osim etry System ..................................................................... .....24
Experim ental Param eters......................................................................25
M onte Carlo Sim ulations.............. ................ ......................... ............... 26
R e su lts ............................... .......... ...................................................2 7
Discussion .................... ...................................28
C o n c lu sio n s........................................................................................................... 3 2

3 A TOMOGRAPHIC PHYSICAL PHANTOM OF THE NEWBORN CHILD
WITH REAL-TIME DOSIMETRY. I. METHODS AND TECHNIQUES FOR
C O N S T R U C T IO N ............................................................................................... 34

In tro d u ctio n .......................................................................................3 4
M materials an d M eth od s ......................................................................................... 36
D ata Form atting and O utput ...................................................................... ..... 37
Production of Soft Tissue Blanks .................................................. .............38
F orm action of Slices ............................................................................... 4 0
B one Introduction ................................................................................. 41
Lung Construction ....................... ......... ......... .........43
D osim eter L ocalization ................................................................................. 46
Phantom Assembly ............... ......... ......... ........47
Results ............... ...... ............ ............. ...............49
D iscu ssion ......... ........... .......... ................ ............................50
C o n c lu sio n s........................................................................................................... 5 6

4 A TOMOGRAPHIC, PHYSICAL PHANTOM OF THE NEWBORN CHILD
WITH REAL-TIME DOSIMETRY. II. SCALING FACTORS FOR
CALCULATION OF AVERAGE ORGAN DOSE IN PEDIATRIC
R A D IO G R A P H Y ................................................................................................. 5 8

In tro d u ctio n ........................................................................................5 8
M materials and M methods ............... .......... ......... ... ................59
Modifications to the Newborn Computational Phantom ............... ............... 59
Monte Carlo Codes for Radiograph Simulation ........................... ...............63
Simulated fields of view ........................... ....... ............. 63
X-ray source modeling and beam characterization .............. ............... 64
Monte Carlo ionization chamber simulations ............................................65
Creation of Point-to-Organ Dose Scaling Factors (SFPOD) ...........................66
Newborn Radiographic Exams ........................ ..................... 68
R esu lts an d D iscu ssion ...............................................................................................7 0
C o n c lu sio n s........................................................................................................... 7 2

5 CHARACTERIZATION AND TESTING OF THE FIBER OPTIC-COUPLED
(FOC) DOSIM ETER ............................................................................ 74

In tro d u ctio n .......................................................................................7 4
M materials an d M eth od s ......................................................................................... 74
Energy D ependence ......................................................................75









Dose Linearity ......................................... ........ ............... 75
A ngular D dependence ................. ............................................... .....................76
Dosimeter Response at Varying Bend Radii .......................................................76
The Twenty-Five Fiber FOC Dosimetry System .........................................77
Sensitizing the FOC dosim eters ....................................... ............... 80
Calibration of the FOC dosimeters............................................................80
R esu lts an d D iscu ssion ..................................................................... ................ .. 83
C o n c lu sio n s..................................................... ................ 8 5

6 OPTIMIZATION OF DOSE WHILE MAINTAINING ADEQUATE IMAGE
QUALITY IN PEDIATRIC COMPUTED TOMOGRAPHY .................................87

In tro d u ctio n .................................... ................................. ................ 8 7
M materials and M methods ....................................................................... ..................93
D o sim etry ...................... ... ... ............................................. .9 3
UF Newborn tomographic phantom..........................................................94
Fiber optic-coupled dosim etry system ............................... ................94
CT scanning of the phantom ................................. ............. .................. 96
D ose calculation ......................................... ........ .. .. .. .. .. ........ .... 99
Im age Quality A analysis ....................................................... ......... ..... 103
Catphan CTP515 module ........ ..........................................103
Phantom image scoring software ........................................ ..... ......104
Threshold contrast-to-noise ratio determination .....................................106
P roto col S election .......... ............................................................ .. .... .. .... .. 10 8
R results ............. ...... ... .. ......... .............................................. 109
D discussion ................ ........ ............................................. ..............119
C o n c lu sio n s........................... ........................................................................... 12 5
Calibration of the FOC Dosimeter ..............................................................129
General Trends in Pediatric CT Protocols.............................. ............... 129
Future W ork. ...................................................... ...... ............. 131

7 DOSE COMPARISON BETWEEN PHYSICAL MEASUREMENTS AND
COMPUTATIONAL SIMULATIONS ...........................................................133

Possible Sources of Error ......... ................. ............................... .................... 139
Incorrect Use of Normalization Factors ...................................................141
Other Possible Sources of Error .............. ........................... ............... 143
Investigation of Possible Error Sources............. ..................... ................. 144
Physical Measurement Errors............................ ........ ............... 144
Com putational Sim ulation Errors ............................. .................................. 147
Conclusions and Future W ork ........................................................ ............... 149

8 C O N C L U SIO N .......... ....................................................................... ........ ... ... 151

R results of this W ork ......... ...................... .. ............................ .. ............ .. ..151
Opportunities for Future Work and Development............................... ...............151
Improvement of the Production Process for Tissue-Equivalent Materials........ 151









Automation of the Physical Phantom Construction Process .............................152
Image Quality Phantom Construction .................................... ............... 155
M modifications to FOC D osim etry System ....................................................... 157
The Future of the Pediatric Organ Dose Project................... ...................................159
F in a l W o rd s ..............................................................................................................1 6 2

APPENDIX

A TISSUE-EQUIVALENT MATERIALS FOR CONSTRUCTION OF
TOMOGRAPHIC DOSIMETRY PHANTOMS IN PEDIATRIC RADIOLOGY.. 164

Introdu action ........................................... ............ ............................ 164
M materials and M methods ........................................................................ ...............165
Soft Tissue-Equivalent Substitute for the Newborn (STES-NB)......................166
Bone Tissue-Equivalent Substitute for the Newborn (BTES-NB)....................166
Soft and Bone TE Substitutes for the Child/Adult (STES and BTES)............167
Lung Tissue-Equivalent Substitute for the Newborn/Child/Adult (LTES).......167
M manufacturing Process ................... ........... .. ........... ............................. 168
Measurement of TE Material Mass Density................................................ 168
Comparison of Radiation Interaction Coefficients ...................................... 169
Calculations of X-ray Attenuation and Absorbed Dose at Depth .....................171
R results and D discussion ............................. .. ........ ... .... .......... .. ................ .. 172
Comparisons of UF Tissue Substitutes to Reference Tissue Compositions......172
Comparison of UF Tissue Substitutes to Other TE Materials........................... 177
Calculations of X-ray Attenuation and Absorbed Dose at Depth .....................181
C o n clu sio n s................................................... .................. 18 2

B BITMAP IMAGES OF THE 5 MM SLICES USED AS TEMPLATES FOR
PHANTOM CON STRUCTION ........................................ ........................... 185

C MCNP CODE FOR CYLINDRICAL PHANTOM DEPTH-DOSE
COM PARISON S .................................................... ......... ........ .... 269

MCNP Code for Homogeneous Soft Tissue Phantom ..........................................270
MCNP Code for Heterogeneous Soft and Bone Tissue Phantom ..........................274
MCNP Code for Heterogeneous Soft, Bone, and Lung Tissue Phantom.................278
MCNP Code for Ionization Chamber Simulation ......................................... 282

D LABVIEW CODE FOR READING OF LINEAR CCD ARRAY USED IN
FIBER OPTIC-COUPLED DOSIMETRY SYSTEM............................................. 285

E MATLAB CODE FOR AUTOMATED SCORING OF IMAGE QUALITY
PH A N T O M IM A G E S ...................................................................... .................. 292

F INSTRUCTION SHEET FOR RADIOLOGISTS' SCORING OF PHANTOM
IMAGES AND FINAL TALLY OF RADIOLOGISTS' PHANTOM SCORES ....304

LIST O F R EFER EN CE S ....................................... ............................ ............... 307









B IO G R A PH IC A L SK E T C H ........................................... ...........................................320
















LIST OF TABLES


Table page

1-1 Tissue w fighting factors from ICRP 60 ........................... ..... ......... .............. 14

3-1. Locations selected for dosimeter placement in the tomographic physical
phantom ................................................................................48

4-1 Organ and skeletal masses within the modified newborn computational phantom .61

4-2 Radiographic SFPOD for the newborn phantom....................... ................ ........... 71

4-3 The effective dose per unit integrated tube current calculated with and without
the application of point-to-organ dose scaling factors SFOD ................................71

6-1 Dose measurement locations for head exams. ................................. ............... 99

6-2 Point dose measurement locations for CAP exams........................................101

6-3 Tissue weighting factors from ICRP 60 ...... .................................102

6-4 Protocol element selection for evaluation. .................................. .................109

6-5 Sample organ dose table for head exams. .... ...............................118

6-6 Sample organ dose table for CAP exams .......................................119

6-7 Calibration factor correction factors. ....................................................... 121

6-8 Magnitudes of average expected dose reductions when adjusting scanning
protocols. .......................................................................... 124

6-9 Default pediatric protocols at Shands Hospital. .............................................. 126

7-1 Comparison of simulated and measured effective and organ doses for head
exam s at 80 kV ....................................................................... 135

7-2 Comparison of simulated and measured effective and organ doses for head
exam s at 100 kV ......................................... ......... ........... .......... ............ 136

7-3 Comparison of simulated and measured effective and organ doses for head
exam s at 120 kV ..................................................................... 137









7-4 Comparison of simulated and measured effective doses for head exams at 100
m A s and a pitch of 1.0. ............................. .............. ...........................137

7-5 Comparison of simulated and measured effective and organ doses for CAP
exam s at 80 kV ....................................................................... 138

7-6 Comparison of simulated and measured effective and organ doses for CAP
exam s at 100 kV ..................................................................... 138

7-7 Comparison of simulated and measured effective and organ doses for CAP
exam s at 120 kV ..................................................................... 140

7-8 Comparison of simulated and measured effective doses for CAP exams at 100
m A s and a pitch of 1.0. ............................... ............ ...........................14 1

7-9 Approximate errors associated with using head normalization factors for CAP
ex am s......... ............................................... .......................................... 14 2

8-1 Data for construction of proposed modifications to the Catphan 500.................. 158

A-i Elemental composition and effective atomic numbers for the UF newborn tissue-
equivalent substitutes and their corresponding reference tissue compositions ......173

A-2 Elemental composition and effective atomic numbers for the UF tissue-
equivalent substitutes needed for phantom construction at ages of 1-year and
older. .............................................................................174

A-3 Results for calculations of narrow-beam photon fluence-rate attenuation through
4 cm of tissue-equivalent material and the resulting single-collision absorbed
dose.............................................................. .....................183

E-1 Sample calibration factor file. ................. .............................289
















LIST OF FIGURES


Figure page

1-1 The attributable lifetime risk from a single small dose at various ages at the time
of ex p o su re .................................................. ........................... 3

1-2 Estimated radiation-related excess relative risk for solid-cancer mortality among
A-bomb survivors. .......................... ........ ... ........ .......... ....... 3

1-3 Lifetime attributable cancer mortality risk as a function of age at examination
for a single typical CT examination of head and abdomen ..................................4

2-1 Schematic of the three phantom configurations used in the depth-dose study. .......23

2-2 Comparison of measured and simulated tissue absorbed dose with depth within
the homogeneous soft tissue phantom ................................. ........................ 27

2-3 Comparison of measured and simulated tissue absorbed dose with depth within
the heterogeneous soft tissue and bone tissue phantom ........................................ 29

2-4 Comparison of measured and simulated tissue absorbed dose with depth within
the heterogeneous soft tissue, bone tissue, and lung tissue phantom...................30

3-1 Typical bitmap image demonstrating the four regions used in this phantom.. ........38

3-2 Example of the Teflon and clay mold used to form the raw soft tissue blanks........39

3-3 Sanded blank used to create the soft tissue outline of each slice of the phantom....40

3-4 Slices after Step 3 of the phantom creation process.....................................41

3-5 Slices after bone introduction ......... ............................................... ... ........... 43

3-6 Lung tissue blanks cut into slices after removal from PVC pipe mold ................44

3-7 Soft tissue outlines for phantom slices containing lung tissue and corresponding
lung regions saved from bitmap transparencies. ................... ................... .......... 45

3-8 View of a phantom slice containing lung after bone introduction, prior to
sanding. ....................................................................46

3-9 Slice 27. ..................................................................50









3-11 Profile photograph of the completed phantom, without arms ..............................52

3-12 Photograph of the completed phantom, with arms............... ...........................53

3-13 Typical dosimeter channels in completed slices. .............................................. 55

4-1 UF Newborn showing the internal organ structure and exterior of the
computational phantom along with the exterior of the corresponding physical
phantom ............................................................... ..... ..... ......... 60

4-2 Axial slices through the UF Newborn phantom .............................................60

5-1 Coated active areas of the FOC dosimeters. ................................. ..................... 77

5-2 Schematic of the design of the FOC dosimetry system. ........................................79

5-3 Graphical depiction of the effects of the sensitization of the FOC dosimeters. .....81

5-4 Illustration of the entire calibration setup. ........................................ ....................82

5-5 Close-up of the FOC dosimeter alignment during calibration ................................82

5-6 Energy dependence of the FOC dosimeter..........................................................83

5-7 Dose linearity of the FOC dosim eter..................................................................... 84

5-8 FOC dosimeter response versus fiber bend radius ................................................84

6-1 Illustrations of the general scanning setup. ................................... ............... 97

6-2 Photographs of the scanning setup for head exams and body exams.....................98

6-3 Approximate scanning coverage of the head exams. ............................................100

6-4 Approximate scanning coverage of the CAP exams .............................................102

6-5 Effective doses corresponding to Head 80 kV and CAP 80 kV.............................110

6-6 Object scores corresponding to Head 80 kV and CAP 80 kV. ............................111

6-7 Object scores (sorted by pitch) corresponding to Head 80 kV and CAP 80 kV.... 114

6-8 Object score and effective dose data presented on the same plot for CAP 80 kV
and CAP 120 kV. ....................... ........ ........... .... ...... .. ............ ... 116

6-9 Total object scores for head exams at 100 mAs, effective doses for head exams
at 120 kV ........................................................................... 127









6-10 Total object scores for CAP exams at 100 mAs, effective doses for CAP exams
at 100 m A s. ...................................................... ................. 12 8

7-1 Raw data from original organ dose measurement scan............... ... ...............146

7-2 Raw data from dose measurement scan with PMT housing shielded..................147

7-3 Raw data from scanning of unshielded and shielded patch cable.........................148

8-1 Various views of the completed vacuum chamber ..............................................153

8-2 Photographs of the new phantom construction system................................155

8-3 Schematic of the proposed modifications to the Catphan 500.............................157

A-1 Ratios of mass attenuation coefficients for the STES-NB, BTES-NB, and LTES
tissue substitutes to their corresponding reference values ....................................175

A-2 Ratios of mass energy-absorption coefficients for the STES-NB, BTES-NB, and
LTES tissue substitutes to their corresponding reference values........................176

A-3 Ratios of mass attenuation coefficients for the STES, BTES, and LTES tissue
substitutes to their corresponding reference values..........................................177

A-4 Ratios of mass energy-absorption coefficients for the STES, BTES, and LTES
tissue substitutes to their corresponding reference values ....................................177

A-5 Ratios of both [t/p and ten/P for STES-NB and acrylic to their corresponding
reference values as a function of photon energy. ...................................................178

A-6 Ratios of both [t/p and ten/P for BTES-NB and aluminum to their corresponding
reference values as a function of photon energy. ...................................................179

A-7 Ratios of both [t/p and ten/P for LTES, LN10/75, and air to their corresponding
reference values as a function of photon energy. ...................................................179

A-8 Ratios of both [t/p and ten/P for STES, MS11, and acrylic to their corresponding
reference values as a function of photon energy. ...................................................180

