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Optimization of Contrast Agents for High Magnetic Fields

Permanent Link: http://ufdc.ufl.edu/UFE0021554/00001

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Title: Optimization of Contrast Agents for High Magnetic Fields
Physical Description: 1 online resource (206 p.)
Language: english
Creator: Cornnell, Heather
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: contrast, imaging, magnetic, mri, nmr, nuclear, paracest, relaxation, resonance, t1, t2
Biomedical Engineering -- Dissertations, Academic -- UF
Genre: Biomedical Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Magnetic Resonance Imaging is one of most popular medical imaging modalities with the advantages of noninvasive and nondestructive high resolution images. Utilizing high magnetic fields can provide increased signal to noise resolution, but current contrast agents are not optimized for use in fields above 3 T. Several types of contrast agent were investigated to evaluate their potential application for cellular or molecular imaging at high magnetic fields in the future. The results indicated that T1 relaxation agents (gadolinium-based) were best used at lower magnetic field strengths, but two others, T2 relaxation agents (iron oxides) and PARACEST agents, hold great promise for high field contrast generation. These results could be applied to future studies to enhance contrast, gain higher resolution, advance understanding of cellular or molecular function, and contribute towards a dual anatomical-metabolic imaging modality of the future.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Heather Cornnell.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Edison, Arthur S.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0021554:00001

Permanent Link: http://ufdc.ufl.edu/UFE0021554/00001

Material Information

Title: Optimization of Contrast Agents for High Magnetic Fields
Physical Description: 1 online resource (206 p.)
Language: english
Creator: Cornnell, Heather
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: contrast, imaging, magnetic, mri, nmr, nuclear, paracest, relaxation, resonance, t1, t2
Biomedical Engineering -- Dissertations, Academic -- UF
Genre: Biomedical Engineering thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Magnetic Resonance Imaging is one of most popular medical imaging modalities with the advantages of noninvasive and nondestructive high resolution images. Utilizing high magnetic fields can provide increased signal to noise resolution, but current contrast agents are not optimized for use in fields above 3 T. Several types of contrast agent were investigated to evaluate their potential application for cellular or molecular imaging at high magnetic fields in the future. The results indicated that T1 relaxation agents (gadolinium-based) were best used at lower magnetic field strengths, but two others, T2 relaxation agents (iron oxides) and PARACEST agents, hold great promise for high field contrast generation. These results could be applied to future studies to enhance contrast, gain higher resolution, advance understanding of cellular or molecular function, and contribute towards a dual anatomical-metabolic imaging modality of the future.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Heather Cornnell.
Thesis: Thesis (Ph.D.)--University of Florida, 2009.
Local: Adviser: Edison, Arthur S.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0021554:00001


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OPTIMIZATION OF CONTRAST AGENTS FOR HIGH MAGNETIC FIELDS


By

HEATHER H. CORNNELL

















A DIS SERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILO SOPHY

UNIVERSITY OF FLORIDA

2009






































O 2009 Heather H. Cornnell




































To my Mom and Dad, the best parents in the world









ACKNOWLEDGMENT S

For the first of my academic acknowledgements, I would like to extend my deepest

gratitude to Dr. Arthur Edison and Dr. Glenn Walter. They have both been invaluable, without

their help none of this work would be possible. Dr. Edison was always insightful, not only in

academics, but with an exceptional big-picture attitude that always made me think outside of my

comfort zone. Dr. Walter always challenged me with an uncanny ability to recall details that

inevitably were correct and essential. I believe this combination helped me produce better

research, and to become a more well rounded scientist. They are two of the smartest people I

have ever met; I will always admire and respect both of them. I will always be grateful.

I thank my committee members Dr. Huabei Jiang and Dr. Rosalind Sadleir for their time

and contributions. I thank Dr. Sherry for introducing me to PARACEST, for his material

contributions to my research and for many insights which significantly contributed to my

research. I thank Dr. Merritt for the CESTFIT MATLAB program, without which a large

porti on of thi s proj ect c would n ot have b een compl eted.

I thank the staff of the Advanced Magnetic Resonance Imaging and Spectroscopy

(AMRIS) and National High Magnetic Field Laboratory (NHMFL) faculty and staff including

(but not limited to) Barbara Beck, Gary Blaskowski, Sam Grant, Kelly Jenkins, Victor Schepkin

and Xeve Silver. I especially thank Dan Plant and Jim Rocca for all of their time and assistance,

and Dr. Joanna Long for insight and recommendations. I thank all of my current and past

labmates, especially Omj oy Ganesh, Seth McNeill and Cherian Zachariah.

On a personal level, first and foremost I thank my parents; without their decision to

procreate, I would not exist. More importantly, they have always provided love and support, and

allowed me to choose my own path. I admire my mom's determination and spirit, and thank my

mom for her willingness to listen, even if she didn't always understand. I admire my dad's










dedication and his unique perception of life, and I thank my dad for the bed time stories and

pretending to understand. They are the best parents I could ever have asked for, and they have

my undying gratitude and love.

I also thank my sister, and although I did not include her version of my research, I certainly

wanted to. She has always been there when I needed her and always knew exactly what to say to

make me feel better. Haley has always been, and will always be my best friend. She too has my

eternal thanks and love.

I want to specify that my next acknowledgements are in alphabetical order. I thank Upohar

Haroon and Andrew Rudd.

Upohar Haroon has been my closest friend, and my most long lasting friendship. Although

she hated me in fifth grade, I suckered her into friendship in eighth grade and we've never looked

back. I think when you are friends with someone that long, they start to change the type of

person you are. Upohar i s caring, intelligent, and loyal to a fault. Being friends with Upohar for

all these years has made me a better person, and I am forever grateful.

Andrew Rudd has been a huge part of my adult life. His pragmatic take on my problems

may not have been what I wanted to hear, but it was always what I needed to hear. I thank him

for balancing my occasional impetuousness; for being the calm in my storm. I appreciate

everything that Andrew has done for me, and for the wonderful person he is. He has my love

and thanks forever.

I would like to thank my friends. I thank Regina Wolper for her emotional support, and for

the treats and coffee. I thank Matthew Peterson for being such a great friend, I couldn't imagine

a better person for Upohar to spend the rest of her life with. I thank Vishal Patel for road trips, I

thank Carly Dionne Williams for rock band, and I thank Elizabeth Van Wagner for









commiseration. I thank all the people in this section most importantly for being my friends, my

life would not be as full of fun, love and laughter without anyone of you.

Last but certainly not least, I thank Billy.












TABLE OF CONTENTS

page


ACKNOWLEDGMENT S ........._.._ ..... .___ ............... 4.....


LI ST OF T AB LE S ........._.._ ..... .___ ............... 10...


LIST OF FIGURE S ............... ............... 11... I


AB ST RAC T ................. ................. 16..............


CHAPTER


1 INT RODUC T ION ................. ................. 17......... ....


Introduction ........................ ............... 17
Alternate Imaging Modalities............... ............... 18

Optical Microscopy ............... .................... 18
Computed Tomography............... ............... 19
Ultrasound ............... .... .. ... .. .. ... ......... 21

Nuclear Magnetic Resonance and Magnetic Resonance Imaging ................. ............... .....23
Introduction ................. ................. 23..............

Background ............... .................... 24
Theory ................. ................. 26..............
T1 Relaxation Agents ............... .................... 3 0
Back ground ............... .................... 3 0
Clinical Use ................. ................. 32..............

T2 Relaxation Agents ........................... ........33
Background ............... .................... 33
Clinical U se .................. ... .... .. ... .. ................ 34....
Chemical Exchange Saturation Transfer Agents ........................... ........35
Back ground ............... .................... 3 5
Chemical Exchange ................. ... ................ 37
CEST versus Magnetization Transfer ............... .................... 39
High Field Advantage................ ............... 39

2 METHODS ............... .................... 50


Contrast Agent Preparation ............... .................... 50
Multimodal Quantum Dots ............... .................... 50
PARACE ST Agents ............... .................... 51
Iron Oxides ................. ................. 51..............
Phantom Preparation ............... .. .. ..... ............. 51
Magnetic Resonance Imaging and Spectroscopy ...._ ......_____ .......___ ...........5
Set U p ............... ... .................. 54
Rel axati on P aram et ers ........._...... ................ 5........ 5...
PARACE ST Agents ............... .................... 56
CE ST Spectra ................. ................. 57..............













Quantification of Contrast ............. ...... .__ .............. 58...
Sim ul nations ............... .................... 59

Rationale and Design............... ................. 59
CE STFIT ............... .................... 60

Cell Labeling ............... ....................60


3 RE SULT S ............... .................... 68


Relaxation Agents .............................. ........68
Multimodal Quantum Dots ............... ....................68
Iron Oxides ........._... ...... ._ ._ ............... 68...

PARACE ST Agents .................. .......... ...............69.......
Multiple Lanthanide lon Complexes .................... ............... 69
Field Strength ............. ...... ._ ...............70....
Concentration ............. ...... ._ ...............70....

pH ............... .................... 7 1
Tem perature ................ ....................72
Effect of Signal to Noise ................. ...............73...............
Presaturation Power and Duration ........._.__....... .__. ...............73...

Cell Labeling........................ ........74
Simulation............... ...............74
Relaxation Times ........._.__....... .__. ...............74....
Chemical Shift Difference................ ...............74

Chemical Exchange Rates ........._.__....... .__. ............... 75...
Pre saturation Power ........._.__....... .__. ............... 75...
Concentration ........._.__....... .__ ...............75....

Temperature Fit................... .................75
Number of Acquisitions Fit. ................. ...............76........... ...


4 DISCUS SION ................. ...............102................


Relaxation Agents .................... ................. 102
Multimodal Quantum Dots ............... .................... 102
Iron Oxides ........._... ...... ._ ._ ............... 103...

PARACE ST Agents .................. .......... ................. 104....
Multiple Lanthanide lon Complexes ........................... ........104
Field Strength Dependence ........................... ........105
Concentration Dependence ............... .................... 107

pH Dependence ............. ...... ._ ............... 107...
Temperature Dependence............... ............... 108
Effect of Signal to Noise ........................... ........109
Presaturation power and Length ........................... ........110
Cell Labeling ............... ................ 110
Sim ulation............... .............. 11
Rel axati on Times ........._.__....... .__. ............... 1 12..
Chemical Shift Differences ........._.__....... .__. ............... 114..

Chemical Exchange Rates ........._.__....... .__. ............... 114..












Presaturation Power ........._.___..... ._ __ ............... 115....
Concentration ........._.___..... ._ __ ............... 115....