A-9 Ratios of [t/p for BTES, IB1, SB5, weighted combination of IB and SB5, and
aluminum to their corresponding reference values as a function of photon
energy ..............................................................................18 1

A-10 Ratios of tgen/P for BTES, IB1, SB5, weighted combination of IB1 and SB5, and
aluminum to their corresponding reference values as a function of photon
energy ..............................................................................182

E-1 Screenshot of the front panel of the main VI. ............................ ..................286









E-2 Screenshot of the front panel of the calibration VI. ...............................................287

E-3 Screenshot of calibration routine after background acceptance .............................288

E-4 Screenshot of calibration VI after irradiation...................... ................. 289















LIST OF OBJECTS


Object page

4-1 Radiograph Organ Dose Calculator developed by Robert Staton and Aaron Kyle
Jones at the University of Florida (61.5 KB, Radiograph_Organ Dose
C alculator.xls). ..................................................... ................. 68

5-1 PDF document containing the complete specifications of the S7031-1007 CCD
image sensor and C7041 detector head (237 KB, CCD_specs.pdf).......................78

6-1 CT Organ Dose Calculator developed by Robert Staton and Aaron Kyle Jones at
the University of Florida (57 KB, CT_OrganDose_Calculator.xls). .................103

6-2 Effective dose plots generated as a result of this study (217 KB, EffectiveDose
P lo ts .x ls) ...................................... ................................................ 1 1 7

6-3 Object score plots generated as a result of this study (255 KB, Autoscore
Scores.xls) ...................................................................... ..........117

6-4 Effective dose plots with object scores (as in Figure 6-8) generated as a result of
this study (229 KB, EffectiveDosePlotswithImage_Quality Data.xls). ........118

6-5 Organ dose tables for Head 80 kV (50 KB, Head_80_kV Summary.xls). ..........118

6-6 Organ dose tables for Head 100 kV (52 KB, Head_100_kVSummary.xls). .......118

6-7 Organ dose tables for Head 120 kV (52 KB, Head_120_kVSummary.xls). .......118

6-8 Organ dose tables for CAP 80 kV (58 KB, CAP_80 kV Summary.xls)..............118

6-9 Organ dose tables for CAP 100 kV (58 KB, CAP_100_kVSummary.xls)..........119

6-10 Organ dose tables for CAP 120 kV (58 KB, CAP_120_kVSummary.xls)..........119

E-1 LabView code CCD7041 (858 KB, CCD7041_code.llb) .................................... 291

E-2 Executable file of the same code contained in Object E-l, CCD7041.11b, along
with necessary run-time engines (18 MB, CCD7041.zip)...................................291

F-l Scoresheet of final tally of radiologists' phantom scores (28 KB, Radiologists
Phantom _Scores.xls).......................................... ...................................... 306















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

DOSE VERSUS IMAGE QUALITY IN PEDIATRIC RADIOLOGY: STUDIES
USING A TOMOGRAPHIC NEWBORN PHYSICAL PHANTOM WITH AN
INCORPORATED DOSIMETRY SYSTEM

By

Aaron Kyle Jones

May 2006

Chair: David E. Hintenlang
Major Department: Nuclear and Radiological Engineering

Pediatric patients in hospitals have benefited enormously from the many advances

in medical care during the past few decades. As a result of these advances, pediatric

patients in hospitals are subjected to many diagnostic exams, and as technology continues

to improve, many of these exams deliver higher and higher radiation doses to pediatric

patients. One of the most rapidly advancing technologies, and perhaps the technology

most responsible for the increase of favorable outcomes in premature infants and

pediatric patients, is medical imaging. However, rapidly improving image quality has

also led to rapidly escalating radiation doses due to diagnostic imaging, and little is

known about the magnitude of these doses in pediatric patients, nor the potential for

reducing these doses.

The ultimate goal of this work was to accurately quantify the doses delivered to

pediatric patients during computed tomography (CT) exams, and thus identify potential

dose-saving protocols that maintain adequate image quality. A tomographic newborn


xviii









physical phantom was constructed from tissue-equivalent materials for use in evaluating

the doses delivered to pediatric patients as a result of diagnostic imaging. Fiber optic-

coupled (FOC) dosimeters were used along with the physical phantom to measure

average organ doses during projection radiography. Also, average organ doses were

measured during CT exams across a wide range of protocol parameters. Then, images of

the Catphan CTP515 low contrast module were acquired using the same protocols, and

scored automatically with a custom-written scoring routine. Thus, potential dose-saving

protocols were evaluated to identify those that maintained adequate image quality.

The results of this work suggest that the vast majority of pediatric CT scanning be

performed using a pitch value of 1.0, with the exception of certain challenging imaging

tasks and specialized imaging protocols. The results also suggest that the majority of

pediatric CT scanning be performed using a collimated beam width of 24 mm (16 x 1.5)

and a gantry cycle time of 0.5 second in order to complete the scan as quickly as possible

and keep dose to a minimum without compromising image quality. It was also confirmed

that increasing the tube potential used for CT scanning results in large dose penalties,

with a twofold (approximately) dose increase expected when the tube potential is

increased from 80 kV to 100 kV, and a 30 percent (approximately) dose increase

expected when the tube potential is increased from 100 kV to 120 kV. It was also

confirmed that reducing the scanning pitch below 1.0 results in large dose penalties as

well; however, it is of interest to note that a pitch of 1.25 did generally lead to inferior

image quality (i.e., low contrast detectability) when compared to a pitch value of 1.0.














CHAPTER 1
INTRODUCTION

Pediatric patients in hospitals have benefited enormously from the many advances

in medical care during the past few decades. As a result of these advances, pediatric

patients in hospitals are subjected to many diagnostic exams, and as technology continues

to improve, many of these exams deliver higher and higher radiation doses to pediatric

patients. One of the most rapidly advancing technologies, and perhaps the technology

most responsible for the increase of favorable outcomes in premature infants and

pediatric patients, is medical imaging. Computed tomography (CT), specifically, has

undergone numerous transformations and upgrades, including helical scanning and the

introduction of multiple-row detectors. This has led to a vast increase in the utilization of

CT and the extension of CT to new types of clinical diagnoses.1' However, there is a

trade-off involved in this situation. While CT provides unmatched diagnostic

information, it is also inherently a high-dose imaging modality. It has been reported that

upwards of 40% of the total dose from medical imaging is a result of CT examinations,'4

and in the U.S., due to the absence of limits on dose per scan, this number may approach

60%.5 There have been an enormous number of studies done regarding dose, image

quality, or both in pediatric CT, encompassing all types of exams. Several studies have

already found that a 30-40% reduction in dose is possible with comparable image quality,

and perhaps even more dose reduction is possible with no loss of diagnostic

information.6-8









Radiation Effects and Risks

Children are ten times more radiosensitive than adults, and one abdominal CT scan

delivers an effective dose equal to 500 chest radiographs and is equivalent to the average

national background radiation over a period of more than 3 years. Also, as a rule of

thumb, the lifetime cancer mortality risk attributable to the radiation exposure from a

single abdominal CT exam in a 1-year-old child is of the order of one in a thousand, an

order of magnitude larger than a similar exam for an adult.9

There are many national and international committees and organizations dedicated

in whole or in part to quantifying the risks of both non-stochastic and stochastic effects

due to radiation exposure. These include, to name a few, ICRU, NCRP, BEIR, and the

ICRP. They draw their conclusions (on which regulations are based) from several data

sets, including the Japanese bomb survivors, medical exposures, animal data, and

occupational exposures. Much useful information can be gleaned from these

conclusions, and a look at that information follows.

Radiation risk varies dramatically with age, and there is also a clear gender

difference that is more pronounced at early ages, with females being more radiosensitive

than males.10 This can be seen more clearly in Figure 1-1.

It is apparent from Figure 1-1 that for pediatric patients, the risk for induction of a

fatal cancer ranges from 10-15% per Sv (however, this risk will not be expressed until

later in lifeo1, while for middle-aged adults this risk falls to around 2-4% per Sv. In

addition, A-bomb survivor data hve now "matured," and the 35,000 A-bomb survivors

who received doses lower than 0.25 Sv have been studied." The results show a small but

statistically significant excess incidence of cancer at doses down to 50 mSv, overlapping










the range of organ doses and effective doses involved in helical CT. Also, as can be seen

from Figure 1-2, the risk of solid cancers appears to be a linear function of dose.9


r
I,
E




.4-


k,


Population averages


Female
Male


. .. .- --- ........... ...................-- -- -- ...........

".......Females
Males *.

0 50 100
Age at Time of Exposure


Figure 1-1. The attributable lifetime risk from a single small dose at various ages at the
time of exposure, assuming a dose and dose-rate effectiveness factor
(DDREF) of 2. The higher risk for the youngest age group will not be
expressed until later in life. Note the dramatic decrease in risk with increasing
age. From ICRP.10


Dose range for
pediatricCT Dose (mSv)
0 25 r 50 75 1


)0 125 150


co



~lI
9 0.




w


0.0 2.5 5.0 7.5 10.0
Dose (rem)


12.5 15.0


Figure 1-2. Estimated radiation-related excess relative risk (and standard error) for solid-
cancer mortality among A-bomb survivors. Also shown is the range of organ
doses characteristic of helical CT. From Hall.9










Finally, Figure 1-3 shows the risk of fatal cancer induction per 10,000 scans at 200

mAs for both abdominal CT and head CT.12



12 -

10- Abdominal CT







8 Head CT




0 10 20 30 40 50 60 70 80 90
C 0

6 -
.-6 U
cS 4-






Age at CT (y)

Figure 1-3. Lifetime attributable cancer mortality risk as a function of age at examination
for a single typical CT examination of head and abdomen. Dose and therefore
risk are proportional to mAs and can be scaled accordingly. Note the rapid
increase in risk with decreasing age. Adapted from Brenner et al.12

Pediatric Radiology

As mentioned in the Introduction, the field of radiology has enjoyed tremendous

advancement and progress over the past decade. Pediatric radiology, in particular, has

also benefited from technological advances in radiology, ranging from the ability to

diagnose congenital defects and repair them to the virtual elimination of the need for

sedation in young patients undergoing CT exams. However, these advancements have

not come without a cost. That cost is dose. CT involves doses orders of magnitude

greater than those involved in conventional radiography, and as mentioned in the

previous section, with these increased doses comes increased risk.









Doses to Pediatric Patients

Pediatric patients are frequently subjected to higher average organ, and therefore

higher effective doses (leading to a higher risk for radiation effects), than adult patients

for a number of reasons. First, children are much more radiosensitive than adults due to

their rapidly growing body tissues.13 This makes young children 10-15 times more likely

to develop a radiation-induced malignancy than an adult.10 Second, a child's skeleton is

comprised of a greater percentage of active bone marrow, a highly radiation-sensitive

organ, than adults.'4 Third, the greater post-exposure lifetime of infants and children

increases the possibility of the manifestation of radiation-induced effects.15 Fourth,

pediatric patients generally have a larger fraction of their anatomy located within an X-

ray field compared to adults, and their organs are spaced more closely together, resulting

in significant irradiation of organs outside the primary beam. Fifth, pediatric patients are

often uncooperative, and therefore faster (and higher dose) scanning modalities such as

helical CT are used more frequently. Finally, for a given procedure, the effective dose is

larger in smaller patients than in adults, as demonstrated by Ware et al.16 and Nickoloff et

al.17

The convenience and ease of use of CT have led to the unfortunate consequence

that little attention is paid to adapting examination protocols developed for adult patients

to better suit pediatric patients, the result being much higher doses to pediatric patients

than is necessary to achieve acceptable image quality.7 This is seldom noticed because

there is no clinical consequence for using too much radiation in CT, unlike plain-film

radiography.









Strategies for Reducing Doses to Pediatric Patients

A starting point for the reduction of dose to pediatric CT patients is the obvious set

of parameters that have a direct effect on dose. As an example, dose is directly

proportional to the tube current-time product (mAs) in any imaging modality, including

CT. Also, although it is not as plain to see, dose decreases with increasing pitch8 and

dose also decreases with decreasing kVp.17-19 Other factors directly affecting dose in

pediatric CT are beam collimation, scan mode, and gantry cycle time (directly related to

the tube current-time product). The effects of the interplay of all of these parameters can

be tested directly by performing experiments while varying the parameters.

However, in addition to the above parameters that directly affect CT doses in

known ways, there are other approaches to dose reduction in pediatric CT. Perhaps the

easiest place to start is simply judicious use of CT.20 It is estimated that perhaps 40% of

all pediatric CT examinations are not clearly indicated.21 Frush mentions that it is

impossible to determine the appropriateness of every CT examination, but gives several

potential strategies to minimize the number of unnecessary CT examinations, including

good communication between radiologists and pediatric care providers (consultations that

lead to alternative exams, etc.) and periodic reviews of CT requests that can lead to

recommendations and advice for those who consistently order poorly indicated

examinations.20 Another suggestion made by Frush under the heading of judicious use of

CT is the limitation of exams to the area in question, i.e., minimizing the length of

coverage.20'22 Finally, Frush also suggests that multi-phase scanning is overused at many

institutions, indicating that it is only necessary (at most) 5% of the time. He also states

that when necessary, multiphase examinations should adjust scan parameters (mAs, kVp,

pitch, etc.) at each phase to minimize radiation dose.2022 It is apparent from these









statistics and comments that a vast reduction in the dose delivered to pediatric patients

during CT can be achieved before the technologist touches the control console of the CT

scanner.

Patient Simulation in Diagnostic Radiology

Patient simulation in diagnostic radiology takes on various forms, from the very

basic (single material and simple geometry) to the incredibly detailed (anthropomorphic

or tomographic phantoms constructed from tissue-equivalent materials). This section

will give an overview of the various methods that have been used to simulate patients for

dose measurement in diagnostic radiology, with particular focus on the simulation of

pediatric patients.

Tissue-equivalent Materials

The five most common types of tissue-equivalent materials in diagnostic radiology

are soft tissue, lung tissue, bone tissue, adipose breast tissue, and glandular breast tissue.

The most frequently used tissue-equivalent materials in diagnostic radiology are those

that are both easy to work with and relatively inexpensive, including acrylic (PMMA),

water, air, aluminum, and copper. Extensive sets of tissue-equivalent materials have been

developed by individuals, the most notable being White,23,24 and White et al.25 Also, an

exhaustive list of existing tissue-equivalent materials has been compiled by the ICRU.26

However, pediatric tissue simulation is performed exclusively with generic materials

used to represent patients of all ages, including acrylic, water, air, and aluminum.

However, this is not an optimal method of simulating pediatric body tissues, as shown by

Jones et al. in their work with tissue substitutes for use in pediatric radiology.2 A

complete set of tissue-equivalent substitutes were developed as a result of his work, for

use with both pediatric patients, and patients of other ages.









Patient Phantoms

As mentioned previously, patient phantoms range from very basic acrylic cylinders

to very complex anthropomorphic phantoms. While several adult phantoms exist for use

in diagnostic dosimetry, the current state of pediatric phantoms will be discussed in this

section, as this is the focus of this research.

The most common type of pediatric phantom is a simple acrylic cylinder.

Nickoloff et al. have constructed a series of phantoms to represent patients of various

ages (6, 10, 16, 24, and 32 cm diameter acrylic cylinders) and examined the effect of

patient size on dose, and found that dose increases with decreasing patient size.1 Also,

one of the most commonly used practices to measure doses to pediatric patients is the use

of the 16 cm adult head phantom from the current AAPM protocol28 to represent a

pediatric body.

A second category of pediatric dosimetry phantoms includes those that involve

unique geometries or a combination of several simple geometries. Phantoms in this class

include a neonatal phantom constructed by Jones et al. from eight 1 cm thick acrylic

sheets with air spaces machined in each sheet to represent lungs29 and a block-style

phantom constructed by Duggan et al. including a head and torso made from water-

equivalent material and a lung-equivalent insert in the torso.30

A third class of pediatric dosimetry phantoms is characterized as anthropomorphic.