Temperature Fit................... ................ 116
Number of Acquisitions Fit. ........._..._. ...._... ...............116...
Conclusions ............... .................... 116


5 FUTURE PERSPECTIVES ............... ....................121


APPENDIX


A IRON OXIDE S DAT A............... .................. 125


B PARACEST AGENTS DATA AND CALCULATIONS ................. ................. ....... 129


Ytterbium, Thulium and Europium PARACEST ............... ....................129
Europium PARACEST Field Dependence ....._ .....___ .........__ ...........12
Europium PARACE ST Concentration ............... .................... 129
Europium PARACE ST pH ................. ................. 13......... 0....
Europium PARACE ST Temperature ........._... ........... ..............._ 130..
Europium PARACE ST Si gnal to Noise .................... .... ............... 13 1
Europium PARACEST Presaturation Power and Duration ............... .....................131

C MATLAB SIMULATION. .........__........_. .............. 166...


M ATLAB Code F or mat................ ................ 166
Modified Bloch Equations............... ............... 166
Variable Chemical Shift Difference............... ............... 172
Variable Chemical Exchange ............... .................... 175
Variable Pre saturation Power ............ ..... .._ ............... 177..

Temperature Fit MATLAB Code................. .................180
Number of Acquisitions Fit MATLAB Code ....._......__. ..........._ ...........8

LIST OF REFERENCES ........................... ........193


BIOGRAPHICAL SKETCH ................. ...............206......... ......











LIST OF TABLES

Table page

3-1 T1 Relaxation values for 10 mM Eu-2 at multiple temperatures, measured at 14. 1 T.......93

B-1 CNR calculations for 4.7 T............... ....................138

B-2 CNR calculations for 17.6 T. .........._.... ...............139..._... ..

B-3 CNR calculations for 21.1 T. .........._.... ...............139.._.._. ..











LIST OF FIGURES


FiEure page

1-1 Nuclear magnetic moment bar magnet representation ......._. ..........._. ........._......4 1

1-2 Precession in presence of Bo .............. .................... 41

1-3 Nuclear magnetic moment alignment ............... ....................42

1-4 Nuclear magnetic moment with orthogonal magnetic field B1 ................. ............... .....42

1-5 Net x-y magnetization immediately after B1 is turned off. ................. ................. ...._43

1-6 Spin lattice relaxation (T1)............... ..................43

1-7 Spin-spin relaxation (T2)............... .................. 44

1-8 Gadolinium (T1 relaxation) contrast. .............. .....................45

1-9 Common gadolinium based contrast agents ................. ...............46...............

1-10 Iron Oxide (T2 relaxation) contrast ................. ...............46...............

1-11 Chemical Exchange Saturation Transfer (CEST) ................. ...............47........... ..

1-12 Example CE ST spectrum ................. ................. 48.............

1-13 PARACE ST Imaging ................. ................. 49......... ....

2-1 Multimodal Gadolinium Quantum Dots ................. ................. 63......... ...

2-2 Chemical Structures of three europium Complexes ................. ..............................64

2-3 Chemical structures of the thulium and ytterbium complexes ............... .....................65

2-4 Contrast to Noise Ratio (CNR) calculation region of interest diagram ................... ...........66

2-5 Percent Decrease (%Decrease) calculation region of interest diagram for Eu-2 ...............66

2-6 Example CEST spectra, with the %Decrease calculation shown. ............_ ... ......_........67

3-1 Quantum Dot T1 relaxation times. .............. .....................77

3-2 Quantum Dot T2 relaxation times................ .................77

3-3 Quantum Dot T2' relaxation Times ................. ...............78...............

3-4 Iron oxide T2' relaxation times............... ..................79











3-5 Europium PARACEST images at 17.6T ................................... 80

3-6 Thulium based PARACEST agents at 21.1T ....._._._ .... ... .... ...............80

3-7 Ytterbium based PARACEST agents at 21.1T .........__........_. ......_. .......8

3-8 PARACEST contrast as a function of magnetic field strength ........._...... ......._._.. .......81

3-9 Difference images of europium complexes at 17.6 and 21.1 T............... ................... 82

3-10 CEST spectra with variable magnetic field strength............... ................83

3-11 Eu-2 serial dilutions ............... .................... 83

3-12 Eu-2 concentration phantom images at 14.1 T, with a presaturation power of 8 CIT.........84

3-13 Eu-2 concentration phantom images at 14.1 T, with a pre saturation power of 128 CIT .... 85

3-14 Concentration versus contrast for serial dilutions ofEu-2 at 14.1 T, with
presaturation power of 64 CIT ............... .................... 86

3-15 CEST spectra at 14.1 T for Eu-2 serial dilutions ............... .................... 87

3-16 Eu-2 (10 mM) at two different pH' s, and in SM............... ...................87

3-17 Eu-2 pH phantom at 17.6 T. ........................... ........88

3-18 Difference images of Eu-2 pH phantom at 14.1 T color enhanced to represent the
corresponding CNR................ .................. 8 8

3-19 Eu-2 at pH values from 3 13 at 14.1T............... ..................89

3-20 Presaturation offset versus normalized signal intensity for Eu-2 at multiple pHs at
14. 1 T .............. .................... 8 9

3-21 Difference images of Eu-2 at multiple pHs at 14.1 and 17.6 T............... ....................90

3-22 CEST spectra for Eu-2 at multiple temperatures. .............. ...............91....

3-23 CEST spectra for Eu-2 with increasing number of scans ................. ................ ...._..92

3-24 Number of acquisitions versus CNR. ........... ....._ ......_ ...........9

3-25 Varying presaturation power difference images ............... ....................93

3-26 Presaturation power versus CNR for serial dilutions ofEu-2 at 14.1 T ............................. 94

3-27 Bulk water signal as a function of number of presaturation pulses ................. ...............94











3-28 Cell labeling image results at 17.6 T................ ................... 95

3-29 Simulated T1 relaxation variation............... ............... 96

3-30 Simulated T2 relaxation variation............... ............... 96


3-3 1 Simulated Aco variation. ................................... 97


3-32 Simulated variation in chemical exchange ............... .................... 98


3-33 Simulated presaturation power variation ........................... ........99

3-34 Simulated concentration variation. ................. ...............99................


3-35 Variable temperature data and fit ................. ...............100..............


3-36 Variable number of acquisitions data and fit ................. ...............101........... ..

4-1 Number of acquisitions versus CNR................ .................. 120

A-1 Iron oxide images at 4.7 T ............... .................... 125


A-2 Iron oxide images at 4.7 T ............... .................... 126


A-3 Iron oxide images at 14.1 T ................................... 126


A-4 Iron oxide images at 14.1 T ................................... 127


A-5 Iron oxide images at 21.1 T ........................... ........127

A-6 Iron oxide images at 21.1 T. ........................... ........128


B-1 Thulium complexes at 14.1 T............... ....................132


B-2 Ytterbium complexes at 14.1 T............... ....................133

B-3 Thulium complexes at 17.6 T............... ....................134


B-4 Thulium complexes at 17.6 T............... ....................135


B-5 Ytterbium complexes at 17.6 T ................................... 136

B-6 Ytterbium complexes at 17.6 T ................................... 137


B-7 Europium complexes CEST spectra at 14.1 T................ ...............138..

B-8 Serial dilutions at 14.1 T ................................... 140

B-9 Serial dilutions at 14.1 T ................................... 141











B-10 Serial dilutions at 14.1 T ................................... 142

B-11 Serial dilutions at 17.6 T............... ....................143

B-12 Serial dilutions at 17.6 T ................................... 144

B-13 Serial dilutions at 21.1 T ........................... ........145

B-14 Serial dilutions at 21.1 T ........................... ........146


B-15 CEST spectrum of Serial Dilutions at 21.1 T ........................... ........147

B-16 Variable pH phantom at 14.1 T. ........................... ........148

B-17 Variable pH phantom at 14.1 T ............... ....................149

B-18 Variable pH phantom at 14.1 T ............... .................... 150

B-19 Variable pH phantom at 14. 1 T ............... .................... 151

B-20 Variable pH phantom at 14. 1 T ............... ....................152

B-21 Variable pH phantom at 14.1 T. ........................... ........153

B-22 Variable pH phantom at 14. 1 T ............... .................... 154

B-23 Variable pH phantom at 14.1 T ............... .................... 155

B-24 Variable pH phantom at 17.6 T ............... .................... 156

B-25 Variable pH phantom at 21.1 T ............... .................... 157

B-26 Variable pH phantom at 21.1 T ............... ....................158

B-27 Variable pH with media phantom at 17.6 T. ........................... ........159


B-28 CEST spectrum of 10 mM Eu-2 at 14 OC. ........................... ........160


B-29 CEST spectrum of 10 mM Eu-2 at 20 OC ................. ...............160............


B-30 CEST spectrum of 10 mM Eu-2 at 26 OC. ........................... ........161


B-31 CEST spectrum of 10 mM Eu-2 at 320C. ........................... ........161


B-32 CEST spectrum of 10 mM Eu-2 at 3 8 OC. ........................... ........162

B-33 CEST spectrum of 10 mM Eu-2 for one acquisition. .............. .....................162

B-34 CEST spectrum of 10 mM Eu-2 for two acquisitions. ........................... ........163










B-35. CEST spectrum of 10 mM Eu-2 for four acquisitions. ................... .............. ..163

B-36 CEST spectrum of 10 mM Eu-2 for eight acquisitions. .............. ......................164

B-37 CEST spectrum of 10 mM Eu-2 for 32 acquisitions ................. ............... 164...........

B-38 CEST spectrum of 10 mM Eu-2 for 64 acquisitions ................. ............... 165...........

B-39 Presaturation power and duration effect on CNR ........................... ........165

C-1 CEST spectrum and fit for one scan. R2 = 0.995. ........................... ........190

C-2 CEST spectrum and fit for two scans. R2 = 0.989. .............. .....................190

C-3 CEST spectrum and fit for four scans. R2 = 0.990............... ...............~~~191

C-4 CEST spectrum and fit for eight scans. R2 = 0.991. ............. .....................191

C-5 CEST spectrum and fit for 32 scans. R2 = 0.985. ............. ...............192....