These phantoms are intended to more closely represent the true external and internal

anatomy of the human body. Examples of phantoms in this class are those representing

0, 2, 6, and 12-year-old children constructed by Giacco et al.31 from various cylindrical

shapes representing the head, torso, arms, legs, and lungs of the patients. These

phantoms were constructed by forming an acrylic shell which was then filled with









deionized water (the lungs remain air-filled). Also included in this class are stylized

newborn and 1-year-old phantoms based on the MIRD geometry constructed using

tissue-equivalent materials at the University of Florida (UF).2 Finally, perhaps the best

example of phantoms in this class are the those developed by Varchena et al.,33,34 which

comprise a set of tissue-equivalent anthropomorphic phantoms of various ages: 0, 1, 5,

10, and 15 years old. While these phantoms provide detailed modeling of pediatric

patients' exterior, skeletal, and lung anatomy, they are not based on corresponding real

patient CT or MR image data sets.

Dosimetry in Diagnostic Radiology

Diagnostic dosimetry incorporates a variety of dosimeters and dosimetric

quantities, which will be discussed in detail in the following sections. CT dosimetry is a

specialized application of diagnostic dosimetry, involving some of its own quantities.

Dosimeters in Diagnostic Radiology

There are many types of dosimeters available for use in diagnostic dosimetry,

including, but not limited to, ionization chambers, thermoluminescent dosimeters

(TLDs), metal-oxide semiconducting field-effect transistors (MOSFETs), diodes,

optically-stimulated luminescence (OSL), and fiber-optic-coupled dosimeters. Dose-

area-product meters are also used, but to a lesser extent, and are not used in CT. These

various dosimeters will be examined below in regards to their individual advantages and

disadvantages.

The ionization chamber is the gold standard in radiation dosimetry, and is mainly

used for various computed tomography dose index (CTDI) measurements, as well as

dose-length product (DLP) measurements. Advantages include high sensitivity, excellent

linearity, uniform energy response, and little to no fading. However, ion chambers are









generally fairly large in size, and therefore are not suitable for making many in-phantom

measurements simultaneously. Also, they are expensive and must be sent away for

periodic calibration.

The thermoluminescent dosimeter (TLD) is by far the most common dosimeter

used for in-phantom dosimetry in diagnostic radiology. Its small size makes it very

useful for in-phantom measurements, as dose measurements in many different locations

can be made simultaneously. The TLD also exhibits an angularly independent response,

and is fairly tissue-equivalent. There are many different types of TLDs available, and

depending upon the application, a TLD with a uniform response and good linearity and

reproducibility over the desired energy range can be found. TLDs are subject to some

fading, but this should not be a problem if they are read soon after they are exposed.

However, TLDs also have their shortcomings. TLDs are very small and difficult to

handle, and must be annealed prior to each use. Also, a time-consuming reading process

(using a suitable TLD reader) must be carried out after the TLD has been exposed. TLD

response may vary significantly with reading parameters (e.g., temperature) as well. In

addition, care must be used when handling TLDs to avoid contamination with dirt or oil

from the skin, which can affect the response of the TLD after it has been calibrated.

Various types of TLDs have been used in phantom studies, including LiF:Mg,Ti3536 and

LiF:Mg,Cu,P,3031 with LiF:Mg,Cu,P being preferred due to its extremely high sensitivity

(its lower measurement limit being 1 itGy30), however, it does under-respond by 10% to

20% at energies less than 20 keV and between 80 keV and 300 keV.30

Diodes have not found their way into experimental use in diagnostic radiology as of

yet, but there are advances being made towards that goal.37 38 Aoyama et al. have









developed a pin silicon photodiode detector that has a sensitivity of approximately 0.02

mGy (20 iGy) and a linear output up to doses of 12 mGy.39 However, it can also be seen

from their work that there is a significant energy dependence of the response of their

dosimeter, as well as a non-uniform angular response.

Optically-stimulated luminescence (OSL) is the term given to the use of A1203:C as

a radiation detector.4042 OSL material is not heated to release the energy stored in traps

due to radiation deposition events; instead, pulsed laser light is used. OSL has a lower

sensitivity limit of approximately 0.01 mGy (10 jiGy).40-42 It also possesses all the

benefits of TLDs without the hassle of annealing. However, it cannot be exposed to light

(except very long wavelength red light), as this will release some of the energy stored in

the traps in the material. OSL is currently only available in sheets (powder deposited on

a plastic substrate) or discs and can only be read using readers owned by companies

dealing in radiation protection dosimetry. However, a real-time OSL reader utilizing rods

of OSL material coupled to fiber-optic cables is in production, but no timetable is

available for its release to the research community.43

The MOSFET dosimeter is also a (relatively speaking) newcomer to the diagnostic

dosimetry field. While it has been well-established as a useful dosimeter in

radiotherapy,4450 its examination for use as a dosimeter in diagnostic applications has just

begun to be investigated.15'51-55 MOSFETs incorporate some of the advantages of TLDs,

including excellent linearity,o5'5 tissue-equivalency (better than TLDs56), and

reproducibility at higher doses (9.5% at 35 mGy/fx to 1.2% at 2.5 Gy/fx,50 and reportedly

even better with the new generation of MOSFET dosimeters). MOSFET dosimeters have

exhibited evidence of post-exposure fading;51 thus it is important to read the dosimeter at









a consistent time after each irradiation. Also, it has been determined that previous

generations of MOSFET dosimeters have exhibited an overresponse from slightly above

a factor of 3 (- 50 keV)56 to slightly above a factor of 4 (33 keV)57 when normalized to

the response of the dosimeter to 6 MV photons. It has also been reported that the

manufacturer released unpublished data which show an overresponse by a factor of 4.4 at

a mean x-ray energy of 45 keV.57 However, MOSFET dosimeters can claim an

advantage over TLDs due to the fact that they can be read immediately after exposure,

and can therefore be utilized in a near real-time dosimetry system. Also, there is no need

for annealing or any sort of post-processing after exposure and reading. In addition, the

dose history is retained in the dosimeter due to the build-up of space charge.45'5 This

does contribute to a reduction in response with increasing dose history, but only by a

small amount (~1%/V).56

Another newcomer to the field of diagnostic dosimetry is the fiber-optic-coupled

dosimeter (FOC).59-63 The active area of these dosimeters is constructed from fused-

quartz glass doped with Cu+ ions. Like OSL, they can be read using a photomultiplier

tube (PMT), however, FOC dosimeters are phosphorescent detectors, requiring no

stimulation for reading, and the output can be integrated over the irradiation time, making

these dosimeters true real-time dosimeters. More details about the construction and

operation of FOC dosimeters can be found in Justus et al.59-61 and Huston et al.62-63

Dosimetric Quantities in Diagnostic Radiology

A wide range of quantities is used to either describe the doses received by a patient

during an exam or provide some sort of estimate or relationship by which to derive the

dose received by a patient. Amongst these quantities are energy imparted, dose-area

product (DAP), dose-length product (DLP), computed tomography dose index (CTDI),









entrance surface dose (ESD), and effective dose (E) (CTDI and DLP are used exclusively

in CT, while DAP is not used in CT). While the first four of these quantities are easily

calculated or measured, effective dose is the only quantity that can actually be used to

quantify the risk to a patient from a diagnostic exam.1" As a matter of fact, many of the

previously mentioned quantities are used as a starting point to derive a value for effective

dose in the absence of measured organ doses. Chapple et al. have derived DLP/E values35

using the phantoms manufactured by Varchena et al.3334 Huda et al. have derived E/unit

energy imparted factors64'65 from Monte Carlo simulations. Finally, Pages et al. have

calculated relationships between DLP, CTDIw, and E using normalized organ specific

dose factors determined for pediatric mathematical phantoms using Monte Carlo.66

However, it is well known that "The effective dose cannot be easily derived from

other dose descriptors."6 The effective dose can, however, be calculated from measured

average organ doses. Effective dose is given by the following expression:

E= wr D (1-1),
T
where w, is the tissue weighting factor for tissue T and D, is the dose to tissue T (this

form of the expression assumes a radiation weighting factor of 1).10 Table 1-1 shows a

list of the currently accepted tissue weighting factors.

Therefore, by measuring the appropriate organ doses, one can calculate the

effective (whole body) dose to a patient and attribute some excess risk of cancer mortality

due to a radiation exposure. The feasibility of measuring organ doses in phantoms in

order to calculate effective dose has been demonstrated by Hintenlang et al.15 and

Chapple et al.35










Table 1-1. Tissue weighting factors from ICRP 6010
Tissue or organ Tissue weighting factor, wT
Gonads 0.20
Bone marrow (active) 0.12
Colon 0.12
Lung 0.12
Stomach 0.12
Bladder 0.05
Breast 0.05
Liver 0.05
Esophagus 0.05
Thyroid 0.05
Skin 0.01
Bone surface 0.01
Remainder 0.051,2
1Remainder is composed of : adrenals, brain, upper large
intestine, small intestine, kidney, muscle, pancreas, spleen,
thymus, and uterus.
2In cases in which a single remainder tissue or organ receives
an equivalent dose in excess of the highest dose in any of the
twelve organs for which a weighting factor has been specified,
a weighting factor of 0.025 should be applied to that tissue
or organ and a weighting factor of 0.025 to the average dose
in the rest of the remainder as defined above.


Image Quality Assessment in Diagnostic Radiology

Image quality assessment in diagnostic radiology is fairly straightforward, but

when extended to CT can become very complicated. Therefore, this section will be

divided into two separate discussions. The first will discuss image quality assessment

techniques in general in diagnostic radiology, focusing mainly on projection radiography.

Another section will follow, discussing the extension of the previously examined

methods to CT.

Methods of Image Quality Assessment in Diagnostic Radiology

Image quality in diagnostic radiology is not determined by a single aspect, but

instead is determined by the product of several factors, including spatial resolution,

contrast, image noise, and the presence/absence of any distortion or artifacts. There is a









variety of image quality assessment techniques used in diagnostic radiology. Some of

these techniques examine one aspect of image quality, while others seek to quantify

image quality by examining two or more aspects of image quality simultaneously.

Several of the techniques used to assess image quality will be discussed in the following

section.

The contrast-detail phantom (such as the ACR mammography phantom) is one

tool for assessing image quality that attempts to examine each aspect of image quality.

Phantoms such as these contain small specks or fibers that are used to assess spatial

resolution, low-contrast objects (e.g., discs) to assess image noise and contrast (or

contrast-to-noise ratio, CNR), and the image of the phantom can also be examined for the

presence of any artifacts or image distortions. This method can be used to qualitatively

assess image quality, or a quantitative assessment can be generated by calculating a total

"score" from all of the individual elements. Also, these phantoms often incorporate

acrylic or some other form of tissue-equivalent material to simulate imaging through the

appropriate thickness of a patient's anatomy.

Other types of phantoms are also used for image quality assessment in diagnostic

radiology. These include spatial resolution phantoms such as line pair phantoms and

contrast phantoms that contain low-contrast objects of various sizes and contrast levels.

One commonly used phantom is the Leeds Test Objects, including the TOR[CDR], which

incorporates both line pair phantoms and low-contrast discs.a Also, there are other

phantoms used for qualitative assessment of image quality on the market that seek to

simulate some part of a patient's anatomy (hand, lung, etc.).


a Leeds Test Objects Ltd, Wetherby Road, Boroughbridge, North Yorkshire, YO51 9UY, UK









Another common method of assessing image quality is the measurement of the

modulation transfer function (MTF) of an imaging system. The MTF is a graphical

representation of how well an imaging system preserves contrast at increasing spatial

resolution. MTFs are derived by measuring the line-spread function (LSF) of an imaging

system (frequently done using edges or wires), then taking the Fourier transform of the

LSF, which is the optical transfer function (OTF) of the system. The real part of the OTF

is the MTF. The MTF is a convenient graphical description of the performance of an

imaging system.

Computational Observers and Computer-Aided Diagnosis (CAD)

A recent development in diagnostic radiology is the use of computational

observers. Among the most popular computational observers is the channelized

Hotelling observer (CHO), which seeks to predict and mimic human visual performance.

Similar to a neural network, these observers are "trained" by giving them information

about the signal for which they are searching. The Hotelling observer is also general

enough to include all sources of randomness, including background and noise.68

However, the use of Hotelling observers has been confined to ideal situations (i.e.,

artificially generated images) for the most part, and is frequently examined for use in

SPECT and PET imaging. Computer aided-diagnosis is also beginning to be examined

for use in diagnostic radiology, mainly in mammography and CT (specifically in lung

imaging). Various studies have examined computerized schemes,69 model-based

detection,7" or computer-aided diagnosis (CAD)1-74 as possible surrogates for radiologists

or to supplement radiologists. Many of these techniques use some sort of thresholding in

order to narrow the number of possibilities to a feasible range. However, the "training"

of the computers used in these studies can require enormous amounts of data in order to









reach reasonable detection rates,7" and some of the techniques are meant only to

supplement or aid radiologists7 or require a radiologist to identify a region containing a

possible abnormality in order to narrow the ROI to allow for the computer to search the

area in a reasonable amount of time.69

Human Observer Studies

Perhaps the most widely used (and widely accepted) quantitative measure of image

quality is the receiver-operating characteristic (ROC) study. The basic premise of an

ROC study is as follows: an observer (one of many) is presented with a series of images

that may or may not have a signal present. The observer is then asked to rank his

confidence about the presence of a signal on a scale such as "definitely not present,"

"probably not present," "not sure," "probably present," or "definitely present." Using

this data, values for the observer's sensitivity (true positive fraction, or TPF) and (1-

specificity) (false positive fraction, or FPF) can be calculated at various decision

thresholds. This, in essence, yields values for the performance of an imaging system (or

alternatively, an observer's performance) at several different decision thresholds, which

can then be plotted. The area under the ROC curve, Az, can then be used to quantify the

performance of an imaging system, or to compare the performance of several imaging

systems.

A close relative of the ROC study is the "two-alternative forced-choice" (2-AFC)

method. The meaning of the area under the ROC curve, Az, is actually given in terms of

the results of a 2-AFC technique.75 It can be shown that the expected fraction of correct

decisions in the 2-AFC experiment is equal to the expected area under the ROC curve

that would be measured with the same images viewed one at a time in a conventional

ROC experiment.75' The 2-AFC experiment utilizes pairs of images, one containing









only noise and the other containing some known signal on the noisy background. An

observer is then presented with various pairs of images, and asked to specify which of the

two images contains the signal. Then, Az can be estimated by simply calculating the

fraction of the pairs of images where the signal was identified correctly. However, the

ROC technique has a distinct advantage over the 2-AFC technique. It can be shown that,

in order to obtain a similar level of confidence, one would need almost twice the number

of images if using a 2-AFC technique versus an ROC technique.5

One final observer study is also related to the 2-AFC experiment. The "M-

alternative forced-choice" (M-AFC) technique is a more general extension of the 2-AFC

experiment. An observer is presented with an image which displays a known signal in a

known position. Within the image, there are M locations that could possibly contain the

same signal. The signal is present in one of these locations, and absent in the other M-1

locations. The observer is then asked to identify which of the locations contains the

signal.79 Aufrichtig provides an excellent demonstration of the use of an M-AFC

experiment.8

Hybrid Methods for Assessing Image Quality

Hybrid methods for assessing image quality provide some of the advantages of

both computational observers and human observers, while eliminating some of the

drawbacks associated with each. While computational observers are ideal for computer-

aided diagnosis, they are not ideal for image quality studies, due to the fact that

ultimately a human observer will be making decisions regarding clinical images. Along

similar lines, it is often not feasible to use human observers for research studies involving

image quality assessment due to the large number of images to be read, and the

associated time commitment required of the professional staff. In addition, the strain on a









clinical CT tube from performing hundreds of scans for a 2-AFC or M-AFC study must

also be considered. This is where the advantages of a hybrid method for image quality

assessment become apparent.

The best, and perhaps only, example of an existing hybrid method is that used by

Pitcher to examine image quality in pediatric computed radiography.81 This method

utilized human observers to determine a threshold contrast-to-noise ratio (CNR) that

could be detected in a phantom image, then applied this threshold in a software program

that was used to automatically score contrast-detail phantom images based on the

calculated threshold CNR.

Objectives of this Research

The objectives of this research are described below. Together, these objectives,

and the work towards achieving them, will lead to the identification of low-dose CT

scanning protocols that maintain adequate image quality while significantly reducing the

radiation dose delivered to pediatric patients.

1. Identify a suitable dosimeter for use in a dosimetry phantom system, and test the
components of the system, including the dosimeters and the tissue-equivalent
materials (previously developed at UF by the author2).

2. Construct a physical, tomographic newborn dosimetry phantom with an
incorporated real-time dosimetry system. The phantom will be constructed from
tissue-equivalent materials previously developed by the author.2 Effective dose is
the most accurate and widely-accepted method for calculating the risk of stochastic
effects from radiation exposure, and this phantom will allow the calculation of
average organ and effective doses when coupled with point-to-organ dose scaling
factors that are currently being developed and have been explored previously.82 It
is hypothesized that this phantom will provide the most accurate average organ and
effective dose measurements to date for neonate patients undergoing diagnostic
exams.

3. Develop the tools necessary to quantitatively assess image quality (low-contrast
detectability, in particular) in computed tomography (CT) imaging. Dose reduction
is prudent only to the point where satisfactory images result, i.e. images that
provide adequate diagnostic information. These tools will allow for the automated









scoring of phantom images, and will be based on trained observers' (i.e.
radiologists') decision thresholds.

4. Use the tools described in (3) and (4) to identify low-dose protocols in CT, and
select those low-dose protocols that maintain adequate image quality.

The remainder of this dissertation describes in detail the methods used to achieve

the goals listed above, the results obtained from this work, conclusions that can be drawn

from this work, and suggestions for future improvements and further work. It is divided

into chapters based on papers either already published in peer-reviewed journals, or

papers to be submitted to peer reviewed journals, with the exception of the Introduction

and Conclusion chapters.

Several appendices have been included in addition to the main body of work.

These appendices provide information that is unsuitable (e.g., bitmap images of the

phantom slices, long computer codes, etc.) for publication in peer-reviewed journals. The

absence of this information from the chapters themselves does not hinder the

understanding or reading of each chapter. However, this information may be of interest

to those in the scientific community reading this dissertation, and will certainly be useful

for those following in my footsteps on this project.














CHAPTER 2
MOSFET DOSIMETER DEPTH-DOSE MEASUREMENTS IN HETEROGENEOUS
TISSUE-EQUIVALENT PHANTOMS AT DIAGNOSTIC X-RAY ENERGIES

Introduction

The MOSFET dosimeter is a relatively new device in the diagnostic dosimetry

field. MOSFET dosimeters are currently widely used for measurements of patient

absorbed dose in radiation therapy, and are increasingly utilized in interventional

radiology. Due to their small size, they do not perturb the radiation field during

measurement, and multiple dosimeters may be placed at several locations simultaneously.

The active area of a MOSFET dosimeter is 0.04 mm2 with a thickness of less than 2 atm,

and the overall size, including the epoxy bubble, is less than 4 mm2.83 While it has been

well-established as a useful dosimeter in radiotherapy,44'45,47-50 its use as a dosimeter in

diagnostic applications has only recently been investigated.15,51-53,55,84 Past generations of

MOSFET dosimeters have demonstrated many of the advantages of thermoluminescent

dosimeters (TLDs), including excellent linearity,50'51 tissue-equivalency (better than

TLDs),56 and reproducibility at high doses (9.5% at 35 mGy/fx to 1.2% at 2.5 Gy/fx).50

MOSFET dosimeters have exhibited evidence of post-exposure fading; however, this

behavior has improved with subsequent generations of dosimeters. It is still good

practice to read the dosimeter following a consistent time interval after each irradiation.

It has also been reported that previous generations of MOSFET dosimeters have

exhibited an over-response from slightly above a factor of 3 (-50 keV)56 to slightly above

a factor of 4 (33 keV)57 when normalized to the response of the dosimeter to 6-MV









photons. Calibrating the dosimeters at the energies for which they will be used typically

solves this problem. However, MOSFET dosimeters can claim a significant advantage

over TLDs as they may be read immediately following exposure. Furthermore, there is

no need for annealing or post-processing following exposure and reading. In addition,

the dose history is retained in the dosimeter due to the build-up of space charge.45'58 This

does contribute to a reduction in response with increasing dose history, but only by a

small amount (-1%/V).56 In a recent paper by Sessions et al.,82 the potential use of

MOSFET dosimeters for making measurements of tissue dose within heterogeneous

stylized phantoms was described for dose assessments in pediatric radiology.

The objective of the current study was to explore the use of MOSFET dosimeters

for measuring tissue depth-dose at diagnostic photon energies in both homogeneous and

heterogeneous tissue-equivalent materials. Simple cylindrical phantoms were employed

as a prelude to more complex measurements in a tomographic physical phantom. Monte

Carlo radiation transport was used to determine values of tissue point-dose at depth,

against which the MOSFET dosimeter measurements were then compared.

Materials and Methods

The experimental setup employed in this study utilized tissue-equivalent substitutes

developed specifically for use at diagnostic photon energies, and reported previously by

our research group.27 These tissue substitutes were designed to mimic the radiation

attenuation and absorption properties of newborn reference tissues as defined by Cristy

and Eckerman for the Oak Ridge National Laboratory (ORNL) series of stylized

anatomic models.85









Cylindrical Phantoms

A modular cylindrical phantom was manufactured for this study comprised of a

series of 5-cm diameter discs, each 1 cm in thickness. The tissue-equivalent materials

used to manufacture the discs included STES-NB (soft tissue substitute for the newborn),

BTES-NB (bone tissue substitute for the newborn), and LTES (lung tissue substitute).27

In addition, channels were machined into one disc of each material to allow for consistent

positioning of the MOSFET dosimeters. Manufacturing the phantoms in this way

allowed for the creation of any desired experimental setup. Three phantom

configurations were chosen for evaluation and they are shown schematically in Figure 2-

1.

Direction of x-ray beam


4 1 !
SSSSSSS SSBBSSS SBLLBSS









STES-NB BTES-NB LTES

Figure 2-1. Schematic of the three phantom configurations used in the depth-dose study.

Each configuration had a total thickness of 7 cm, allowing for measurements

ranging from the phantom surface to a tissue depth of 6 cm (e.g., top of the seventh and

final disc). Phantom SSSSSSS thus indicates seven contiguous slices of soft-tissue

equivalent discs, while phantom SSBBSSS denotes a stack of two soft-tissue discs,









followed by two discs of bone-equivalent material, and then three discs of soft-tissue

equivalent material. In a similar manner, phantom SBLLBSS represents a heterogeneous

stack of either soft-tissue (S), bone (B), or lung (L) tissue-equivalent discs.

MOSFET Dosimetry System

The system used in this study was a MOSFET dosimetry system manufactured by

Thomson and Nielsen Electronics, Ltd.a and consisted of the TN-RD-60 patient dosimetry

system (including dual-sensitivity bias supplies used at their high sensitivity setting) in

conjunction with the TN-1002RD dosimeter, the most current dosimeter in the isotropic,

high-sensitivity line of MOSET devices from this company.

It is our recommendation that the MOSFET dosimetry system be calibrated using

the x-ray energies that will be used during the experiment, as the response of the

MOSFET dosimeter can vary across the energies encountered in diagnostic radiology.

Consequently, the following calibration protocol is used by the Pediatric Organ Dose

(POD) research group at the University of Florida.

When used for experiments with diagnostic x-ray energies, the dosimeters are

calibrated using a pancake ionization chamber. We are currently using a Keithley Model

96035B dual entrance window ionization chamber along with a Keithley Model 35050A

electrometer.b Both the MOSFET dosimeters and the ionization chamber are placed on

some form of tissue-equivalent backscatter material (STES-NB, acrylic, etc.) and

irradiated simultaneously until an exposure of-1 R is accumulated within the ionization

chamber. The MOSFET dosimeters are then read, and calibration factors are assigned.



a Thomson and Nielsen Electronics Ltd., 25E Northside Road, Nepean, Ontario, Canada, K2H 8S1

b Keithley Instruments Inc., 28775 Aurora Road. Cleveland, Ohio 44139









This process is then repeated several times to determine a reliable calibration factor for

each individual dosimeter (this process is simplified by the software included with the

dosimetry system and the user simply needs to input the exposure accumulated within the

ionization chamber).

Experimental Parameters

Experimental data for this study were acquired using a diagnostic x-ray tube at 66

kVp a tube potential selected from technique charts at Shands Hospital at the University

of Florida as representative of those appropriate for newborn patients. The tube was

characterized and found to have a half-value layer of 2.35 mm of Al and 5% voltage

ripple at 66 kVp. A tube current of 200 mAs was used throughout the study to provide

better measurement statistics in a shorter period of time.

Nine repeated measurements were taken at each of seven depths in the cylindrical

phantom (0, 1, 2, 3, 4, 5, and 6 cm) for each of three phantom configurations shown in

Figure 2-1. An interval of 10 seconds was allowed to elapse following each irradiation

prior to the dosimeter read (this was also the case during dosimeter calibration). By

reading the dosimeters at a consistent post-irradiation time, variations in experimental

data due to charge fading or ramping of the voltage are minimized. However, neither of

these phenomena appears to be a significant issue with current generations of MOSFET

dosimeters.

Point estimates of tissue absorbed dose can be inferred from MOSFET dosimeter

measurements using the following relationship:


Dtissue-MOSFET mx CF x kexposure-to-kenna (en/P)tssue (2-1),
(len /P)air









where m is the MOSFET response (in mV), CF is the energy-dependent MOSFET

calibration factor (C kg-1 mV-1), k is the conversion factor from exposure to air kerma

(mGy per C kg-1), and (Pen/P)air and (pn/p) tissue are the mass energy-absorption

coefficients for air and soft tissue, respectively, weighted over the x-ray energy spectrum

incident upon the cylindrical phantom.

Monte Carlo Simulations

In order to estimate actual point-values of tissue absorbed dose within the physical

phantom used for the MOSFET measurement, a series of Monte Carlo simulations were

also performed using the radiation transport code MCNP5.86 X-ray energy spectra (66

kVp) used as input to MCNP5 were obtained using the TASMIP tungsten anode spectral

model of Boone and Siebert.8 Point estimates of tissue dose were calculated in MCNP5

by tallying the energy deposited by photons and their secondary electrons within 1-mm

diameter spheres located at the lower edges of each simulated tissue-equivalent discs of

the cylindrical phantoms of Figure 2-1. The material composition of each sphere was

identical to that of the disc in which it was positioned. Again, the purpose of these

simulations was to provide "true" estimates of dose versus depth for comparison of the

point doses measured by MOSFET dosimetry, and not to computationally model the

microelectronic structure and irradiation response of each MOSFET dosimeter.

Consequently, various problems in modeling the dosimeter geometry, its material

composition, and energy deposition events with the small sensitive volume of the

dosimeter"8 were circumvented.










Results

In Figure 2-2, values of absorbed dose are plotted as a function of depth in the

homogeneous soft-tissue phantom as calculated via Monte Carlo simulation (open

circles). These values are then compared to measured values of point dose as given by

the MOSFET dosimeters.


0 1 2 3
Depth (cm)


4 5


0 1 2 3 4 5 6
Depth (cm)

Figure 2-2. Comparison of measured and simulated tissue absorbed dose with depth
within the homogeneous soft tissue phantom. Two orientations of the
MOSFET dosimeters were considered: (A) Epoxy bubble facing the x-ray
beam, and (B) Flat side of the dosimeter facing the x-ray beam.


A Phantom SSSSSSS


a







Measurement bubble side facing beam
o Monte Carlo simulation
III









Experimental data points (closed circles) represent the mean + one standard

deviation from nine replicate measurements. The dose resolution of the dosimetry

system was determined to be -0.3 mGy, based on the MOSFET dosimeter sensitivity and

the voltage resolution of the reader. In Figure 2-2A, the dosimeters were positioned at

each depth such that the epoxy bubble side of the dosimeter faced the incident x-ray

beam. In Figure 2-2B, the dosimeter positions were reversed such that the flat side of the

dosimeter at each depth faced the incident beam. Similar comparisons of simulated and

measured point doses as a function of depth are given in Figure 2-3 for the heterogeneous

phantom SSBBSSS and in Figure 2-4 for the heterogeneous phantom SBLLBSS. In each

case, the calibration factors of Equation 2-1 were determined with the dosimeters

positioned with the epoxy bubble side facing the x-ray beam as recommended by the

manufacturer.c'd

Discussion

The data of Figures 2-2A, 2-3A, and 2-4A indicate that at depths exceeding 2 cm,

strong agreement is seen between measured and simulated values of point tissue dose

within both the homogeneous and heterogeneous phantoms regardless of the dosimeter

orientation. However, when the MOSFET dosimeters are positioned with the epoxy

bubble facing the x-ray beam, measured values of tissue absorbed dose consistently fall

below simulated values within the first 2 cm of each cylindrical phantom. The maximum

percent difference was noted to be 21% at a depth of 1 cm within the homogeneous

phantom of Figure 2-2A. However, when the 0 to 2 cm depth measurements were

repeated with the flat side of the dosimeters facing the x-ray beam, the agreement

c http://www.thomson-elec.com/downloadables/m20calibration.pdf
d 11hp \ \\ \\ .thomson-elec.com/downloadables/ascalibration.pdf










between measured and simulated point doses improved significantly within the

homogeneous SSSSSSS and heterogeneous SSBBSSS phantoms.


7 .
A Phantom SSBBSSS


0 5-





0





0 1 2 3 4 5 6
Depth (cm)
2

_u 2-3. Measurement bubble side facing beam







B Phantom SSBBSSS


a)
05








c1 ng* tMeasurement bflat side facing beam te
0 Monte Carlo simulation

0 1 2 3 4 5 6
Depth (cm)
within the heterogeneous phantom SSBBSSS
(3 5
g i




5

1 Measurement flat side facing beam
o Monte Carlo simulation

0 1 2 3 4 5 6
Depth (cm)

Figure 2-3. Comparison of measured and simulated tissue absorbed dose with depth
within the heterogeneous soft tissue and bone tissue phantom. Two
orientations of the MOSFET dosimeters were considered: (A) Epoxy bubble
facing the x-ray beam, and (B) Flat side of the dosimeter facing the x-ray
beam.












A Phantom SBLLBSS


6
E
5o
04

3
0
.1 2 -

1 I Measurement bubble side facing beam
0 Monte Carlo simulation

0 1 2 3 4 5 6
Depth (cm)


B Phantom SBLLBSS



E
6


04
1 4
3
0
$ 2
S Measurement flat side facing beam
o Monte Carlo simulation

0 1 2 3 4 5 6
Depth (cm)


Figure 2-4. Comparison of measured and simulated tissue absorbed dose with depth
within the heterogeneous soft tissue, bone tissue, and lung tissue phantom.
Two orientations of the MOSFET dosimeters were considered: (A) Epoxy
bubble facing the x-ray beam, and (B) Flat side of the dosimeter facing the x-
ray beam.

A more modest improvement was noted for the heterogeneous SBLLBSS phantom

at 0 to 2 cm depth. We attribute the discrepancies seen in Figures 2-2A, 2-3A, and 2-4A

to photon attenuation within the epoxy coating of the dosimeters. This "epoxy









attenuation" effect was subsequently verified through additional MCNP5 modeling (data

not shown).