C-6 CEST spectrum and fit for 64 scans. R2 = 0.989. .............. .....................192









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

OPTIMIZATION OF CONTRAST AGENTS FOR HIGH MAGNETIC FIELDS

By

Heather H. Cornnell

August 2009

Chair: Arthur S. Edison
Major: Biomedical Engineering

Magnetic Resonance Imaging is one of most popular medical imaging modalities with the

advantages of noninvasive and nondestructive high resolution images. Utilizing high magnetic

fields can provide increased signal to noise resolution, but current contrast agents are not

optimized for use in fields above 3 T. Several types of contrast agent were investigated to

evaluate their potential application for cellular or molecular imaging at high magnetic fields in

the future. The results indicated that T1 relaxation agents (gadolinium-based) were best used at

lower magnetic field strengths, but two others, T2 relaxation agents (iron oxides) and

PARACEST agents, hold great promise for high field contrast generation. These results could be

applied to future studies to enhance contrast, gain higher resolution, advance understanding of

cellular or molecular function, and contribute towards a dual anatomical -metaboli c imaging

modality of the future.









CHAPTER 1
INTRODUCTION

Introduction

The purpose of this proj ect was to optimize contrast for high field magnetic

resonance imaging, with the ultimate goal of enhancing molecular imaging. Molecular

imaging enables the visualization of cellular function and molecular processes in living

organisms. Alternate imaging modalities were considered, but due to its advantages and

accessibility, magnetic resonance was chosen. Currently MRI is widely used in hospitals

at 1.5 and 3 T to provide excellent anatomic information. Using higher field (4.7 -

21.1 T) magnets can increase the resolution for even better anatomical images.

Unfortunately, no functional or physiologic information can be obtained using traditional

MRI. If improvements in MRI contrast could allow functional and physiological

information to be obtained, the results would have nearly limitless possibilities.

Advances towards imaging cell function and/or physiologic processes could aide in

earlier disease detection and diagnosis, as well as evaluation disease progression and

possibly di sease prevention by detecting very early signs. It could also be useful for

developing better treatments through optimized clinical trials, targeted treatment and

better treatment evaluation. This improvement in imaging could have long range

economic effects due to earlier and more precise diagnosis, extended life span and better

quality of life. The myriad of possible benefits from molecular imaging was the driving

force in the investigation of high field magnetic resonance imaging contrast.









Alternate Imaging Modalities


Optical Microscopy

When discussing molecular imaging, the topic of microscopy inevitably arises, as it

is well known for imaging on a cellular level, and remains a maj or method to verify other

imaging modalities. Traditional optical imaging uses visible light and a system of lenses

to produce a magnified image of an obj ect, sample or specimen [1]. Microscopy literally

refers to image visualization on a microscopic (10-6 m) lCVel and light microscopy has a

maximum resolution which is limited to the wavelength of the light source used [2].

However scanning electron microscopy and transmission electron microscopy have

resolutions on the order of 10-10 m [3].

There are several types of optical imaging contrast agents. Stains or dyes can be

used provide contrast or change the color of the desired sample. Fluorescent dyes or

proteins can also be used to cause the obj ect of interest to fluoresce when excited with a

specific wavelength of light. In recent years the ease and specificity of fluorescent

staining or labeling has increased substantially [4] and fluorescent labeling cells or tissues

has become one of the most important research methods available [5]. Methods

employing multiple fluorescence sources can study reaction mechanisms, nearest protein

neighbors, and interaction sites using fluorescence energy transfer (FRET) which uses the

excitation of one fluorochrome to excite the second if the two are within a molecular

distance of each other [1]. The methods using gene constructs like enhanced green

fluorescent protein (eGFP) are especially promising for molecular imaging, because only

the labeled cells which are expressing eGFP will fluoresce, providing a highly specific

gene marker which effectively doubles upon cell division.









Microscopy currently has very high resolution, but suffers from the general limited

depth penetration (due to absorbance or scattering of the excitation light and scattering of

the reflected/emitted light). This is often circumvented by thinly slicing the sample,

which has the disadvantage of invasive sample removal and destruction. These are the

main disadvantages to choosing optical microscopy as an option for molecular imaging.

Investigation of MRI as a potential molecular imaging modality was chosen as a

noninvasive alternative to optical imaging, but optical imaging will be used for

verification for the foreseeable future.

Computed Tomography

Computed Tomography (CT), also known as Computerized Axial Tomography

(CAT), is a clinical imaging modality which utilizes x-ray images, taken at different

angles, to create a 2D axial slice of the obj ect being imaged. The axial view allows better

visualization than conventional x-ray images. As such, use of CT is growing as a popular

clinical imaging tool, with 62 million scans completed in 2008 [6]. CT provides

anatomical images with resolutions on the order of 107 m or better [7,8], which can be

used to differentiate between bone and soft tissue, including organs, muscles and tumors.

Software is used to manipulate the signal to increase resolution for different tissues,

allowing the visualization of the lung airways, blood vessels or the visualization of bone

[9]. Methods of multi-planar reconstruction combine multiple axial (transverse) slices to

create a 3D image [7], which can then be used to create an image in alternate orientations

(e.g. saggital or coronal). The different orientations can be used to aid in diagnosis, pre-

surgical planning or other treatment, including radiation therapy where effective dosage

is highly dependent on precise density, size and location of the tumor.










X-ray, and therefore CT, images are created by transmitting x-rays through the

patient, and detecting the remaining x-ray signal on the opposite side of the body.

Inherent contrast is generated in CT images due to the differential attenuation of the x-ray

signal between tissues [10]. For additional contrast, radiopaque dyes including barium

sulfate or iodine are administered most often orally (barium or iodine) or intravenously

(iodine) depending on the area where contrast is desired. These contrast agents provide

positive contrast by increased attenuation of the x-ray signal [1 l]. The toxicity of the

iodine agents is low, but contraindications may arise from known allergic reactions,

which can increase the risk of severe reaction from fourfold for patients with a hi story of

nonspecific allergy up to 11i-fold for patients with a hi story of reaction to contrast media

[12]. Additional contraindications include hyperthyroidism (as increased amounts of

iodine in the system may, especially in patients with hyperthyroidism, cause increased

production of thyroid hormones which can be toxic) and impaired renal function (as the

patients would have difficulty eliminating the contrast) [l l].

Recently, CT has been combined with Positron Emission Tomography (PET) to

create images with anatomical and metabolic information. In contrast with CT and other

traditional anatomical imaging modalities, PET images the "spatial distribution of

radiopharmaceutical s introduced into the body [10]." The radiopharmaceutical s or

'radiotracers' consist of an isotope that decays by releasing positrons linked to a chemical

substrate. The structure of the substrate determines the distribution of the tracer

throughout the body, and often take advantage of the increased metabolic activity of

malignant tumors [13]. The radiotracer decays and each releases a positron, which upon

encountering an electron is annihilated and releases a pair of photons (y-rays). PET









detects the pairs of photons, and generates an image showing the distribution of the

radiotracers, and thereby the general location of metabolic activity. But PET alone does

not result in anatomic information, and therefore is often combined with another imaging

modality. The combination of CT and PET allows for good spatial localization of

metabolic information [13], but suffers from increased radiation.

CT can also be combined with single photon emission computed tomography

(SPECT), which like PET uses radio pharmaceuticals. The main difference is that the

radiopharmaceutical in SPECT releases a single y-ray upon decay. Because PET results

in two y-rays, it has an increase in sensitivity two to three orders of magnitude greater

than SPECT [10].

In the New England Journal of Medicine, Brenner and Hall [6] report that the

radiation doses administered during repeated CT scans may actually increase the risk of

those patients developing cancer further down the line, with a possible 0.4% of all

cancers in the United states attributable to the radiation from CT studies [14, 15]. CT also

requires patients to lie still for the duration of the scan, and can therefore suffer from

motion, or breathing related image distortions. MRI shares motion related distortions

with CT, and maintains similar resolution and neither have depth penetration issues.

Ultrasound

Ultrasound works by transmitting sound waves from a transducer into the body.

The sound waves travel through tissue until they hit an interface that reflects the sound

wave back to the probe (called a "reflector") [16]. By assuming that the speed of sound

in biological tissue is 1540 m/s [16] and that the sound wave travels in a straight line to

and from the transducer, the ultrasound machine can calculate the distance from the









transducer to the reflector. Ultrasound images are formed by transmitting very thin

beams of sound waves then receiving the reflected sound waves repeatedly and

assembling the resulting data [17].

Contrast within an ultrasound image is caused by the interface between two tissues.

The interfaces reflect sound b because di fferent types of ti ssue propagate sound at di fferent

speeds and have different tissue density (combined, these are referred to as Impedance

[16]). Most soft tissues have similar impedance values, with bone and air having much

higher and much lower values, respectively [10]. The small differences cause detectable

reflections while the large differences cause near total reflection (as with bone) or

negligible reflection (as with air). The large impedance of bone and air, cause their

presence to hinder ultrasound imaging. Resolution depends on the size of the ultrasound

pulse, with typical axial (parallel to the US pulse) resolution values of 0.5 to 1 mm and

typical lateral (perpendicular to the US pulse) resolution values of 2 to 5 mm [8]. The

depth penetration is limited by the pulse repetition period (time between pulses), which is

limited by the potential for an echo to return from a large depth to the transducer after the

next pulse has occurred. The depth penetration depends on the pulse frequency, but is

limited to 38.5 cm (for a pulse frequency of 2 k
time of 500 Cls) [8].

To obtain greater sensitivity, contrast is used to enhance the ultrasound signal.

Microbubbles are <10 Clm in diameter (thus can safely cross capillary beds) and are

generally inj ected intravenously where they remain in the vasculature [17]. In this way

they can be used to obtain better visualization of vascular abnormalities or tumor

vasculature. Specifically, microbubbles work to enhance the signal using harmonics [18].









This phenomenon is accomplished through the reverberation of the bubble walls. The

ultrasound pulse causes the bubble to initially contract, in response to the increased

pressure due to the ultrasound wave. After the initial contraction, the bubble expands,

then contracts, and expands again, in a harmonic fashion. This reverberation causes an

increase in the signal received by the ultrasound instrument. The bubbles are stabilized

with a biodegradable shell to ensure they do not burst or merge into larger bubbles. As

with contrast agents used for other types of imaging modalities, there is currently a trend

in research towards targeting the ultrasound microbubbles to specific molecules [19-21].

By adding functionalized groups to the surface of the microbubble, these groups can be

used to make the bubble stick to specific surfaces. This functionality could possibly be

used for disease diagnosis, drug delivery, and potentially molecular imaging.

Ultrasound is currently a widely used technology for imaging human beings, with

the advantages of being easily portable, noninvasive and relatively inexpensive [10].

Ultrasound is an extremely safe method of imaging with no limits on the number of

patient examinations over a given amount of time set forth from the FDA [10]. Despite

the safety, it suffers from relatively poor resolution and limited depth penetration.

Nuclear Magnetic Resonance and Magnetic Resonance Imaging

Introduction

The concept of Nuclear Magnetic Resonance (NIVR) was first described in 1938,

by Isidor Rabi, as a direct measurement of nuclear magnetic moments [22], and he was

awarded the Nobel Prize in Physics in 1944 for this discovery. In 1946, the first N1VR

experiments were conducted independently by Felix Bloch [23], at Stanford University,

and Edward Purcell [24], at Harvard University. These experiments led to development

ofNIVR as a new spectroscopic technique that has since proven to be a versatile, accurate









and dependable method of chemical research [25]. Bloch and Purcell were j ointly

awarded the Nobel Prize in physics in 1952 for their work in NMR. Richard Ernst made

the observation that sensitivity could be greatly enhanced by computing the spectrum

from a free induction decay (the resulting recorded response to a short r.f. pulse) by

means of a numerical Fourier Transformation (FT) [26,27]. He was then able to apply

this transformation to develop 2D FT NMR methods [27]. For hi s contributions Ernst

received the Nobel prize in Chemistry in 1991, and he is often recognized as one of the

maj or early contributors to MRI. The principles of NMR spectroscopy were utilized by

Paul Lauterbur and Peter Mansfield to develop a technique for noninvasive MR imaging.

Paul Lauterbur incorporated a weaker gradient magnetic field for spatial localization [28],

and Peter Mansfield introduced the k-space formalism [29] and the concept of

transversing reciprocal space [3 0]. The two were maj or contributors to the development

of Magnetic Resonance Imaging (MRI), and were j ointly awarded the Nobel Prize in

Medicine in 2003 for their discoveries.

Background

Magnetic Resonance Imaging has become one of the most powerful imaging

modalities in biomedical research [31]. Some of its advantages include being

nondestructive and noninvasive; there are no known long-term negative side effects on

human beings (unlike the radiation exposure in CT scans). Different tissues have

inherent contrast differences based on their relaxation rates and special sequences can be

used to enhance the detail of certain tissue types, or negate the effects of others.

However, MRI is relatively slow and is sensitive to motion artifacts, which is to say if the

patient moves during the scan it can affect the quality of the image. Additional

disadvantages (which generally are outweighed by the advantages) include patient









discomfort. This discomfort can be due to confinement in the small space of the scanner

during the imaging. Also, there are multiple sources of acoustic noise, which can be

disconcerting or uncomfortable for the patient. Noise can arise from gradient or eddy

currents, or radio frequency and slice select pulses [32], and higher fields can generate

higher levels of noise [33]. In addition to patient discomfort, the noise can interfere with

certain functional MRI studies [34]. Due to the small net magnetization (discussed

below) the sensitivity of MR techniques are low when compared to the hi gh contrast and

sensitivity of PET.

MRI is widely considered to have a good safety profile, especially when compared

to its main anatomical imaging alternative CT, which exposes the patient to large do ses

of radiation [35]. Side effects from MR imaging have been shown to be mainly transient,

and can include a metallic taste, vertigo, nausea, and magnetosphenes (flickering lights)

[36-38]. Despite this safety profile, there are patients who will always be

contraindicated for MR imaging, mainly those with magnetic metal implants, shrapnel, or

pacemakers. Six of seven reported fatalities associated with MR imaging in 1998 were

caused by either movement of metal (aneurysm clip) or the presence of a pacemaker (the

link between fatality and the MR scanning was uncertain in several of these deaths) [3 7].

There is some concern that increasing the static magnetic field strength may lead to

increased side effects [37,39-41], but the results on humans in fields up to 8 T and

animals up to 16 T indicate no decrease in the safety [37]. As it is used clinically today,

MRI i s generally considered safe and effective [3 9].

Currently there is a limit of the detail that can b e seen using MRI. Higher

resolution can be attained with longer scanning times, but this can lead to an increase in









motion artifacts and patient discomfort. One alternative is to use chemical agents to alter

the signal and cause increased contrast. Currently, relaxation agents are commonly

employed to alter the contrast at clinical field strengths of<1.0-3 T. Higher magnetic

fields can be employed to increase signal, increase resolution, and decrease scan time

[42]. The combination of increased signal from the higher field and increased contrast

from the chemical agent may allow for much higher resolution than is currently available

in clinical imaging. Thi s increase in resolution would be beneficial for various medical

reasons including reduced scan time and earlier disease detection. Also, increased

resolution could contribute towards advanced research in disease treatment because it

could be used for drug tracking or to obtain physiological information.

Theory

In order to explain nuclear magnetic resonance, two main nuclear properties mu st

be discussed: nuclear spin and nuclear magnetism. Spin is an intrinsic property of nuclei

which is difficult to conceptualize but may be simplified to mean that the nucleus

behaves as if it is spinning, similar to a planet rotating about its axis in space [43].

Classically, any obj ect that rotates or spins about itself is said to have angular

momentum, visualized as a vector pointing along the axis of rotation [43]i. Nuclear

momentum is quantized, resulting in distinct energy levels. Nuclear spin is a form of

angular momentum which is not produced by the rotation of a particle but is an intrinsic

property of the particle [43]i. Nuclear spin can also be quantized resulting in separate

energy levels which depend on the spin quantum number of the elemental particle. For

protons, the quantized spin (S) is V/2 Which results in a two state spin polarization which is

oriented independent of any spatial dimensions. In the absence of an external field the

two states are degenerate, but in the presence of an applied magnetic field the energy









levels split and the nuclear spins distribute between the energy levels according to the

Boltzmann Distribution (Equation 1-1).


N ,



In Equation 1-1, N, refers to the number of spins in the lower energy state (ot), Np refers

to the number of spins in the higher energy state (P), AE is the energy difference between

the two states, kB is Boltzmann's constant and T is the temperature. The energy

difference causes a difference in the population of spins between the high and low energy

states, and this causes the spin polarization to develop. The spin polarization determines

the basic MRI signal strength. Spin polarization may be simplified by viewing it as

classical angular momentum with the axis of rotation aligning either parallel or anti

parallel to the applied magnetic field. While technically incorrect, the simplification is

often employed to describe IVRI. At room temperature thi s results in a polarization ~1

ppm with an applied 0.3T magnetic field [44].

Nuclear magnetism refers to the interaction of the nucleus with a magnetic field.

An additional simplification can be made by visualizing nuclear magnetism, usually

expressed in terms of the magnetic moment (C1), as the particle being a tiny bar magnet

(Figure 1-1). Nuclear magnetism and spin are proportional, as shown in Equation 1-2.

p = 7S (1 -2)

The proportionality constant y is the gyromagnetic ratio and S is the spin angular

momentum [43]. Because the spins have both angular momentum and magnetism, when

they 'align' with the applied magnetic field, they process (Figure 1-2) about an axis










aligned with Bo [43]. The frequency (coo) of this precession is called the Larmor

frequency (Equation 1-3).

coo = -yBo (1-3)

The sign of the Larmor frequency refers to the di reaction of precession, clockwi se or

countercl ockwi se about Bo [43].

At room temperature in the absence of and external magnetic field, the spins states

are degenerate and the magnetic moments in a given sample are uniformly distributed in

all possible directions in space [43]. The sum of the randomly oriented nuclear magnetic

moments is essentially zero, resulting in no net magnetization (Figure 1-3-A). MRI uses

a static magnetic field (Bo) to align the nuclear magnetic moments of the atoms in the

sample (Figure 1-3 -B). Bo causes the atoms in the sample with individual spins to align

either parallel or antiparallel with the magnet, according to the Boltzmann distribution

(Equation 1-1), with a small excess parallel. The resulting slight excess results in an

overall net magnetization (Mo), which aligns with Bo (referred to as along the z-axis).

The atoms' nuclear magnetic moments are aligned with Bo, but randomly

distributed in the x-y plane (Figure 1-4-A). Radio frequency (RF) coils are used to

induce an orthogonal magnetic field B1, which changes the alignment of the nuclear

magnetic moments into the x-y plane (Figure 1-4-B).

Once B1 is turned off, the nuclear magnetic moments return to alignment with Bo

(the static field). This realignment with Bo causes a changing magnetic field, which

induces a current in the receiver coils, as depicted in Figure 1-5. This signal is a

sinusoidally dampened decay, called the Free Induction Decay (FID), and is converted

into the frequency domain using the Fourier Transform. Gradient coils are used to










produce controlled variations in the main magnetic field (Bo) which effect the Larmor

frequency of the spins. By changing the frequency of precession, the resulting signals

can be differentiated between different spatial locations within the image. The image is

created through the difference in intensities in the peaks per pixel.

There are two mechanisms of nuclear spin relaxation. Spin-lattice relaxation refers

to the mechani sm by which the nuclear spin magnetization returns along the z axi s

(aligned with Bo). The interaction between the spin and its environment causes this

relaxation, which is characterized by a time constant T1. Any time the magnetization is

not in equilibrium, T1 relaxation occurs. For example, after the application of an

appropriate B1 the system equilibrium was disrupted and the net magnetization along the

z axis is approximately zero and the spins are not in equilibrium. Through spin lattice

relaxation the spins recover to the equilibrium net magnetization (Figure 1-6). In general

anything that can give rise to fluctuating magnetic fields can contribute to this relaxation

including chemical shift anisotropy, magnetic dipole-dipole and electric quadrupole

interactions [31]. Temperature and field strength also have an effect on T1 [45].

The second relaxation mechanism is spin-spin relaxation, characterized by T2.

Spin-spin relaxation refers to the mechanism by which the nuclear magnetization

disappears (dephases) in the x-y plane orthogonall to Bo). While T1 refers to the net

magnetization returning on the z axis, T2 TeferS to the dephasing of the spins in the x-y

plane. At equilibrium, all the spins in the x-y plane are out of phase (Figure 1-4-A); they

cancel each other resulting in a net magnetization with only a z component. When a 90

degree pulse is applied, the net magnetization is in the x-y plane (Figure 1-4-B). After

B1 is turned off, the spins begin to dephase, and eventually the net magnetization in the x-










y plane returns to zero (Figure 1-7). This relaxation occurs because of the interaction of

the spins with each other, but may also be due to inhomogeneities in the Bo field [31].

T2* TeferS to the relaxation caused by both effects, and varies between instruments.