Another feature of note is the slight departure of measured and simulated values of

tissue absorbed dose at depth within the homogeneous soft tissue phantom. This

departure is potentially attributed to a lower MOSFET sensitivity with depth as fewer and

fewer low-energy photons interact within the dosimeters at increasing depth in the

phantom. Dosimeter sensitivity may decrease by as much as 15% at a depth of 6 cm

relative to its value at the surface. A less sensitive dosimeter would have a greater

calibration factor associated with it, which explains the under-response seen in Figure 2-

2A at greater depths in the phantom. Nevertheless, these errors associated with the

sensitivity decrease are on the same order of magnitude as the experimental errors

associated with the MOSFET dosimeters themselves (at these depths in tissue), and

therefore we do not think that separate calibration factors (which would prove

cumbersome for in vivo or experimental measurements) for each measurement depth

would be warranted.

Even when the "epoxy attenuation" effect is minimized through suitable MOSFET

dosimeter orientation (flat side toward the beam), discrepancies between simulated and

measured point doses at depth are still evident within the heterogeneous phantoms of

Figures 2-3B and 2-4B. We believe that these differences are due to the fact that the

epoxy bubble (now faced away from the incident x-ray beam) attenuates low-energy

photons and electrons scattered from the underlying bone layers (a secondary "epoxy

attenuation" effect). In particular, the discrepancy between measured and simulated









surface doses is noted to be higher in Figure 2-4B (bone layer 1 cm from the surface)

than seen in Figure 2-3B (bone layer 2 cm from the surface).

A final feature of note is evident in Figures 2-3 and 2-4 in which slight increases in

measurement variability are noted for those MOSFET locations within and at the

boundaries of the bone discs of the two heterogeneous phantoms. We attribute these

observations to a loss of charge-particle equilibrium (CPE) at the boundary of the bone

and soft tissue (or lung tissue) layers of the phantom. The absence of CPE will induce

large dose gradients within these regions of the phantom, and any slight variation in the

depth-positioning of the MOSFET dosimeters within the phantom will result in

corresponding variations in measured response.

Conclusions

It is evident from the results presented in this study that the MOSFET dosimetry

system and tissue-equivalent substitutes can be used to accurately measure radiation

absorbed dose as a function of depth within a simple phantom. However, we do make

several recommendations regarding the use of a MOSFET dosimetry system.

The TN-1002RD dosimeter has been used throughout this experiment. The epoxy

attenuation effects described in this paper could be reduced by using the TN-1002RDM

dosimeter, a micro-MOSFET dosimeter. However, the added cost of these dosimeters

makes this option less desirable, and the "epoxy attenuation" effect would still exist,

albeit at a smaller magnitude. Similar (or better) reduction of these attenuation effects

can be achieved through careful placement of the MOSFET dosimeters within the

phantom. When making surface dose measurements in projection radiography, one must

ensure that the flat side of the dosimeter is oriented towards the x-ray beam. However, in

our tomographic newborn phantom, more than 95% of the dose measurement locations









are at depths greater than 2 cm, the depth at which the epoxy attenuation effect is

insignificant. For measurement locations at depths shallower than 2 cm, the MOSFET

dosimeter should be oriented so that the flat side of the dosimeter faces the x-ray beam.

In all cases, these recommendations are valid for calibrations performed as per the

manufacturer's protocol at diagnostic energies.














CHAPTER 3
A TOMOGRAPHIC PHYSICAL PHANTOM OF THE NEWBORN CHILD WITH
REAL-TIME DOSIMETRY. I. METHODS AND TECHNIQUES FOR
CONSTRUCTION

Introduction

Phantoms used in diagnostic radiology can be divided into two general classes: (1)

those used to assess image quality, and (2) those used to assess the radiation absorbed

dose delivered to patients during diagnostic procedures. Those used to assess image

quality are fairly general (i.e., not patient-specific in regard to age, sex, or physical

stature), while those used to assess patient dose are more tailored to these same individual

patient characteristics. In addition, dosimetry phantoms range in complexity from simple

shapes using a single material to anthropomorphic phantoms utilizing several different

tissue-equivalent materials.

The most common pediatric dosimetry phantom is a simple acrylic cylinder.

Nickoloff et al. constructed a series of phantoms to represent patients of various ages (6,

10, 16, 24, and 32 cm diameter acrylic cylinders) and examined the effect of patient size

on CTDI values. In this study, the authors found that CTDI values increase with

decreasing patient size.1 Also, one of the more commonly used practices to measure

radiation doses to pediatric patients is the use of the 16-cm adult head phantom from

AAPM Report #3128 to represent a pediatric body.

A second category of pediatric dosimetry phantoms involves unique geometries or

a combination of several simple geometries. Phantoms in this class include a neonatal

phantom constructed by Jones et al.29 from eight 1-cm thick acrylic sheets with air spaces









machined in each sheet to represent the lungs, and a block-style phantom constructed by

Duggan et al.30 that included a head and torso made from water-equivalent materials and

a lung-equivalent insert within the torso.

A third class of pediatric dosimetry phantoms are characterized as

anthropomorphic. These phantoms are intended to more closely represent the true

external and internal anatomy of the human body. An example of phantoms in this class

are those representing 0, 2, 6, and 12-year-old children constructed by Giacco et al.31

from various cylindrical shapes representing the head, torso, arms, legs, and lungs of the

patients. These phantoms were constructed by forming an acrylic shell which was then

filled with deionized water (the lungs remain air-filled). Also included in this class are

stylized newborn and 1-year-old phantoms based on the MIRD geometry constructed

using tissue-equivalent materials at the University of Florida (UF).32 Finally, perhaps the

best example of phantoms in this class are the those developed by Varchena et al.,33,34

which comprise a set of tissue-equivalent anthropomorphic phantoms of various ages: 0,

1, 5, 10, and 15 years old. While these phantoms provide realistic modeling of pediatric

patients' exterior, skeletal, and lung anatomy, they are not based on corresponding real

patient CT or MR image data sets.

The objective of the present study was to construct a tomographic physical

phantom of a newborn patient which would be uniquely matched to a corresponding

tomographic computational phantom of the same patient anatomy, based on a CT image

set. The combination of phantoms thus establishes a dosimetry system in which the

advantages of each are used in concert to fully characterize internal organ dosimetry and

patient effective dose from diagnostic procedures ranging from radiography to









interventional fluoroscopy and CT. A distinct advantage of physical phantoms is that

explicit knowledge of the photon energy spectrum and patient irradiation geometry is not

required for dose assessment. While traditional TLD dosimetry may be utilized, the UF

newborn physical phantom was constructed for explicit use of either near real-time

(MOSFET dosimeters)89 or real-time (gated fiber-optic-coupled or GFOC dosimeters)59

dose measurement. The companion computational phantom, on the other hand, allows

one to average the absorbed dose over the full extent of soft-tissue organs or even over

voxel sub-regions of those organs, a feature unattainable from limited point-dose

measurements within the physical phantom. Finally, assessment of regional and whole-

body absorbed dose to complex and distributed organs such as the active bone marrow,

skeletal endosteum, and skin are uniquely suited to computational phantoms, and their

results can thus be used to guide the interpretation of point-dose estimates made within

the skeleton of the corresponding physical phantom. In the present paper, we present the

construction of the UF newborn physical phantom. Its application in determining organ

doses in pediatric projection radiography is given in the companion article by Staton et

al.90 (see Chapter 4).

Materials and Methods

The construction process involved in creating a tomographic physical dosimetry

phantom is neither apparent nor straightforward when first undertaken. Much thought

and creativity must be invested before construction begins. Situations arise that must be

troubleshot, and many lessons are learned that are certain to make future phantom

construction more efficient, including various methods for manufacture automation. The

construction process, along with many of the obstacles encountered along the way, is

discussed in the following sections.









Data Formatting and Output

Phantom construction began with the formatting and outputting of the CT data set

that was used to create the phantom. The CT data used to construct the UF newborn

phantom consisted of 485 CT slices of a 6-day-old female cadaver, which was imaged

within 24 hours of death. A helical CT scan was used for data collection, resulting in 512

x 512 images with an in-plane resolution of 0.586 mm and a z-axis resolution of 1 mm.

The cadaver mass was recorded at 3.83 kg.91 The IDL-based routine CT_Contours was

used to segment the CT images into four regions for the purpose of phantom construction

(air, soft tissue, bone tissue, and lung tissue). Details of the segmentation procedure can

be found in Nipper et al.91 In addition to image segmentation, a grid spaced in 32 pixel

increments was overlaid onto the segmented images to facilitate proper alignment of

phantom slices at the time of assembly.

Next, the bitmap output from CT_Contours underwent a final manipulation before

it was used as the basis for phantom construction. Because 1-mm slice thicknesses are

fragile and difficult to manufacture, a decision was made to construct the newborn

phantom at a slice thickness of 5 mm. Therefore, the 1-mm contour data was re-sampled

to a 5-mm slice thickness using a custom-written MATLABa routine. Also, the decision

was made prior to phantom construction that the arms of the phantom should be

removable below the humeral head in order to facilitate correct positioning of the

phantom for various types of simulated patient exams. Consequently, several different

data sets were created using MATLAB, including a complete anatomical data set, a data

set without arms, and a data set containing only arm data.


a The Mathworks, Inc., 3 Apple Hill Drive, Natick, MA 01760-2098









Following the data manipulation described above, one final step was necessary to

transfer the data into a format from which it could be easily transferred to the soft tissue

blanks used for phantom construction. Each of the individual slices was printed onto

transparencies using a laser printer. The transparencies could then be cut with a hobby

knife to make a stencil which was then be used to transfer the outline of each slice to a

soft tissue blank. Figure 3-1 displays an example of one of the bitmap images that would

subsequently be printed onto a transparency for phantom construction.















Figure 3-1. Typical bitmap image demonstrating the four regions (soft tissue, bone
tissue, lung tissue, and air) used in this phantom. This image corresponds to
Slice 28.

Production of Soft Tissue Blanks

The beginning point for each slice of the phantom was a soft tissue blank, ranging

in size from approximately 7.5 cm x 15 cm to 15 cm x 25 cm, depending upon the region

of the body being constructed at the time. These blanks were created by pouring STES-

NB,27,b a newborn soft tissue-equivalent material developed at UF, into molds formed

from Teflon and clay. Teflon was chosen due to its non-reactive properties, allowing for

easy removal of the blank with no potential for contamination of the STES-NB. The

b Information about the composition and manufacturing of STES-NB can be found in Appendix A.









method for mixing and pouring STES-NB, as well as the corresponding methods of

producing BTES-NB and LTES (newborn bone tissue-equivalent and lung tissue-

equivalent materials, respectively) are discussed in Jones et al.27 (this information can

also be found in Appendix A). The molds were filled to depths of slightly greater than 5

mm, as some bubble formation was inevitable due to the mechanical stirring and curing

processes involved in manufacturing the tissue-equivalent materials. A typical filled

mold is shown in Figure 3-2.



















Figure 3-2. Example of the Teflon and clay mold used to form the raw soft tissue blanks.

After the molds had cured, the final stages of blank formation were finished. First,

the blank was machined using a jigsaw into the very basic outline of the current slice to

be constructed. Second, the top portion containing any residual bubbles was sanded

using a belt sander to form a smooth blank of a uniform 5-mm thickness. Figure 3-3

shows a sanded soft tissue blank ready to be used for slice formation. Also, at periodic

intervals during the phantom construction process, samples of the soft tissue blanks were









tested using Archimedes' Principle (see Jones et al.27) to verify uniformity of the material

densities.



















Figure 3-3. Sanded blank (5 mm) used to create the soft tissue outline of each slice of the
phantom.

Formation of Slices

The formation of each slice of the phantom began with the aforementioned soft

tissue blanks. First, the outline of the slice to be constructed was transferred to the blank

via the transparency method discussed previously. The regions representing bone tissue

and air were removed from the transparency, and the outline of the slice was cut from the

transparency as well. In addition, if the slice contained lung regions, the lung portions of

the transparency were carefully removed and saved for creation of the sections of lung

tissue (described in the "Lung Construction" section). Next, the visible outline of the

slice was cut using a jigsaw. Fine adjustments were made using a rotary tool with a

sanding band. Following the shaping of the outline, regions containing bone, lung, or air

were removed using both a jigsaw and a rotary tool. In addition, several regions of the

anatomy, including the head and spine, contained regions of bone completely









surrounding a region of soft tissue. Therefore, it was important to label and save these

soft tissue "islands", which were placed in their correct position in the corresponding

slice before bone introduction. After all machining was finished to create the soft tissue

outline of each slice, the 32 pixel spaced grid was traced onto the top and sides of each

slice to facilitate correct alignment during phantom assembly. Figure 3-4 shows a group

of phantom slices at this point in construction. These slices are now ready for bone

introduction, which will be described in the following section.



















Figure 3-4. Slices after Step 3 of the phantom creation process. Soft tissue outlines have
been created and grid has been transferred to the slice. Slices are now ready
to be prepared for bone introduction.

Bone Introduction

Following the construction of a batch of soft-tissue slices (25-30), bone tissue-

equivalent material (BTES-NB)27', a homogeneous mixture of cortical, trabecular, and

marrow tissues was introduced into the regions containing bone. This process began by

using tape to "mask" the bottom of the individual slices to (1) prevent the BTES-NB


Information about the composition and manufacturing of BTES-NB can be found in Appendix A.









from running under the slice, and (2) hold the BTES-NB in the void where it was poured

while it cured. A second item of preparation included marking the regions containing air

with a pencil mark so there was no confusion as to which regions were to be filled when

the fresh BTES-NB was mixed and ready to be poured.

After an appropriately sized batch of BTES-NB was mixed (usually 200-400 g), a

syringe with a large bore needle was filled with BTES-NB, and the end of the syringe

was clipped to make a small, narrow opening to allow for better control of the flow of

bone-equivalent material. Regions containing bone were then systematically filled with

the syringe contents, refilling as needed. It is important to note here that regions smaller

than approximately 1 mm in diameter are very difficult to fill, and it is impossible to

visually confirm that the BTES-NB has filled the void with no air spaces. Therefore,

bone regions smaller than 1 mm in size were not created in this particular phantom. A

wooden toothpick was found to be an excellent tool for prodding BTES-NB into the

smaller voids in the slices and eliminating air spaces in bone-filled regions. Indeed, a

toothpick was inserted into each region after the first pass with the syringe was made to

eliminate any air spaces that might be present. After leaving the BTES-NB to settle for a

few minutes, a second pass was made with the syringe, and each region was overfilled

slightly. This was done because as the BTES-NB cures, it reduces in volume slightly,

and a perfectly filled region will end up needing more BTES-NB material when cured.

After the slices that had been filled with BTES-NB had been allowed to cure for

several days, a visual inspection was performed to identify which regions, if any, needed

additional material to fill them completely. If regions needed additional BTES-NB, it

was applied and allowed to cure. Next, the masking was removed from the slices, and a









combination of detail sanding and block sanding was used to flatten the bone regions and

remove any film of BTES-NB from the top or bottom of the slices.

An important exception to the above processes involved the slices containing lung

regions, which could not be constructed in a similar manner. BTES-NB was introduced

into these slices after the lung material (LTES) was inserted. Figure 3-5 shows a group

of slices after the bone introduction process.
















Figure 3-5. Slices after bone introduction.

Lung Construction

The construction of the lungs was one of the more challenging tasks involved in the

development of the UF newborn phantom. Because our data set came from a newborn

CT image set, the lungs were very small, especially in the most superior and inferior

extents of the organ. This fact, combined with the fact that the lung tissue-equivalent

material (LTES)27,d used to construct the lungs expands to approximately three times its

original volume during the foaming/curing process, made it necessary to consider

alternative means for lung construction.


d Information about the composition and manufacturing of LTES can be found in Appendix A.