Differences in T1 and T2 between samples (or regions within samples) cause

different signal intensities, and create contrast within images. Different pulse sequences

can be used to increase the contrast effects due to these inherent differences. Chemical

agents containing paramagnetic ions have been described for contrast generation as early

as 1978 by Maria Helena Mendonga-Dias and Paul C. Lauterbur [46]. Since then

research using chemical agents to increase these differences in signal intensity resulting

in image enhancement has grown significantly.

T1 Relaxation Agents

Background

There are a myriad of uses for increased resolution, and one promising path to

obtain this resolution is through contrast agents used at high magnetic fields. Caravan,

Ellison, McMurry and Lauffer [47] published a detailed description of paramagnetic

agents used for MRI contrast generated through the interaction of the paramagnetic

agents with water. This interaction changes the local magnetic environment to which the

water protons are exposed. This alteration induces a change in the relaxation properties

of water, resulting in modifications of signal and contrast. Relaxation contrast agents can

cause concentration dependent changes in T1 or T2.

Gd (III) can be used to induce a change in the local magnetic environment

resulting in a change in T1. Free Gd (III) is toxic [48], but chelating ligands greatly

reduce the free Gd(III) toxicity [49]. Gd (III) based chelates are commonly used because

when compared to other lanthanide ions, Gd (III) is more efficient at relaxing bulk water









protons [50]. This type of contrast is generally directly related to the amount of Gd

present, which can be limited by stability of the chelating ligand and allowable

concentration of inj section [49]. Once present in the tissue, the contrast remains altered

until all of the Gd has been eliminated from the system, which i s uaually around 24 hours

[51] in healthy patients. Gadolinium has recently come under increased scrutiny as a

result of a possible association with increased incidence of nephrogenic systemic fybrosis

(NSF) [52]. While no direct link was found, the American Medical Association still

recommended di continuing the use of gadolinium based contrast agents in patients with

renal disease. The safety profile of Gd chelate contrast agents is still considered

especially good when compared to profiles ofiodinated x-ray agents [53].

T1 relaxation agents like those based on Gd work through inner and outer sphere

relaxation as illustrated in Figure 1-8. The paramagnetic ion, in Figure 1-8 Gd-TSPETE,

creates a fluctuating magnetic field which provides a relaxation pathway for the (inner

sphere) water protons via electron-proton dipolar coupling, which drops off with distance

[54]. The most efficient T1 relaxation agents have fast water exchange (characterized by

a small zM) so that a large number of water protons experience the inner sphere

relaxation. Inner sphere relaxation is also characterized by the rate of rotation

(characterized by the rotational correlation time Tr), and in general slowing down the

tumbling rate increases the relaxation [55]. Slowing down the frequency of rotation of

the Gd complex results in the fluctuations of the paramagnetic field being closer to the

Larmour frequency, which causes more efficient relaxation [54]. The last factor in T1

relaxation is diffusion (characterized by the correlation time tD) which allows bulk water









molecules surrounding the Gd complex (in the outer sphere) to experience relaxation

[54].

Clinical Use

Magnetic Resonance Imaging (MRI) is a clinical imaging modality with many

advantages including good spatial resolution, multiple intrinsic contrast mechanisms,

chemical specificity and nondestructive analysis. Enhancements in contrast can be

generated by the interaction of water with paramagnetic gadolinium (Gd) chelates, which

change the local magnetic environment to which protons in water are exposed. This

alteration induces a change in the relaxation properties of water, resulting in

modifications of signal and contrast. An overwhelming maj ority of contrast enhanced

clinical exams are performed with gadolinium complexes, with nearly 10 million MRI

studies are performed with gadolinium each year [56]. Gadolinium is currently used in

different forms as a contrast for MRI, including gadobenate dimeglumine (Multihance@),

and gadopentate diglumine (Magnevist@) [57]. Both agents have similar chemical

structures [58,59] (Figure 1-9) and work by changing the magnetic relaxation properties

of water protons. This is accomplished through interaction of water in human tissue with

the contrast agent. Since the tissue interacts with the Gd chelate, its relaxivities

(rl=(A(1/T1 ))/[Contrast Agent] and r2=(A(1/ T2))/[COntrast Agent] [60]) are altered. As

such, any area that is imaged that contains the contrast agent will appear brighter on an

MR image. Gd based agents are generally excreted partly by the kidneys and partly by

the biliary system.

Contrast agents have been shown to enhance different types of lesions/disease

differently. Some di sease states do not require contrast. For example, conventional MRI









has been shown to demonstrate vascular malformations such as AVMs, capillary

telangiectases, cavernous angiomas and venous malformations [57] because of the detail

of the parenchyma shown while simultaneously showing the venous malformation.

However, the use of contrast agents can help to further improve the delineation of feeding

arteries and draining veins when trying to assess the presence and severity of brain

aneurysms, venous thrombosis, central venous drainage, periventricular or deep location

or occurrence of previous hemorrhage (within the brain). Also, contrast use is important

to the diagnosis of several types of tumors. Contrast enhanced imaging is extremely

useful in the evaluation of infectious types of lesions like meningitis, encephalitis, or

abscess formation. Although the enhancement may be nonspecific, the contrast images

used in conjugation with other special sequences (such as diffusion weighted imaging, as

previously discussed) can still be used to diagnose the lesion.

In addition to the use for diagnosis of a disease/lesion contrast enhances MRI can

be used pre-surgery to give the surgeon a better map of the area which needs treatment.

MRI-guided bi opsy i s a growing area of clinical importance. Thi s i s due in part to the

tissue contrast which can be provided using MRI (and contrast agents) which cannot be

provided using ultrasound or CT.

T2 Relaxation Agents

Background

In addition to T1 relaxation agents, there are T2 Or T2' relaxation agents, which

generally contain iron oxides. The iron-oxide agents are commonly ferumoxides

(FeO1.44) and can be SPIOs (superparamagnetic iron oxides and USPIOs (ultra-small

superparamagnetic iron oxides) which are smaller in size.









Similar to gadolinium agents, iron oxides also induce a change in the relaxation

properties of water, but instead of T1 relaxivity, the iron oxides influence T2 TelaXiVity.

Unlike Gd contrast agents, iron oxides do not require chelating ligands, but they are

dextran or carbodextran coated to prevent bioreactivity. They are not toxic at low

concentrations, but the FDA has set guidelines to the amount of iron that can be

administered during one time (.56 to .8 mg of iron/kg body weight depending on the type

of agent) [61]. Like Gd based contrast agents, once present in the blood the contrast

remains until all of the iron oxide has been eliminated (25hrs from blood, 7 days from

system [62]).

Iron oxides affect the T2 relaxation parameters through outer sphere as shown in

Figure 1-10. The iron oxides in superparamagnetic particles have a combination of Fe2+

and F e3+ ions, which are aligned in one of two specific orientations. The Fe3+ ions cancel

each other out, but there are some uncompensated Fe2+ ions, which result in a large

magnetic moment [63]. This large magnetic moment induces strong local field gradients

(Figure 1-10) which cause the surrounding water protons to quickly lose phase coherence

(dephase) [54]. This rapid dephasing is not limited to close (inner sphere) water protons

and can be seen at a considerable distance [54].

Clinical Use

Contrast agents containing iron-oxides are useful in the liver and spleen because

they efficiently accumulate in those areas within minutes ofinj section [64]. Their primary

affect is on the T2 TelaXiVity of the ti ssue. SPIO particles are useful in the diagnosis of

different liver lesions because lesions remain unchanged, while the SPIO particles are

taken up by normal liver tissue, specifically the Kupffer cells [61]. This causes a

reduction of the signal intensity in the normal liver while the lesion signal intensity









remains unchanged. This results in increased signal difference between the liver and the

lesion, making the lesion appear bright and more easily di stinguished; the use of iron

oxide contrast has been shown to significantly improve the number of liver lesions

detected compared to unenhanced MRI [65].

The safety profiles of iron oxides are slightly less desirable than those of the Gd

chelates [53]. In a 1995 phase III clinical trial, a study involving 208 patients reported no

serious reactions to ferumoxides, but reported adverse reactions in 15% of the

patients [62]. The most common negative side effects are include mild to moderate back

pain and flushing [61,62]. Other reported side effects included nausea, vomiting,

diarrhea, headaches, body pain (neck, chest, abdomen), dry mouth, dizziness, muscle

spasm, leg cramping and cold extremities [62]. Most symptoms were mild and resolved

without additional complications. The disadvantages of the adverse reactions generally

are outweighed by the increased detection of liver lesions.

Limitations of this contrast agent include a high incidence of false positives

[53,66], cellular internalization of iron oxides may be metabolically unfavorable (to the

cells) [67] and as with Gd agents, the contrast remains present in any images until the

agent has been cleared from the system. However, iron oxides maintain the advantage of

at least an order of magnitude greater in sensitivity than paramagnetic contrast agents.

Chemical Exchange Saturation Transfer Agents

Background

Recent studies [68-70] have proposed an alternate method for contrast generation,

adapting chemical exchange-dependent saturation transfer (CEST) methods along with a

new type of chemical contrast agent. These agents contain paramagnetic lanthanide ions

that have been shown to induce termendous NMR hyperfine shifts in nearby NMR









nuclei [50,71]. Paramagnetic Chemical Exchange Saturation Transfer (PARACEST)

uses a chelated paramagnetic lanthanide ion with an exchanging site which binds to

water. This bound water is in constant exchange with the surrounding bulk water (Figure

1-11-A). The bound water is chemically shifted away from the bulk water through its

interaction with the lanthanide ion. Supplying an RF pulse at the chemical shift induced

by the paramagnetic lanthanide ion, saturates the bound water protons (Figure 1-11-B).

Nuclei maintain magnetization through the process of chemical exchange, so when the

bound water exchanges with a bulk water it retains saturation (Figure 1-11-C). Because

the system is in constant chemical exchange, applying saturation at the chemical shift of

the bound water protons for an amount of time allows the system to build up the number

of saturated (or transfer the magnetization to the) bulk water protons (Figure 1-11-E),

which results in a decrease in the overall water signal.

This phenomena can be utilized to measure chemical exchange in spectroscopy or

produce contrast during imaging. A CEST spectrum (also referred to as Z spectrum) is

the result of selectively presaturating a range of frequencies and plotting the remaining

bulk water signal intensity as a function of presaturation offset (Figure 1-12). It can also

be used to generate contrast in an imaging experiment. If the RF saturation is applied on

the resonance of the bound water protons (Figure 1-1 3-A), a decrease in the bulk water

signal will be observed, but only where the lanthanide complex is present (Figure 1-13-

B). The same RF saturation is applied on the opposite side of water (Figure 1-13-A), and

there is no decrease in the water signal (Figure 1-13-C). The contrast is further

elucidated by taking the difference between the two images (Figure 1-13-D).









Despite resulting in a decrease in signal intensity similar to SPIO contrast agents,

CEST images have the advantage that they can be turned on and off for different

lanthanide (III) paramagnetic chelates (PARACEST agents) through the use of frequency

selective presaturation pulses. PARACEST agents are particularly useful due to their

large chemical shifts and long life times of bound water, which can result in a more

efficient transfer of magnetization to bulk water.

Chemical Exchange

By definition PARACEST agents rely on chemical exchange with water to carry

the magnetization of the nuclei from the bound to the bulk water pool. An estimation of

the effect of chemical exchange on the bulk water signal is given by Equation 1-4 [70].


"n [1 ks, ,] (1-4)


Mon is the magnitude of the bulk water signal with presaturation on the resonance of the

bound water pool, Mopp is the magnitude when the presaturation is on resonance on the

opposite side ofwater, kl is the pseudo first order exchange rate constant and Tisat is the

spin-lattice relaxation rate of the bulk water signal with saturation of the bound water

protons. The pseudo first order exchange rate (kl) can be simplified as the single

exchange site rate constant (kex) for simple reactions multiplied by the total number of

exchange sites available (proportional to the concentration) [70].

The estimation of effect of chemical exchange (Eqn 1-4) shows that systems with

longer T1 values allow for a larger range of viable exchange rates because the longer T1

values allow the spins to maintain their magnetization longer. It also shows that as long

as the exchange is sufficiently rapid, the signal associated with exchanging sites can be

detected through ob servation of changes in bulk water [70].









However, if exchange is too fast, and the system is in fast exchange, the spectral

distance between the bulk and bound water proton pools would be too small, and the

peaks would coalesce. In order for viable contrast generation, the chemical exchange

must occur to fulfill the slow to intermediate exchange requirement as shown in

Equation 1-5 [72].

Am rg, > 1 (1-5)

Equation 1-5 is alternatively written as Equation 1-6.


- 2 (1-6)


Amo is the frequency (or chemical shift) difference between the bound and bulk, zM is the

bound water lifetime which is the inverse of the single site exchange rate constant (kex).

The chemical exchange rate is dependent on other factors such as pH, temperature and

ionic environment [70]. As a result PARACEST agents have been developed to monitor

pH [73 -76] and temperature [77-79], both of which are dependent on chemical exchange.

The slow to intermediate exchange requirement shows the dependence of CEST

contrast generation on both exchange rate and chemical shift difference. The chemical

shift difference in PARACEST is caused by the lanthanide ion, and each lanthanide has a

difference Amo. Larger chemical shift differences have an advantage of allowing faster

exchange rates while maintaining a discrete spectral difference between the bound and

bulk water protons [72]. Large Amo (>2ppm) differences can also increase specificity by

avoiding direct saturation of water [70]. They can also allow broad band excitation

without direct saturation effects on water protons, which can avoid the magnetic

susceptibility resulting from poor/inhomogeneous saturation.









CEST versus Magnetization Transfer

CEST utlizes saturation similarly to Magnetization Transfer (MT) techniques,

which uses a single offresonance irradiation to generate contrast based on

macromolecule-water proton interactions [80,81]. The macromolecules used to create

MT are endogenous, and can sometimes be overproduced or in higher abundancies in the

brain as a result of white matter diseases; therefore MT can be used for creating contrast

to help characterize white matter diseases [54]. The effect of the macromolecules is

approximately symmetric about water (the center of symmetry is actually in the aliphatic

region not exactly on water) and can be detected over a large range of frequencies (~100

k
Conversely, CEST uses proton chemical exchange between water and metabolites

(or chemical agents with exchangeable protons) with bound water pools farther from

water [70]. The CEST effect is assymetric and has a much higher frequency specificity

than MT [82]. Additionally, where MT is dependent on chemical exchange and cross

relaxation, CEST is dependent on only chemical exchange [82].

High Field Advantage

Higher magnetic field strengths in MRI have the inherent advantage of an increase in

signal, which is shown in the Boltzmann Distribution (Eq. 1-1). The larger static

magnetic field creates a greater energy difference between the two spin states, and results

in a greater proportion of spins in the lower energy state (N,) than the higher energy state

(Np). This greater proportion of spins results in a larger MRI signal. The predicted

increase in signal with field strength i s proportional to BO7/4 foT Small samples (dominated









by coil noise) [45,83] and proportional to Bo for large samples (dominated by sample

noise) [45,84].

In addition to the increase in signal at high fields, CEST benef its from a greater

chemical shift difference. The Larmor frequency of each nuclei increases proportionally

with field strength (Eq. 1-3), which effectively increases the frequency difference

between bulk water protons and the lanthanide chemically shifted water protons. For

example, given an exchanging system where Ano is 50 ppm, this translates to 10 k
4.7 T and 45 k~z at 21.1 T. This effectively lowers the zM (increasing the possible

exchange rate) needed to maintain the Aommal. Therefore, while few lanthanide agents

would be able to create PARACEST contrast at lower field strengths of 1.5 or 4.7 T,

several lanthanide complexes (including praseodymium, neodymium, samarium,

europium, terbium, dysprosium, holmium, erbium, thulium, and ytterbium ions [50])

should exceed the Amo z2l requirement making contrast generation possible.

High magnetic fields offer an inherent increase in signal, which can be used to

increase the resolution. This, coupled with the increase in contrast potential for

PARACEST contrast, makes PARACEST at high fields an area with great potential for

molecular imaging.



























Figure 1-1. Nuclear magnetic moment bar magnet representation.


r


B


Figure 1-2. Precession in presence of Bo.


























Figure 1-3. Nuclear magnetic moment alignment. A) The nuclear magnetic moments are
randomly distributed in the absence of an external magnetic field. B) The
nuclear magnetic moments are aligned (parallel or antiparallel) when placed
inside the magnetic field.








A B .-

Fiue14 ula agei oetwt rthgoa mantifel d )We h
samplel isi h anei ili itialy al spnsae anolydstibtdn
ther xy mgneizaioncancls u.B h Fplecetsteotooa
mageti fil (Bi ) hc ret hmi oedrcin o e
magetzaio in __ thex- plne



















A coil B 0 -11 time
Figure 1-5. Net x-y magnetization immediately after B1 is turned off. A) Tracking the
x-y magnetization from the top view. B) The resulting Free Induction Decay
(F ID).


C
O vl


bl
5~cl~

rg
Z


T1 recovery


time


Figure 1-6. Spin lattice relaxation (T1). A) The net magnetization along the z-axis
during application of the orthogonal magnetic field (shown in red) and after
relaxation to equilibrium (shown in blue). B) The net magnetization along the
z-axis shown as a function of time, showing T1 as the time it takes for the net
z-axi s magnetization to return to equilibrium.


























.tigI \ T2Decay







Figure 1-7. Spin-spin relaxation (T2). A) The net magnetization (in red) in the x-y plane
during application of the orthogonal magnetic field, with the direction of
precession in blue. B) The net magnetization (shown in red) in the x-y plane
after some time < T2, Showing dephasing (individual magnetization vectors
shown in black). C) The net magnetization in the x-y plane from the
application of the orthogonal magnetic field (start point in red) until relaxation
to equilibrium at zero. D) The net magnetization in the x-y plane shown as a
function of time, showing T2 aS the time it takes for the net magnetization in
the x-y plane to return to zero.












Outer Sphere (~OS)


Inner Sphere (IS)


Figure 1-8. Gadolinium (T1 relaxation) contrast.


HOH


H O H









H, H
O O .o .0
Od~ H




OO


H ,H

O- ----, O; HO

D HOJ


O Ot[

Figure 1-9. Common gadolinium based contrast agents. A) Multihance@ [58]. B)
MAGNEVIST@ [59].


Sphere (OS)


Figure 1-10. Iron Oxide (T2 relaxation) contrast.












A~t Bl C~ Y-~ C




D~' Umtrae RFSrrain Stuae
Fiue11.C heia xhneStrto Trnse (C ET.A Unatrt e bul
wae rtn ecagn ih on ae mo lecl.B neR





prtn.D Unsatua~ FSnaa auratedadstr atdpoos adR auaio aee.E


After some time, the saturation has transferred to multiple bulk water protons.































100 0 -100
Pirsaturation Offset (ppm)

Figure 1-12. Example CEST spectrum. Each line represents the remaining bulk water
signal with presaturation supplied at the offset on the x-axes; shown for a
complex with the bound water pool located at ~50 ppm, illustrated by the dip
in the left side of the spectrum. The larger dip at 0 ppm is a result of the direct
saturation of water.
















III I
so o (ppm) -50


Figure 1-13. PARACEST Imaging. A) Example 1D proton spectra, with location of
bulk (unsaturated) water and bound (saturated). B) Example image where RF
saturation is supplied at the offset of the bound water protons. C) Example
image where RF saturation is supplied at the offset on the opposite side of the
bulk water. D) Example difference image showing the resulting contrast.


Qe)i









CHAPTER 2
METHOD S

In order to determine optimal contrast agents for use at high magnetic fields, various

agents were tested to determine their contrast generation potential. The contrast agents were

made into solutions, and put into phantoms for evaluation at several applied magnetic field

strengths (Bo). The type of testing varied depending on the type of contrast agent, but included

both imaging and spectroscopy. As di scussed in the previous chapter, three types of contrast

agents were investigated: Ti relaxation agents, iron oxides and PARACEST agents. Relaxation

agents and iron oxides both produce contrast that is dependent on their relaxation effects on

water. In these cases, the relaxation parameters were measured done using imaging. For

PARACEST agents the contrast is dependent on a presaturation RF pulse and chemical

exchange, and can be seen using imaging and spectroscopy. In order to test PARACEST images

with presaturation at varied offsets were taken to best illustrate the contrast generation via

imaging. Alternatively, CEST spectra were obtained to show the PARACEST contrast as the

decrease in bulk water signal. The methods for these procedures are included in the following

Chapter.

Contrast Agent Preparation

The chemical agents which were used to produce contrast were obtained from various

sources, as detailed below.

Multimodal Quantum Dots

The Particle Engineering Resource Center at the University of Flordia supplied thi s

experiment with the fluorescent gadolinium contrast agent. Gadolinium functionalized quantum

dots (Gd Qdots, Figure 2-1) were fabricated by as previously described by synthesizing water-

soluble silica-coated ZnS-passivated CdS:Mn (CdS:Mn/ZnS/SiO2 core/shell/shell) fluorescent










Qdots [85]. After aqueous stabilization, the Qdots were functionalized by adding n-

(trimethoxy silylpropyl) ethyldiamine, triaceti c aci d tri sodium salt (T SPETE) and Gd (III)

acetate. Dysprosium quantum dots were fabricated in a similar manner.