The approach used for lung construction was similar to the process used to form the

original soft tissue slice outlines. LTES material was mixed and poured into sections of

3" PVC pipe to foam and cure. After curing, the sections of LTES were removed and cut

into round 1-cm thick blanks. Then, just as in the construction of the soft tissue slices,

the blanks were sanded until they reached a uniform thickness of 5 mm.e The blanks

were then ready to be used for creation of the lungs. A set of these 1 cm blanks (prior to

sanding) is shown in Figure 3-6.



I
I




Figure 3-6. Lung tissue blanks cut into slices after removal from PVC pipe mold.

The lung slices were formed by tracing on paper the outline of the void in the soft

tissue slice that was to contain each particular slice of lung (remember, the voids would

eventually contain both the lung and the bone tissue that belonged in them). Next, the

outline of the lung portion of the transparency (which was saved) that corresponded to the

current soft tissue slice was traced in the appropriate position on the outline already

present on the paper. In this way, an outline of only the lung tissue (minus the bone

tissue) could be created. This outline was then removed using a hobby knife, and taped

to a lung tissue blank using double-sided tape. Finally, a fine-toothed jigsaw blade was

used to cut around the paper outline, creating a lung slice that would fit tightly within the




e Care must be taken with the fragile lung material-a fine grit sanding belt should be used. It is also
necessary to lightly tap the blanks against a firm surface several times on each side to ensure that all the
dust created by the sanding process is removed.









corresponding soft-tissue slice. A group of soft tissue slices, along with their

corresponding saved portions of lung from the transparencies, is shown in Figure 3-7.






















Figure 3-7. Soft tissue outlines for phantom slices containing lung tissue and
corresponding lung regions saved from bitmap transparencies.

After all of the lung slices were created, it was necessary to find a method for

securing them into their appropriate soft-tissue slices. Also, it was important to protect

the edges of the lung slices that would be adjacent to freshly poured BTES-NB material

(e.g., ribs). Therefore, the portions of the edges of the lung slices that would contact

BTES-NB were masked with strips of a very thin masking tape. The tape prevented the

incursion of BTES-NB into the lung tissue while at the same time leaving the attenuation

characteristics of the phantom virtually unchanged. After masking the edges, the lung

slices were secured in their proper positions using a very small amount of STES-NB

(STES-NB is much more viscous than either BTES-NB or any glue that could have been

used, therefore there was no chance for the STES-NB to seep into the lung tissue).









The final step in the lung construction process involved filling the remaining voids.

Any small voids (other than bone-tissue containing voids) that remained after the

insertion of the lung slices were filled with small amounts of STES-NB. Finally, the

bottoms of the individual slices were masked with tape, and BTES-NB was introduced

into the appropriate voids as described in the previous section. The slices were then

sanded flat after the BTES-NB regions had cured. Figure 3-8 shows a lung-containing

phantom slice after the insertion of the lung, and filling with bone tissue.



















Figure 3-8. View of a phantom slice containing lung after bone introduction, prior to
sanding.

Dosimeter Localization

Prior to final assembly of the phantom, channels that allow for the insertion of

individual dosimeters into the locations designated for point dose measurements were

machined within the individual slices of the phantom. A list of the locations selected for

dosimeter placement is given in Table 3-1. The anatomical description of the location is

listed in the first column, followed by the slice number of the dosimeter locations. The

third column lists the number of dosimeters in the selected location and slice, and the









fourth column contains any pertinent information concerning the location of or the

number of dosimeters in a particular site. Locations listed in bold print are always

utilized (for the purpose of calculating effective doses), while other locations are used as

necessary.

Channels for dosimeter placement were routed into individual slices prior to

phantom assembly to ensure that each dosimeter would be located exactly in its planned

location. Constructing the channels in this manner ensures that each dosimeter will be

located at its intended location within the phantom, and allows one to make sure that the

channel remains in the correct slice and does not deviate in either the positive or negative

z-direction. This is critical in the assessment of organ and effective dose, especially

when using the computational and physical phantoms together, as it is extremely

important that the locations of both the simulated dosimeter and the actual dosimeter are

identical. This is even more critical at tissue interfaces, such as between bone and soft

tissue regions, where significant attenuation gradients may be present.

Phantom Assembly

Permanent assembly of the phantom was selected for simplicity reasons. Also,

permanent assembly eliminates the possibility of losing or misplacing portions of the

phantom. Permanent assembly was possible because the dosimeters intended for use

with the phantom are attached to a reader system and are very small, allowing for

minimal alteration to the phantom in order to facilitate their correct placement.

A commercially available wood glue was selected as the adhesive for phantom

assembly. Previous work with tissue-equivalent phantoms in the UF Pediatric Organ

Dose (POD) Project has found that wood glue behaves most like soft tissue-equivalent

materials in an x-ray beam and is virtually indistinguishable from soft tissue-equivalent







48


Table 3-1. Locations selected for dosimeter placement in the tomographic physical
phantom. Locations in bold print are always used in effective dose
measurements, while other locations may be included as required.
Dosimeter Site Slice Number No. of Dosimeters Notes
Skin* Varies with field 1 Placed in the center of the x-ray field
Skull (Right + Left) 5 2 Centered in the antero-posterior direction
Brain 10 2 Posterior and anterior
Skull (Post. + Ant.) 11 2 Centered in the lateral direction
L eye/lens* 15 0 Skin dosimeter used as surrogate
R eye/lens* 15 0 Skin dosimeter used as surrogate
Maxilla (Right + Left) 17 2
Cervical vertebra (C4) 24 1 Positioned in the vertebral body
Thyroid 24 1 Centered laterally
Thymus 30 1
Left lung 31 1 Located 1/3 of the way through the lung
Right lung 31 1 Located 1/3 of the way through the lung
Esophagus 32 1 Positioned in the esophageal wall
Ribs 33 2 One dosimeter in each rib cage
Heart 34 1
Left lung 35 1 Located 2/3 of the way through the lung
Right lung 35 1 Located 2/3 of the way through the lung
Thoracic vertebra (T6) 37 1 Positioned in the vertebral body
Spleen 41 1
Stomach 41 1
Liver 42 2 Large organ required two dosimeters
Gallbladder 42 0 Mean of liver dosimeters used as surrogate
Pancreas 43 1
Gut 44 1 Small intestine location
Left kidney 46 1 Also describes left adrenal gland
Right kidney 48 1 Also describes right adrenal gland
Gut 49 1 Large intestine location
Gut 53 1 Large intestine location
Lumbar vertebra (L3) 55 1 Positioned in the vertebral body
Illium (Right + Left) 57 2
Ovary (Right + Left) 59 1 One dosimeter located between the ovaries
Bladder 60 1
Rectum/sigmoid colon 61 1
Uterus 62 1
Femoral heads
64 2 Located in the proximal femur heads
(Right+Left)
*These dosimeters will be positioned on the surface of the phantom









materials in images.92 Also, it is much preferred to STES-NB or a plain epoxy-hardener

combination due to its more rapid curing and ease of removal from unintended locations

during phantom assembly.

Slices were glued together two-by-two, with the first pair of slices acting as a

"seed" to which the subsequent pairs were attached. Slices were prepared for gluing by

sanding the surfaces to be glued with a fine grit sandpaper, and wiping them clean with a

damp cloth. A dry matching was attempted first, to make sure the slices would mate

correctly. If necessary, small corrections in the slices were then made by block sanding.

Glue was applied sparingly to the surfaces to be attached, and the slices were aligned

using the grid system described in previous sections. After alignment, the slices were

clamped and allowed to cure for at least 36 hours. The body was then completely

assembled from the resulting large sections of assembled phantom.

Results

The manual construction process used to build this phantom produced excellent

results. Figure 3-9 shows a bitmap image corresponding to slice 27 of the phantom, and

also slice 27 following bone introduction, prior to final sanding. As one can see, the final

product matches the template extremely well.

Figures 3-10 through 3-12 provide various views of the completed phantom.

Figure 3-10 is a front view of the completed phantom, without arms. Figure 3-11 is a

profile view of the completed phantom, without arms. Figure 3-12 is a front view of the

phantom, with arms. Note from these figures that the arms and legs were truncated at the

feet and hands to prevent breakage during use and transport.






































Figure 3-9. Slice 27. (A) Bitmap image corresponding to Slice 27. (B) Slice 27 after
bone introduction.

Discussion

Some aspects of phantom construction underwent several iterations before we

settled upon their final form. Several methods for making soft tissue blanks were

examined before one was finally selected. The original plan was to construct the

phantom using a slice thickness of 2 mm. Two methods of constructing these slice

thicknesses were attempted; one utilizing thin Teflon molds, the other utilizing an

aluminum slab coated with a Teflon spray with 2 mm high aluminum borders around the

edges. Both methods produced satisfactory blanks, however upon examination of the

blanks it was noticed that a small amount of "settling" of the epoxy base had occurred,

























































Figure 3-10. Photograph of the completed phantom, without arms.



























































Figure 3-11. Profile photograph of the completed phantom, without arms.


























































Figure 3-12. Photograph of the completed phantom, with arms.









leading to a stratification of the blank. When the 5-mm blanks described previously were

created, no such problem was observed, and thus a 5-mm slice thickness was selected as

the minimum slice thickness for future phantom construction.

Several ideas were also entertained for lung construction before the previously

described method was chosen. The simplest idea was to introduce the LTES into the

appropriate voids in the soft tissue blanks, as was done with the BTES-NB. However,

this did not allow for adequate foaming of the lung material, and the correct density could

not be achieved. The second idea involved attempting to create the lungs in one piece.

First, the torso section of the phantom would be assembled after bone introduction, then

an appropriately sized batch of LTES mixed and poured into the cavities for the left and

right lungs, where it would foam and cure. This method was rejected because if any

problems occurred, such as the lung material failing to achieve the correct density,

removing and replacing the cured lung material would be a large undertaking. Also,

constructing the lungs in this manner would have prohibited us from using the dosimeter

placement strategy described previously.

When deciding on dosimeter locations and a dosimeter placement strategy, several

problem areas became apparent, necessitating the creation of a set of "goals" for

dosimeter channel placement. Three goals that were established during the machining of

the dosimeter channels are listed below.

1. All dosimeter channels should follow an oblique path, i.e. no dosimeter channel
should proceed in a straight line from the anterior, posterior, or either lateral
direction in the phantom. This is important as it prevents radiation streaming into
the dosimeter channels, which may create artificially high dose readings. Oblique
exams are far less common than AP, PA, and lateral exams in projection
radiography. In CT, the contributions from all angles are averaged as the beam is
constantly moving, and thus radiation streaming into an individual dosimeter









channel is of very little concern. There has been no evidence of radiation streaming
during initial measurements using the phantom.

2. Each dosimeter channel was machined in the correct side of the slice-top or
bottom. This was done depending upon where the dosimeters were located in the
contoured data set to be used for computational simulations (dosimeters were
placed in the computational data set by the author). If a dosimeter was located in
the superior two 1-mm CT data slices that formed the single 5 mm phantom slice,
the corresponding channel in the physical phantom was machined within the top of
that slice. If a dosimeter was located in the inferior two 1 mm CT data slices that
formed the single 5 mm phantom slice, the corresponding channel in the physical
phantom was machined within the bottom of that slice. If a dosimeter was located
in the middle 1 mm CT data slice that formed the single 5 mm phantom slice, the
corresponding channel in the physical phantom was machined alternately in the top
or the bottom of the slice in each instance when this was the case.

3. The channels were machined to be just large enough to accommodate several types
of dosimeters, including FOC dosimeters, MOSFET dosimeters, and other types of
dosimetry systems, such as optically cabled OSL should they become available.
By doing this, the dosimetry system in the phantom can be updated as necessary.
The channels will also accommodate traditional passive devices such as TLDs.

Figure 3-13 shows some typical dosimeter channels in completed slices, and also


illustrates the application of our dosimeter placement strategy.


Figure 3-13. Typical dosimeter channels in completed slices.









Just as areas of concern became apparent during dosimeter placement, several

issues were also encountered during phantom assembly. One issue common to all body

sections was the filling of dosimeter channels with glue. This was easily prevented by

using a straight piece of wire to clean the glue from inside the channels after the slices

were clamped together. There were also some problems specific to certain areas of the

body of the phantom. These are addressed below:

1. Head: Care was taken to remove any glue that made its way into air spaces such as
the trachea and nasal sinuses. This was possible because the head was glued
together from the top down two slices at a time, so the trachea and sinus cavities
were accessible after each addition.

2. Torso: Again, care was taken to remove glue from the trachea. Also, the lungs
presented a special problem in this section, for the same reasons that their edges
were masked before BTES-NB was poured around them. Masking the entire
surface of the lung was not an option, so other options were considered, including
using a smaller amount of a stronger adhesive. However, this did not provide an
adequate bond between slices. Therefore, wood glue was used, but applied only to
areas of the slice exclusive of lung tissue, and in very sparing quantities. Also, the
glue was allowed to cure slightly before assembly and clamping to prevent
migration of the glue as much as possible.

3. Abdomen/Lower body: No unique issues were encountered during construction of
this section of the phantom.

Conclusions

The UF newborn phantom possesses several advantages over existing pediatric

phantoms. First, this phantom is the only tomographic physical phantom in existence,

created completely from a detailed patient CT image data set. This means the anatomy is

real and not just realistic, and the anatomy used for phantom construction is well-

documented, and close to standard reference values. Second, this phantom has a

computational "twin", a segmented computational phantom that was created from the

exact same image data set including all internal soft-tissue organs, bones, and other

structures of interest.91 This combined system of physical and computational phantoms









allows for the calculation of point-to-organ dose scaling factors (SFpoD)8290 that will be

used to calculate average organ doses delivered to the phantom during various diagnostic

exams, and also enable the calculation of effective doses delivered to the phantom during

these same exams. Next, this phantom utilizes a slice thickness of 5 mm, the thinnest

slices of any pediatric physical phantom available. Also, the tissue-equivalent materials

and used to construct this combined phantom/dosimetry system have been extensively

tested (using MOSFET dosimeters) and compared to Monte Carlo simulations in an

experiment which demonstrated their usefulness (see Chapter 2). Finally, we have

developed a methodology for construction that can be extended to all sizes and ages of

phantoms.

The phantom itself will be used to measure effective and average organ doses in all

types of diagnostic exams, and much of the data acquired will be combined with data

such as image quality assessments and the like to form a comprehensive assessment of

the current state of pediatric radiology. Also, the phantom has the potential to be used as

a teaching tool for x-ray technology students if so desired. Finally, the techniques used to

create this phantom and the pitfalls encountered during construction will allow for the

phantom construction process to be streamlined, and the automation of the phantom

construction process is currently underway, bringing the goal of creating a family of

physical phantoms within reach.














CHAPTER 4
A TOMOGRAPHIC, PHYSICAL PHANTOM OF THE NEWBORN CHILD WITH
REAL-TIME DOSIMETRY. II. SCALING FACTORS FOR CALCULATION OF
AVERAGE ORGAN DOSE IN PEDIATRIC RADIOGRAPHY

Introduction

The currently used and accepted method for organ dosimetry in anthropomorphic

physical phantoms involves the placement one or more thermoluminescent or other

dosimeters within a body region or location. The measurement is then used as a

surrogate value for the absorbed dose averaged across the entire organ as needed for dose

reconstruction studies in epidemiology or in the assignment of the effective dose for

radiation protection.10 The measurement of a point dose may not be an accurate measure

of the absorbed dose averaged across the entire organ when large dose gradients exist due

to photon attenuation with depth and/or partial field coverage. Similarly, point estimators

of the average absorbed dose are particularly problematic for distributed organs such as

the active bone marrow and skeletal endosteum. If the dosimeter locations are known in

advance, and the irradiation geometry of the patient is fixed, computational simulation

models of the patient phantom may be used calculate both quantities in the same

anatomical representation of the patient. The ratio of the true average organ dose to the

point dose estimate is defined in this study as the point-to-organ dose scaling factor, or

SFPOD.

The SFPOD differs from the dose conversion coefficient currently used to calculate

organ doses in computational phantoms. With conversion coefficients, Monte Carlo

simulations are used to calculate individual organ doses based on free-in-air dose









quantities, such as air kerma or exposure. Conversion coefficients can be used for

radiology dose calculations, and are commonly used to calculate organ doses in

computational phantoms. Veit and Zankl have used a different type of organ dose scaling

factor in their studies.94 This factor is used to calculate organ doses in patients of various

sizes based on the organ doses in a "standard" patient.

The concept of the SFPOD was first proposed by Sessions et al. in their work with a

stylized physical newborn phantom created at the University of Florida.82 The approach

has resurfaced in the Pediatric Organ Dose (POD) Project with the recent completion of

construction of a state-of-the-art tomographic physical newborn phantom with an

incorporated dosimetry system. The purpose of the present study was to create a

comprehensive set of SFPOD using the identical computational91 and physical (see Chapter

3) tomographic newborn phantoms (see Figure 4-1) created in the POD Project at the

University of Florida, and subsequently use the scaling factors to calculate accurate

values for organ and effective doses delivered within various newborn radiographic

examinations.

Materials and Methods

The following sections will describe in detail both the creation of the scaling

factors using the computational phantom, and their use in calculating effective doses in

the physical phantom for various radiographic newborn examinations.

Modifications to the Newborn Computational Phantom

The newborn tomographic computational phantom was constructed from

segmented CT data of a 6-day-old female cadaver as previously described by Nipper et

al.91 Since the original publication, the newborn phantom has been modified to include

both additional organs that were not originally segmented, and revisions to existing































Figure 4-1. UF Newborn showing the internal organ structure (left) and exterior (middle)
of the computational phantom along with the exterior of the corresponding
physical phantom (right).

organs (Figure 4-2). Table 4-1 gives a list of all segmented organs and their masses with

identification of new and modified organs.


Salivary Glands

Larynx ---
'Z to


C--c~


Pharynx hAw
'I


Stomach Wall Right Colon Wall
Stomach Contents ColonGas\ / Left ColonWall BladderWall BladderContents
Colon Contents ~r S .

S- Small Intestines Wall
Small Intestines Contents


Figure 4-2. Axial slices through the UFNewborn phantom showing modified and
additional organs and tissues.

Organs that were added in this study include the salivary glands, larynx, pharynx,

and trachea (all modifications described in this paragraph and the following paragraph

were performed by Choonik Lee). The lower gastrointestinal tract that was originally

segmented as "gut" was further segmented into the small intestine, left colon, right colon,


Trachea











Table 4-1. Organ and skeletal masses within the modified newborn computational
phantom
Organ Mass (g)

Respiratory Tract
Larynx + 1.30
Pharynx + 0.25
Trachea + 0.60
Lungs 32.52

Alimentary Tract
Salivary Glands + 5.99
Esophagus 2.59
Stomach Contents 6.14
Stomach Wall 7.00
Small Intestines Contents 22.73
Small Intestines Wall 33.97
Right Colon Contents 5.21
Right Colon Wall 7.46
Left Colon Contents 7.20
Left Colon Wall 7.35
Rectosigmoid Colon Contents 2.99
Rectosigmoid Colon Wall 2.77
Liver 109.13
Gallbladder (wall + contents) 2.19
Pancreas 1.33

Circulatory System
Heart 21.13

Urogenital System
Kidneys 21.58
Urinary Bladder Contents 6.48
Urinary Bladder Wall 4.00
Ovaries 0.29
Uterus 3.52
Skeletal System
Skull (cranium + facial bones) 107.38
Mandible 7.38
Leg bones femoraa, tibiae, fibulae, patellae, ankles, and feet) 34.78
Arm bones humerii, radii, ulnae, wrists, and hands) 24.16
Scapulae 7.30
Clavicles 3.08
Ribs and sternum 34.62
Pelvis 14.02
Spine (all vertebrae) 47.99

Integumentary System
Skin 102.10

Other Organs and Tissues
Adrenals 3.01
Brain 291.38
Eyes (with lens) 2.93
Remainder tissues (with arms and legs) 2509.17
Spinal Cord 15.13
Spleen 7.64
Thymus 10.00


Thyroid









Table 4-1. Continued

Total Body Mass (kg) 3.54
* Modified organs, + Added organs

and rectosigmoid colon following the ICRP 89 definition.14 For all regions within the

lower gastrointestinal tract, wall and contents were defined separately. Identifiable

regions of bowel gas were also segmented within the colon. Revisions were also made to

the stomach and bladder to include a delineation of their wall and contents.

Though their relative anatomical locations were identifiable, these soft tissue

organs were somewhat difficult to distinguish from surrounding tissues due to the lack of

contrast among soft tissues in the original cadaver CT data. During segmentation,

attempts were made to match organ masses to those in the ICRP 89 reference newborn

data.14 The final revised newborn phantom includes a total of 80 segmented organs and

tissues. Newborn tissue compositions and densities published by Cristy and Eckerman85

that were used in the creation of the newborn phantom tissue substitutes were also

employed in the newborn computational phantom.

Simulated dosimeter placements were also included (by the author) in the newborn

computational phantom to match those previously described in the corresponding

physical phantom (a list of these locations can be found in Chapter 3). Each dosimeter

region was modeled as a 4 x 4 x 3 (x, y, z) group of voxels with a resulting volume of

14.9 mm.91 This region is slightly larger than the actual volume of a physical dosimeter,

but was required to ensure sufficient statistical precision in the Monte Carlo simulations

of x-ray exposure. Each simulated dosimeter was assigned the same density and

elemental composition as the surrounding tissue.









The following sections describe, in detail, the development (computationally) of

the point-to-organ dose scaling factors (SFPOD), work done in large part by Robert

Staton.95 The author performed all measurements (including exposure measurements), in

addition to aiding in the tube characterization and development of the SFPOD.

Monte Carlo Codes for Radiograph Simulation

The Monte Carlo radiation transport code EGSnrc96 was selected for simulations

due to its versatility in handling large voxel arrays. With a rectilinear array of 512 x 512

x 485 voxels, the newborn tomographic computational phantom is represented by a

matrix of over 127 million individual voxels. Each voxel has a tag (ID value) belonging

to a given segmented organ, tissue, or dosimeter location. For each segmented region,

energy deposition was tracked across all voxels to report absorbed dose. The tally within

each region was obtained in units of absorbed dose (MeV per g) per launched photon.

For skeletal sites, volume-averaged energy fluences were acquired to assess the absorbed

dose to the active marrow through the use of fluence-to-absorbed dose response

functions.9 The absorbed dose to homogeneous bone was used as a surrogate for the

absorbed dose to the skeletal endosteum for all bone sites.98 Photons were followed down

to an energy of 1 keV and secondary electrons with kinetic energy below 10 keV

deposited their energy locally. All radiograph simulations were run with 500 million

initial particles to achieve acceptable statistical errors for organs (<1%) and dosimeter

locations (<1%) inside the field-of-view.

Simulated fields of view

For the development of a general set of scaling factors, four "whole-body" views

were simulated for the tomographic computational phantom: rectangular fields of view in

the antero-posterior (AP), postero-anterior (PA), right lateral (RLAT), and left lateral









(LLAT) directions. The "whole-body" field-of-view extended from the top of the head to

just below the femoral heads and covered the entire lateral extent of the phantom.

"Whole-body" fields-of-view were used to generate a comprehensive set of SFPOD values

that can be used for any field that has partial coverage within the torso (i.e., chest,

abdomen, pelvis, etc.). The "whole-body" fields were found to produce SFPOD values

that were very similar to values for partial coverage fields for organs fully within the

field. For example, the SFPOD for the right lung for a chest field at 66 kVp was found to

be 0.90, while the same value for a "whole-body" field was determined to be 0.91. For

organs outside of the field, minor differences in the SFPOD values are not important since

the average organ doses themselves are very low in comparison to those within the x-ray

field. For example, the average organ dose for the bladder wall for a chest field at 66

kVp is about 70 times smaller than the value for the right lung.

For all lateral and chest views, the arms were removed (by deleting voxels of the

arms which were distal to the humeral epiphyses) to represent clinical positioning of the

patient. For AP and PA projections, lateral field boundaries were chosen just outside of

the most lateral extent of the trunk. All lateral views were restricted to the outermost

extent of the anatomy in the antero-posterior direction.

X-ray source modeling and beam characterization

The x-ray energy spectra needed for Monte Carlo simulations of the projection

radiographs were generated using the tungsten anode spectral model TASMIP.8

TASMIP requires three parameters to generate energy spectra: peak tube potential (kVp),

voltage ripple, and added filtration. The x-ray tube at Shands Hospital that was used was

characterized and found to have a HVL of 3.74 mm of Al and a voltage ripple of 5.0% at

80 kVp. Values of total filtration were evaluated by iteration in a series of Monte Carlo









simulations of filtered x-ray fields so that predicted values of HVL matched measured

HVL values at 80 kVp. X-ray beam characteristics for input to TASMIP were set at a

voltage ripple of 5.0% and an added filtration of 2.35 mm Al.

For all simulations, the x-ray unit was modeled as a photon point source with

angular sampling slightly larger than the field of view and forming a conical beam. The

beam was then reduced to the actual field of view through simulated collimation whereby

photons starting outside the rectangular field-of-view were terminated. To accurately

simulate pediatric radiography, the source was positioned at a source-to-image distance

(SID) of 100 cm. The SID was defined in the simulations to be the perpendicular

distance from the source to the farthest extent of the phantom at the center of the field.

Monte Carlo ionization chamber simulations

To relate organ doses calculated within the Monte Carlo radiograph simulations to

those a newborn patient would receive clinically, ion chamber measurements of air kerma

were made for each radiographic view. Free-in-air measurements 95 cm from the focal

spot of the x-ray tube were recorded experimentally for each field-of-view with a

Keithley Model 96035B dual entrance window ion chamber and a Keithley Model

35050A electrometer. All measurements were recorded at clinically relevant technique

factors for a patient with a mass equivalent to that of the newborn tomographic phantom

(-3.54 kg). Next, the ion chamber was simulated within EGSnrc, applying the same

dimensions and material compositions of the ion chamber used experimentally. The air

kerma free-in-air within the modeled ion chamber was calculated in units of absorbed

dose (MeV per g) per launched photon. Simulations were run to mimic the field size at

the patient's surface for each of the examinations studied. Values of organ/tissue

absorbed dose DT are thus calculated as:










DT = D MCx (4-1),
EAKmc

where DT,MC is the Monte Carlo estimate of tissue absorbed dose, EAKMC is the Monte

Carlo estimate of the free-in-air entrance air kerma, and EAKIc is the ion chamber

measurement of that same quantity.

Creation of Point-to-Organ Dose Scaling Factors (SFpoD)

For each "whole-body" MC simulation, SFPOD values were calculated for all

organs/tissues which contained dosimeter locations (see Chapter 3). For each

organ/tissue, the SFPOD value was calculated as the ratio of (1) the MC calculated average

organ absorbed dose and (2) the MC calculated absorbed dose in the simulated dosimeter.

For organs with multiple dosimeter locations, the average of all point doses within the

organ was used in the SFPOD calculation. MC simulations of AP, PA, RLAT and LLAT

"whole-body" fields were simulated at tube potentials of 66, 80, and 100. The results of

these simulations were used to develop a comprehensive set of SFPOD for general

application in pediatric newborn radiography.

Methods were also developed to calculate skeletal-averaged red bone marrow and

bone surface absorbed doses from point dose measurements within the skeleton (which is

currently modeled as a homogeneous mixture of active marrow, cortical, and trabecular

bone tissue) of the newborn phantom. For active bone marrow, marrow dose conversion

factors (MDCF) were calculated as the ratio of the active marrow absorbed dose to the

homogeneous bone absorbed dose and were indexed to each skeletal region of the

phantom. The active marrow dose in each bone site was calculated using fluence-to-dose

response (FDR) functions published by Cristy and Eckerman.97 The MDCF thus allows

for the conversion of each homogeneous bone point dose measurement (DHB) to an active









marrow absorbed dose for a given skeletal region. Since the FDR functions are a

function of the x-ray spectrum incident on the skeletal region, the corresponding MDCF

values are indexed to the x-ray energy spectrum incident on the phantom and to the

radiographic projection. The MDCF value for a given skeletal region, b, is thus

calculated by the following expression:

SD b,E -FDRb,dE
MDCFbkVp,Proj = E- (4-2).
b,HB

The active marrow dose for a single skeletal region is calculated as the product of

the dosimeter dose and the appropriate MDCF. The skeletal-averaged active marrow

absorbed dose is then calculated as a mass-weighted average of the active marrow dose in

each skeletal region. Values of the marrow weighting factor (MWF) for skeletal region b

for the newborn are used as published by Cristy.99 The skeletal-averaged active bone

marrow absorbed dose is thus given by the following expression:

M kVpProj Proj
(Dcive kelrrow) lrag = Y, (Ddoi.e xl b X MDCFbk'pj xMWF (mGy) (4-3).
b b

Similar methods were also used to calculate skeletal-averaged absorbed dose to the

bone surfaces. However, the homogeneous bone dose has been found to be a better

estimate of the bone surface dose than calculations involving FDRs.98 The point dose

measurement in each skeletal region was used to approximate the bone surface absorbed

dose for that region. These point doses were then mass weighted across the entire

skeletal according to a bone weighting factor (BWF) defined as the fraction of total bone

mass contributed by that skeletal region b in the newborn phantom. The equation used

for calculating the skeletal-averaged bone surface absorbed dose is thus given as:










(DBone Saces )Skeletal Average= E D F x B WF (mGy) (4-4).


For bone sites with multiple dosimeter locations, the average of point dose measurements

within the bone sites was used in both Equations 4-3 and 4-4.

Methods were also developed to estimate skin and remainder doses since both are

also needed in the calculation of the effective dose. A dosimeter was placed on the skin

surface at the center of each field in the phantom and in the MC simulations. In the MC

simulations, a total skin dose can be calculated, which is not possible using the phantom.

Using the MC simulations of each specific field, the ratios of the point dose

measurements to total skin doses were used to calculate the SFPOD values needed to report

total skin doses from a single dosimeter measurement on the physical phantom. The

remainder tissue absorbed dose was taken as an average of all point doses given by

dosimeters located within the soft tissues of the physical phantom (i.e., not located within

skeletal or lung regions of the phantom). Object 4-1 contains the Radiograph Organ

Dose Calculator developed at the University of Florida.

Object 4-1. Radiograph Organ Dose Calculator developed by Robert Staton and Aaron
Kyle Jones at the University of Florida. This spreadsheet allows the user to
input the type of exam, the tube potential used, and values for point dose
measurements, and returns values for average organ doses and effective dose
(61.5 KB, Radiograph_OrganDose_Calculator.xls).

Newborn Radiographic Exams

The radiographic exams of the experimental portion of this study were performed

on the UF tomographic physical newborn phantom. Dosimetry was performed using a

MOSFET dosimetry system manufactured by Thomson and Nielsen Electronics, Ltd.,a


a Thomson and Nielsen Electronics Ltd., 25E Northside Road, Nepean, Ontario, Canada, K2H 8S1.









including the TN-RD-60 patient dosimetry system (which includes dual-sensitivity bias

supplies used at the high sensitivity setting for this experiment) in conjunction with the

TN-1002RD dosimeter, the most current dosimeter in the isotropic, high-sensitivity line

of dosimeters. The dosimetry system, along with the phantom components, were

previously evaluated against Monte Carlo simulations to verify their performance in

diagnostic radiology89 (see Chapter 2).