PARACEST Agents

Three europium (Figure 2-2), two ytterbium (Figure 2-3 A and B) and two thulium (Figure

2-3 C and D) PARACEST agents were investigated in this study. All PARACEST Agents were

obtained from A. Dean Sherry (Department of Chemistry and Biochemistry, University of Texas,

Dallas, TX) or purchased from Macrocyclics (2110 Research Row, Suite 425, Dallas, TX 75235

www.macrocyclics.com).

Iron Oxides

Ferumoxide (FeO1.44) WAS used as a representative dextran coated superparamagnetic iron

oxide for this study. Specifically, Feridex@ is manufactured by Bayer Healthcare

Pharma ceuti calls .

Phantom Preparation

A phantom for MR imaging refers to an inanimate obj ect which can b e imaged. Phantoms

can be used for many purposes, including testing the performance of an imaging system before

imaging a living organism. Phantoms can also be used to image a specific type of tissue or

chemical compound to optimize the imaging parameters. Phantoms were used to evaluate

several chemical compounds for their efficacy as contrast agents. For this study phantom refers

to a container consisting of a larger tube filled with liquid (usually water) containing several

smaller tubes filled with different liquids (usually a contrast agent). Generally, a phantom was

created by filling capillary tubes with different contrast agents, and closing off the capillary tubes

with clay. The capillary tubes were put into the larger tube, and then the large tube was filled

with double deionized water (ddH20), and the large tube is closed with a plastic cap.









The Gd QDots were obtained already in solution of given concentrations. These solutions

were often not entirely soluble, and therefore precipitated out of solution if left sitting for some

time. The time for precipitation was dependent on the batch, but ranged from ten minutes to

several hours. In order to avoid precipitation during an imaging experiment, the Gd QDots were

suspended in agarose for imaging. A 2% agarose solution was be prepared by mixing 0.50 mg of

analytical grade agarose in 25 mL of ddH20. This solution was microwaved on high, and

swirled often, until the agarose completely dissolved. This agarose solution solidified when

allowed to come to room temperature. Phantoms of Gd QDots were prepared by mixing equal

parts aqueous Gd QDot solution with 2% agarose. The resulting solution (which was half the

concentration of the original solution) was quickly transferred into capillary tubes and allowed to

harden. Phantoms ofPBS buffer, dysprosium quantum dots, and dysprosium DTPA were

prepared in a similar manner. The tubes were capped and labeled.

The ferumoxide was obtained in solution, and was completely soluble. Because

precipitation was not a concern, the ferumoxide was diluted by adding 900 CIL ddH20 with 100

CIL of the original solution, for a 1:10 dilution. This solution was then used for additional

dilutions by taking 100 CIL aliquots of the 1:10 dilution and adding 100, 400 and 900 CIL of

ddH20 for resulting 1:20, 1:50 and 1:100 solutions. These dilutions were then transferred to

capillary tubes, capped and labeled.

The PARACEST agents were obtained as solid lanthanide ion complexes (powders) with

their formula weights listed. These complexes have been shown to extremely kinetically inert,

even at very acidic conditions [86], so were assumed to be stable in solution. The solutions were

created by dissolving the powder in ddH20. The desired concentration of these solutions was

obtained by weighing the appropriate amount of powder according to the formula weight of the










complex, in accordance to Equation 2-1. For example, Eu-2 has a formula weight of 890.94

g/mol. For 1mL of 10 mM solution ofEu-2:

DesiredConcentration FormulaWeight
Volume Water
10-3mo01 g
10 x x I890.94 g x lx 10-3L) =0.0089094g =89.1Img (2-1)
L moll

In order to get 1 mL of 10 mM Eu-2, 89.1 mg of the solid Eu-2 powder was dissolved in 1

mL of ddH20. Solutions of varied concentrations, or different lanthanide ion complexes were

created in a similar manner using the desired concentration and formula weight for each. After

the desired concentration solution was prepared, the pH was measured and noted.

In some cases, the pH was altered using small volumes of 10 100 mM KOH or HCI to

respectively increase or decrease the pH. The dilutions phantom for investigation of

concentration dependance was created by first making a 200 CIL of 100 mM Eu-2 solution.

Serial dilutions were prepared by taking 100 CIL of the original solution, and diluting with 100

CIL of ddH20 (resulting in 200 CIL of 50 mM Eu-2 solution). This process was repeated to obtain

concentrations of 100, 50, 25, 13, 6, 3, 1.5, 0.8 and 0.4 mM. One phantom was made using 1%

agarose to test the viability of contrast generation in an environment with less free movement of

water.

To complete the phantom, the capillary tubes were inserted into a larger NMR tube. The

large tube was then filled with ddH20, for multiple reasons including stabilizing the capillary

tubes, avoiding imaging artifacts which would result from air, and to use as a comparison for

contrast generation.

Magnetic Resonance Imaging and Spectroscopy

The imaging and spectroscopy for this study were done at the National High Magnetic

Field Laboratory (NHMFL), in Tallahassee or at the Advanced Magnetic Resonance Imaging









and Spectroscopy (AMRIS) at the McKnight Brain Institute at the University of Florida. The

4.7, 11, 14.1 and 17.6 T magnets are located in the AMRIS facility, and the 21.1 T magnet is

located in the NHMFL Tallahassee. All systems have Bruker software, including different

versions of Paravision (for imaging) and xwinnmr or topsin for spectroscopy. The procedure for

operation varies slightly between magnets, and software versions. This method section outlines

the basic procedure for the imaging and spectroscopy completed in this study.

Set Up

For all experiments, the set up was the same; the phantom was first centered in the coil (for

the 4.7, 11, 17.6 and 21.1 T magnets) or placed in the spinner with the sample in the desired

region (for the 14.1 T magnet). For the 4.7 and 11 T magnet, the sample was tuned using a

network analyzer and then placed in the magnet. A position scan was run to ensure that the

sample was in the center of the magnet. The phantom in the spinner was dropped (slowly via air

pressure) into the center of the 14.1 T magnet. For the 17.6 and 21.1 T magnets, the probe with

the sample centered in the attached coil was placed into the magnet. For the 14.1, 17.6 and 21.1

T magnets, the sample was tuned once in the magnet.

Once the sample was in the center of the magnet and tuned, the swimming was completed.

This was done either automatically, using the Paravision autoshim tool, or manually using

xwin/topspin. After swimming, the basic frequency was set in Paravision. For imaging, the 90

degree pulse power was calibrated using the onepulse or singlepulse sequence (which uses an RF

pulse to obtain 1D data). This was completed by determining the attenuator power required to

maximize the signal, and then altering the gain to set the digitizer filling between 60 and 80%.

For spectroscopy, the power level was set (usually 0-6 dB) and the duration of the pulse was

varied using a broker sequence which repeats a pulse sequence keeping all but one variable

constant (paropt). The duration of the power pulse was determined as the 90 degree pulse, where









the signal should b e a maximum, but was also calculated as half of the pulse length when the

signal was 0 (the 180 degree point). For imaging, a tripilot scan (which results in three 2D

images in each of three orthagonal axes i.e. x,y and z) was run to ensure correct sample

placement, and to set up the geometry for the remainder of the images. An axial image slice was

selected from the tripilot, in a homogenous region with no artifacts from air bubbles or

contaminants. The axial slice dimensions fit the largest of the phantom tube; for a 5 mm N1VR

tube, the field of view was set to 5 mm by 5 mm.

After the preceding set up procedure, individual imaging and spectroscopy experiments

were performed depending on the type of contrast agent.

Relaxation Parameters

Phantoms were imaged at magnetic field strengths of4.7, 11.1, 17.6 and 21.1 T using

spin-echo and gradient-recall echo sequences that quantify T1, T2 & T2* relaxation by varying

either the repetition time (TR) Or the echo time (TE).

In order to calculate the T1 relaxation time, a variable repitition time (VTR) or progressive

saturation experiment was performed in which the repitition time was varied from 10s to the

lowest repition time allowable with eight intermediate time points (ex TR = 10 s, 6 s, 3 s, 1.5 s,

750 ms, 500 ms, 250 ms, 125 ms, 75 ms, and 50 ms). Each TR TOSulted in one image which was

used to calculate the T1. This calculation was performed using Paravision software, through

Image Sequence Analysis (ISA) in the Image Display Tool. The region of interest tool (ROI)

was used to create circular regions of interest drawn large enough to encompass the area of the

contrast while excluding the NMR tube. Within the ROI tool, "calculate" was chosen, and the

average signal intensity for each TR image was determined by the program. Using the ISA tool,

the ISA fit function was set to 'tlytr', and the program fit the ROI values to Equation 2-2.










M (t)= M M -M (0)9~ (2-2)

The net magnetization along the z axis is given by Mz and is a function of time (t). At thermal

equilibrium the net z magnetization is Mz,eq.

In order to calculate the T2 relaxation time, a multi-slice-multi -echo (msme) T2 experiment

was performed, in which the TR WAS held constant (5 s), and an echo image was taken at each

echo TE. The number of echo images was input as a sequence parameter, and the T2 calculation

was performed as above. ROIs were created and calculated, the ISA fit function was 't2', and the

ROI values were fit to Equation 2-3.


M x(t)= M (0)e (2-3)

The net magnetization in the xy plane is Mxy and is a function of time (t).

T2' relaxation times, were calculated by performing several multiple-sli ce-multi-echo or

fast-low-angle-shot (FLASH) gradient echo images with a constant TR (either 400 ms or 2000 s)

and varied TE (fTOm the minimum value ~ 3 ms to 90 ms). Each echo image was taken from one

individual pulse sequence. The images were similarly processed, using the ROI tool in

paravision, and the values were fit using Equation 2-3. The difference between measuring T2

and T2* WAS that the echo images for T2 are taken from the same image sequence, one after the

other, all within the same TR. The T2' images are taken from different sequences, with each echo

image being taken in its own TR

PARACEST Agents

Phantoms of the lanthanide ion complexes were imaged magnetic field strengths ranging

from 4.7 to 21.1 T using spin-echo, gradient recall echo, and FLASH magnetization transfer

(MT) "prepped" sequences. These MT sequences were created from traditional pulse sequences,

but the user was set to expert, and under 'preparation', 'magnetization transfer' was set to 'yes'.