All radiographic exams completed on the newborn phantom were performed using

a diagnostic x-ray tube at Shands Hospital at the University of Florida. The radiographic

views selected were AP Skull, Lateral Skull, AP Chest, PA Chest, Lateral Chest, AP

Abdomen, and AP Pelvis. These views were not selected as a comprehensive

representation of newborn radiography, but rather as a general set of exams on which the

scaling factors could be tested. All exams were performed at a tube potential of 66 kV,

which was determined from the technique charts used at the hospital. A tube current of

200 mAs was utilized throughout the study to give better statistics and shorten the

experimental time. The resultant effective dose values were then normalized to units of

iSv/mAs. X-ray technicians at Shands were recruited to properly position the phantom

and set the dimensions of the x-ray field for each individual exam. The MOSFET

dosimeters used for dose measurement were calibrated against a Keithley Model 96035B

dual entrance window pancake ionization chamber using the x-ray energies used

throughout the experiment, as recommended by Jones et al.89

MOSFET dosimeter placement differed from exam to exam. The MOSFET

dosimetry system used supports 20 dosimeters at any given time, and thus we were

limited to 20 simultaneous measurements. Dosimeters were placed in all available









dosimeter locations within the field of view, as well as locations bordering or in close

proximity to the field edges. The large number of organs in some fields (e.g., AP

Abdomen) required that the bone dosimetry and soft tissue dosimetry be conducted as

separate measurements. Also, a skin dose measurement was made by placing a MOSFET

in the center of each field.

In several cases, MOSFET dosimeters were located just outside the x-ray field and

consequently they recorded very small absorbed dose values, primarily from scattered

radiation. As a result, rules had to be set to determine which dosimeter locations would

be considered to have received a non-zero dose for purposes of calculating the effective

dose. The decision was made that any dosimeter that recorded two or fewer non-zero

dose measurements out of seven total measurements would be disregarded. Also, along

with dosimeters that recorded two or fewer non-zero readings, dosimeters whose

measurement errors were equal to or greater in magnitude than the absorbed dose

measurement itself were also disregarded. By taking these simple steps, accurate

estimates of organ and effective doses could be calculated.

Results and Discussion

A comprehensive set of radiographic SFPOD values calculated for the newborn

phantom is given in Table 4-2.

It can immediately be seen that large, walled, or widely distributed organs such as

the liver, stomach wall, and skin have SFPOD values that are significantly less than or

greater than 1.0, depending on the specific projection. It can also be seen that small

organs such as the ovaries, uterus, and eyes have associated SFPOD values very close to

1.0, as expected. Also of interest are SFPOD values associated with the rectum/sigmoid

colon. The reason for the large departure from unity for these scaling factors arises from










Table 4-2. Radiographic SFpOD for the newborn phantom (valid over the range 60-100
kVp).
Organ AP PA RLAT LLAT
Brain 0.86 0.99 0.98 1.04
Left Eye 1.00 1.02 0.92 1.01
Right Eye 1.01 1.01 0.99 1.06
Thyroid 0.92 0.97 0.95 1.03
Thymus 1.06 1.08 0.99 1.03
Right Lung 0.91 0.94 1.03 1.05
Left Lung 0.98 0.95 0.84 0.98
Esophagus 1.05 0.97 0.84 0.85
Heart 1.01 1.07 1.00 1.05
Spleen 1.08 0.94 1.10 1.02
Stomach Wall 0.82 1.32 0.97 0.93
Liver 0.89 1.28 1.05 0.94
Pancreas 1.02 0.98 1.12 0.99
Gallbladder 0.93 1.15 0.96 1.07
Left Kidney 1.05 0.97 1.05 1.01
Right Kidney 1.01 1.01 0.95 1.09
Small Intestine 0.89 1.32 0.85 1.14
Left and Right Colon 1.01 1.13 1.00 1.28
Right Ovary 0.99 0.99 1.03 0.97
Left Ovary 1.01 0.99 0.98 0.99
Bladder Wall 1.05 0.96 1.05 0.94
Rectum/Sigmoid Colon 1.71 0.70 0.95 1.67
Uterus 1.00 0.95 0.99 1.04

the fact that improvements and modifications are sometimes made to the computational

phantom, such as segmentation of organ walls or further segmentation of existing organs

(as is the case with the rectum/sigmoid colon), as new tools become available. While the

physical phantom is not easily modified or changed, these modifications can in essence

be "transferred" to the physical phantom through the use of scaling factors.

The results of effective dose calculations utilizing the newborn physical phantom

are displayed in Table 4-3.

Table 4-3. The effective dose per unit integrated tube current (tiSv / mAs) as calculated
with and without the application of point-to-organ dose scaling factors SFpOD.
Projection/Exam E with SFPOD E without SFPOD Percent Error (%)
AP Skull 1.28 0.04 1.70 + 0.05 32.8
Lat Skull 1.44 0.06 1.92 0.08 33.3
AP Chest 7.81 0.23 8.96 0.26 14.7
Lat Chest 5.67 0.24 6.22 0.26 9.7
PA Chest 5.80 + 0.33 5.82 0.34 0.3
AP Abdomen 13.0 + 0.44 13.1 0.44 -0.8
AP Pelvis 8.72 0.55 8.01 0.51 -8.1









Values calculated both with and without the use of the SFPOD values are listed

(errors represent one standard deviation), as is the error associated with calculating

effective doses without the aid of point-to-organ dose scaling factors. It is apparent that

calculating effective doses using only point doses generally leads to an overestimation of

the actual dose delivered to the phantom (the negative value for the AP Pelvis exam can

be attributed mostly to the rectum/sigmoid colon modification discussed previously).

Also, one can see that the error associated with using only point doses is magnified when

there are relatively few organs of interest in the x-ray field, as demonstrated by the AP

and lateral skull exams.

Conclusions

We have demonstrated that calculating doses, both effective and organ, using only

point dose measurements in physical phantoms can lead to significant errors in certain

projections. We have also demonstrated that by using an identical computational

phantom, SFPOD values can be derived and used to calculate average organ absorbed

doses within the corresponding physical phantoms.

Computational phantoms alone have weaknesses-as imaging technology becomes

more and more sophisticated (e.g., helical 64-slice CT scanners), simulations will become

more and more difficult and time-consuming than they already are, forcing more and

more approximations to be made. However, with a physical phantom, it is a relatively

simple matter to directly measure point absorbed doses delivered during various

radiographic and CT examinations of the physical phantom. This being said, physical

phantoms alone also have their shortcomings, namely the inability to accurately measure

average organ doses, especially in large, walled, or widely distributed organs.

Furthermore, physical phantoms are difficult or impossible to modify once constructed.






73


However, as we have demonstrated in this work, a dosimetry system based upon a

matched set of physical-computational tomographic phantoms is a powerful tool for

determining the organ and effective doses delivered to patients during diagnostic

radiology procedures.














CHAPTER 5
CHARACTERIZATION AND TESTING OF THE FIBER OPTIC-COUPLED (FOC)
DOSIMETER

Introduction

This chapter addresses the initial testing performed on the fiber optic-coupled

(FOC) dosimeters used for computed tomography dose evaluation. Fiber-optic-coupled

(FOC) radiation dosimeters are based on the detection of phosphorescence from a Cu+-

doped quartz fiber.59'62 This charge-trapping material is fabricated by doping fused-quartz

glass with Cu+ ions.60'61'63 The doped quartz perform is drawn into a fiber, and a short

length is attached to a multimode optical fiber via a plasma fusion fiber splicer. The

photons released (due to prompt radioluminescence) during irradiation can thus be read

passively in real-time during the irradiation as they travel from the active area through the

fiber optic cable and eventually impinge on some type of detector (photomultiplier tube

(PMT), CCD, etc.). A more detailed description of the doped quartz and the fiber

construction process can be found in references 59-63.

Materials and Methods

The majority of the testing performed on the FOC dosimeter was done using a

single FOC dosimeter read by a PMT. The dosimeter, reader, and software (simply

scoring total counts) were supplied by Alan Houston and Paul Falkenstein from the

Optical Sciences Division of the U.S. Naval Research Laboratory.a The single dosimeter


a U.S. Naval Research Laboratory, Optical Physics Branch, Code 5611 4555 Overlook Avenue, SW,
Washington, D.C. 20375









consisted of an active area 4 mm in length and 400 [im in diameter. The active area was

fused to a 1 m long fiber optic patch cable coated with an opaque, light-tight black Tefzel

coating, and the active volume was coated first with a low refractive index clear polymer

cladding, and then with an opaque black epoxy to ensure light-tightness. All tests

described in this section were performed with the dosimeter resting on 2 cm of acrylic

backscatter material. The x-ray source used was a clinical x-ray tube at Shands Hospital

with a half-value layer of 2.35 mm of Al and 5% voltage ripple at 66 kVp. The

ionization chamber used for simultaneous exposure measurements was a 15 cc Keithly

Model 96035B dual entrance window pancake ionization chamber, along with a Keithley

Model 35050A electrometer.

Energy Dependence

The energy dependence of the FOC dosimeter was tested by performing five

irradiations of the dosimeter at successively higher tube potentials, ranging in increments

of 10 kVp from 40 to 120 kVp, while simultaneously irradiating the ionization chamber.

A tube current-time product of 100 mAs was used for all irradiations. Using this data,

values of the dosimeter's sensitivity, in units of counts/mR, were calculated for each tube

potential.

Dose Linearity

The dose linearity of the FOC dosimeter was tested by performing five irradiations

of the dosimeter at successively higher tube current-time product values, ranging from 20

mAs to 200 mAs. A tube potential of 80 kVp was used for all irradiations. A linear fit

was applied to the collected data, and the square of the correlation coefficient was used to

assess the dose linearity of the dosimeter.









Angular Dependence

Due to the active area's cylindrically symmetrical shape, only two orientations

were tested during the angular dependence portion of the study. The FOC dosimeter was

first irradiated "normally" (x-ray beam incident perpendicularly on fiber) while resting

flat on the acrylic backscatter media, and then irradiated tip-on. These measurements

were performed using a tube potential of 100 kVp and a tube current-time product of 100

mAs.

Dosimeter Response at Varying Bend Radii

This test was performed using the FOC dosimetry system described below (and in

Chapter 6), and using a clinical x-ray tube at the Orthopedic Institute at Shands Hospital.

However, the dosimeters were calibrated prior to testing using the same energies and the

same tube. Four of the twenty-five 15 m fibers were selected for testing. Five

measurement points were selected, and two irradiations of the four dosimeters were

performed at each point. The first two irradiations were performed with the entire length

of the fibers perfectly straight. Subsequent pairs of irradiations were performed while

bending the fibers in circles of progressively smaller radii. The bends spanned one half

of the circumference of each diameter circle, with the fibers exiting the bend 40 cm from

the active area. The circle diameters used for the bends were 37 cm, 23 cm, 13 cm, and

6.7 cm. All irradiations were performed at a tube potential of 100 kVp and a tube

current-time product of 500 mAs.

The following sections will transition from characterization of the FOC dosimeter

to the preparation of the 25 fiber FOC dosimetry system for use in in-phantom dose

measurements. A description of the FOC dosimetry system will ensue, followed by









sections addressing the sensitization of the FOC dosimeters, and finally a section on the

calibration of the FOC dosimeters.

The Twenty-Five Fiber FOC Dosimetry System

The system used in this work was constructed by Huston and Falkenstein in the

Optical Sciences Division of the Naval Research Laboratory It consists of 25

dosimeters, each with an active length of approximately 4 mm, with each fiber having a

400 [tm core diameter. The doped-quartz fibers are individually coupled to 15 m long

fiber-optic patch cables, which are coated with an opaque, light-tight black Tefzel

coating, and the active volumes are coated first with a low refractive index clear polymer

cladding, and then with an opaque black rubber material to ensure light-tightness. Figure

5-1 shows the coated active areas of the dosimeters.


Figure 5-1. Coated active areas of the FOC dosimeters.









The fibers were then permanently epoxied into a casing which houses the optics

and the image sensor used to image each of the 25 fibers, a thermoelectrically cooled

Hammamatsub linear CCD (model S7031-1007 with a model C7041 detector head). This

CCD consists of a 1044 x 128 pixel matrix (1024 x 122 active pixels), with a pixel

dimension of 24 x 24 im. The spectral response range is 200-1100 nm (90% quantum

efficiency at peak sensitivity), with a typical dark current of 200 e-/pixel/s at 0 OC and a

typical readout noise of 8 e- rms (along with a readout noise of 20 e- rms for the detector

head). However, the dark current is an exponential function of temperature, therefore, at

our typical operating temperature of approximately -10 C, the dark current is

significantly less. This information, plus a much more detailed analysis of the S7031-

1007 and C7041, can be found in the PDF object located below. Figure 5-2 shows two

photographic views of the system, with the components of the system labeled.

Object 5-1. PDF document containing the complete specifications of the S7031-1007
CCD image sensor and C7041 detector head, including detailed graphs of dark
current vs. operating temperature (237 KB, CCD_specs.pdf)

Custom LabView software was written to interface with the FOC dosimetry system

via a National Instrumentsc AT-AI-16XE-10 16-bit, 16 analog input data acquisition card.

The software not only allows for the reading of the dosimeters, but also contains a

calibration routine that is used for inning the output corresponding to each fiber image,

as well as calculating calibration factors (and their corresponding errors) in mR/counts

for each dosimeter. In addition, both the calibration and reading routines automatically

correct the raw data by subtracting background contribution to the data.


b Hamamatsu Corporation, Bridgewater, NJ

' National Instruments Corporation, 11500 N Mopac Expwy, Austin, TX 78759




















































Figure 5-2. (A) Schematic of the design of the FOC dosimetry system. (B) Detailed
view of the CCD detector head and other components.









Sensitizing the FOC dosimeters

Prior to use, the FOC dosimeters were sensitized by irradiating them in our Co-60

irradiator while monitoring the radioluminescence signal via the LabView software. The

signal increased with time as described in reference 59, reaching a saturation level that

was approximately 170% of the initial level after 5 hours of irradiation and an

accumulated dose of approximately 1400 kGy. Figure 5-3 shows the raw voltage signal

from the CCD at the beginning of the irradiation, and at the end of the irradiation,

showing the corresponding increase in dosimeter sensitivity over the irradiation period.

After sensitization, the response of the dosimeters is expected to remain constant over

many kGy of ionizing radiation.59

Calibration of the FOC dosimeters

The final step to prepare the FOC dosimeters for use after their initial sensitization

was calibration. Any dosimeter that is used to make point dose measurements must be

calibrated against an accurate instrument (such as a calibrated ionization chamber) to

determine the linear transformation needed (provided the dosimeter has a linear response

to radiation) to convert the output of a dosimetry system (counts, mV, V, etc.) into a

value (e.g., mR) that can then be used to calculate dose.

All calibrations for the FOC dosimetry system were performed using a clinical x-

ray tube at the Orthopedic Institute at Shands Hospital. The measured HVL was 3.25

mm Al, and the voltage ripple was 3% at 100 kV. The ionization chamber used for the

calibration was a 15 cc Keithly Model 96035B dual entrance window pancake ionization

chamber, along with a Keithley Model 35050A electrometer. A separate calibration was

performed for each of the tube potentials that were to be examined as part of the study









described in Chapter 6 (80, 100, and 120 kVp). Both the ionization chamber and FOC

dosimeters were irradiated atop 5 cm of acrylic to provide a scatter contribution.















A














B

Figure 5-3. Graphical depiction of the effects of the sensitization of the FOC dosimeters.
(A) Raw data output (volts/sec) from the CCD at the beginning of the
sensitization period. (B) Raw data output from the CCD at the end of the
sensitization period (elapsed time of 5 hours). Note the difference in the y-
axis scaling between the two graphs.

Five calibration points were acquired at each energy, using a tube current-time

product of 250 mAs at a source-to-detector distance of 60 cm each time. The ionization

chamber was irradiated before and after each set of five calibration points, with the two

exposure values being identical each time. The FOC dosimeters were spaced tightly