Several parameters were varied within the 'MT parameters' including the duration of

presaturation, type of saturation pulse, length of pulse, inter pulse delay, number of pulses, and

presaturation offset. The presaturation consisted of a 1 ms pulse that was repeated 2000 to 4000

times. The pulse type was either 3-lobe since or gaussian pulses, and there was a 10 Cls inter pulse

delay for a total irradiation time of two to four seconds. The offset value for each set of images

was varied between 0 and +500 ppm, and the irradiation power was varied from 1 through 200

CLT. To maintain a consistent level of RF power deposition, the CEST image was generated from

the difference between two images, one with a positive offset value and the other with the

negative offset value.

Determining the amount of presaturation power applied in each experiment was not

straight forward. Published values were up to 250 CIT [69], but these values were not obtainable

on the systems tested (to avoid arcing and sample heating). Initial studies were done by fully

saturating water on resonance, and doubling that power for the rest of the imaging. The value of

the presaturation pulse in CIT was appropriately set by determining the actual value for the

MTC_pulsegain (in the pulse program under the previously mentioned 'MT parameters'). This

resulted in presaturation values of 4 to 128 CIT.

CEST Spectra

1D 1H NMR on phantoms that contain only one sample were used to obtain the CEST

spectra. 1D spectra were taken using a presaturation pulse over a range of chemical shifts in

increments ranging from 1 10 ppm dependent on contrast agent. The europium agents have an

optimal offset around 50 ppm, therefore 1 2 ppm increments were used. The thulium and

ytterbium agents have optimal offsets of 250 and 500 ppm respectively, so 10 ppm increments

were used. The water peak from each of the 1D spectra was plotted versus the chemical shift of










the presaturation pulse. For phantoms containing multiple samples, a similar CEST spectra

were obtained by running sequential magnetization transfer pulse sequences, with the offset of

the presaturation pulse varying over a range of chemical shifts by the aforementioned

increments. A graph of the percent remaining bulk water signal (in each of the samples) versus

offset frequency create the CEST spectra. CEST spectra illustrated two minima when the

PARACEST agent demonstrated significant contrast generation; one minima at the resonant

water frequency (a zero offset), and one at the optimal CEST frequency.

Quantification of Contrast

The contrast generated by PARACEST agents was calculated in different ways. When

using an imaging approach, the difference images were u sed to calculate the Contrast to Noi se

Ratio (CNR). This is a ratio of the Signal to Noise Ratio (SNR) of the contrast sample to the

SNR of water. This is calculated with Equations 2-4 and 2-5, with representative images and

regions of interest shown in Figure 2-4.


SNR (Isnipe Yose)(2-4)
Noise

CNR = SNRSansple SNRworev (2-5)

Where l is the average signal intensity within the region of interest, and o is the standard

deviation of the signal intensity within the noise region.

Contrast was also measured as percent increase or decrease the bulk water signal, in either

imaging or spectroscopy. PARACEST contrast is inherently a decrease of signal, thus the

percent decrease is calculated with Equation 2-6, and illustrated in Figure 2-5.


%D~ecrease =SN, xN,) 100 (2-6)
SNR
opposite









Where SNRon is the SNR of the region of interest with the presaturation applied at the offset of

the bound water, and SNRoffis the SNR with the presaturation applied on the opposite side of the

bulk water peak.

CEST Spectra are generally normalized to the bulk water signal (when the presaturation

pulse is applied far away from the bulk and bound water). The y-value on the resulting

normalized CEST Spectra can then be used to calculate the percent decrease, also known as the

percent CEST effect. Because the CEST Spectra was already normalized, the percent decrease

for the Spectra can be calculated from Equation 2-7.

%Decrea~se~pectr M 1 l) x 100 (2-7)

Where Nlhu is the minimum normalized intensity at the bound water shift (identified in Figure

2-6).

Simulations

Rationale and Design

Computer models provide a method for simulating contrast agent behavior under various

conditions. Computer simulations are less expensive and faster than performing each experiment

on an MRI instrument. By developing a predictive model for PARACEST contrast, variables

such as temperature and pH can be tested for without hours of spectrometer time; and different

PARACEST agents can be simulated to determine to contrast behavior.

MATLAB was used to create a program to simulate the Bloch equations modified for

chemical exchange. The CESTFIT MATLAB program (Appendix A), created by collaborators

(M.E. Merritt, D.E. Woessner, S. Zhang, and A.D. Sherry) at the University of Texas

Southwestern Medical Center, Dallas Texas [87], was used for the simulations in this proj ect.

This simulation assumes two pools of water protons: those in bulk water, and those bound to the









PARACEST agent. (It can be altered to include 3 pools, with an additional bound pool of

protons, ie. bound to an amine group). The water protons bound to the PARACEST agent will

be saturated when a presaturation pulse is applied at the chemical shift of the PARACEST agent.

The chemical exchange occurs between these two pools and causes saturation transfer from the

bound water to the bulk water, which results in a decrease in the bulk water signal. The

simulation tracks the remaining bulk water signal as a function of presaturation offset, which

should indicate a minimum at the optimal chemical shift offset for the presaturation pulse.

CESTFIT

MATLAB was chosen as the program in which to run the simulation for several reasons.

The CESTFIT program [87] was originally written in MATLAB, so it has already been shown to

work well within MATLAB. Thi s code was adapted slightly, to fit the desired data. CEST

spectra were obtained in the previously discussed method, and then processed in MATLAB. The

CEST spectra were loaded into MATLAB, and then the area under each peak was calculated.

The resulting single point spectra (versus peak spectra) were then fit to the Modified Bloch

Equations using the CESTFIT program. Input variables were altered until the simulations fit (via

an unconstrained nonlinear minimization (Nelder-Mead) program included in the MATLAB

software) the data to R2 ValUeS of .95 to .99.

Cell Labeling

As a step towards molecular imaging, direct cell labeling with contrast was investigated.

Mouse monocyte/macrophage J774 cells were chosen because of their ability to uptake

exogenous compounds and maintain viability [88]. The cells were defrosted, washed with and

then resuspended in a supplemented medium (SM).

The SM was made by mixing 450 mL Dulbecco's modified Eagle's medium

(DMEM)(GIBCO, Grand Island, NY), 50 mL fetal bovine serum (FB S)(Summit Biotechnology,










Ft. Collins, CO), 5 mL glutamex (GIBCO), and 5 mL peni cillin/streptomycin (GIBCO). After

mixing, the SM was filtered through a millipore sterile cup and refrigerated before use.

The defrosting was completed by thawing a vial of J774 cells, adding 0.5 mL SM to

thawed cells, and transferring all resulting liquid to a 10 mL conical centrifuge tube. The

original vial was rinsed with 1 mL SM, and the contents added to the 10 mL tube. The 10 mL

tube was then filled with SM for a total volume of 10 mL.

Washing was completed by spinning down (1100 rpm for 5 min) the 10 mL tube, and

decanting off the liquid, leaving the cell pellet in the bottom of the tube. The cell pellet was

resuspended in 10 mL of fresh SM, and then all liquid was transferred to a 50 mL tube. Thi s

tube was then filled with SM for a total volume of 30 mL.

Each of three large cell culture plates was then seeded with 10 mL of the washed cell

containing liquid. The plates were then placed in an incubator. The media was replaced (by

aspirating the old media and dead cells, and then replacing with fresh SM) 24h after initial

plating, and the cells were allowed to attach and grow to confluency (usually 2-3 days). After

confluency, the cells were split into new plates.

Replating was completed by manually detaching the cells from the bottom of the plate (by

scraping). The resulting media/cell mixture was transferred to a 10 mL tube, and washed with

SM as above. Cells were counted by first creating a 1:10 dilution (with 100 CIL of cell solution,

500 CIL phosphate buffer solution, and 400 CIL trypan blue stain). Ten CIL of this dilution were

transferred to a counting slide, the number of cells in each of 4 quadrants were counted. This

number was used to calculate the number of cells through Equation 2-8.

NenCezz x 10'x V (2-8)

#ofsquares









where Neen is the total number of cells, Caell is the cell count from above, and VT is the total

volume prior to dilution. The 10' factor takes into account the dilution and the volume fraction.

The total number of cells was then used to determine the final volume, which was adjusted (by

adding fresh SM) to achieve a specific concentration of approximately 2x105 cells/mL. Ten mL

aliquots of the 2x105 cells/mL solution was pipetted onto fresh cell culture plates.

The first PARACEST labeling solution was made by adding enough Eu-2 to SM for a

resulting 40mM solution [69]. For a 1 mL solution, this was done by adding 35.6 mg of Eu-2 to

1 mL SM. The second labeling solution was created by mixing 35.6 mg of Eu-2 with 100 CIL of

SM, and bringing the pH of that solution to ~7 before adding the additional 900 CIL of SM for a

final volume of 1 mL and concentration of 40 mM.

After a small cell plate reached 70-80% confluency, cell labeling was completed by first

aspirating the old media and dead cells and then adding 1 mL of the labeling solution. The cells

were allowed to incubate in the labeling solution over night (12-14 hours), after this time the

labeling media was aspirated off of the cells, and replaced with fresh SM. The cells were

washed and counted as above.

After washing, the cells were spun down one final time, and resuspended in a small

amount (~200 400 CIL) of 1% agarose for phantom preparation and imaging (as above).





sil ica


Figure 2-1. Multimodal Gadolinium Quantum Dots. A) Schematic representation. B) Image of
sample of quantum dots under UV light (360nm), showing yellow fluorescence
(590nm).


TSPETE












































Eu-3.





















Ar


C D









Figure 2-3. Chemical structures of the thulium and ytterbium complexes. A) C36H64YbNsO4 Mol.
Wt.: 952.34g/mol. Referred to as Yb-1. B) C24H48YbNsO4 Mol. Wt.: 792.09g/mol.
Referred to as Yb-2. C) C24H48TmNsO4 Mol. Wt.: 787.98g/mol. Referred to as Tm-
1. D) C36H64TmNsO4 Mol. Wt.: 948.24g/mol. Referred to as Tm-2.






















Figure 2-4. Contrast to Noise Ratio (CNR) calculation region of interest diagram. The
difference image is shown with the water, contrast agent and noise regions indicated.















Figure 2-5. Percent Decrease (%/Decrease) calculation region of interest diagram for Eu-2. A)
The ROI for SNRon, taken from the image at +53ppm. B) The ROI for SNRopposite
taken from the image at -53ppm.










1

0.9


0.Ls 'Decrease = ( 1 .7 xl0
a I -= 30%Jo




0.2

0.1


100 80 60 40 20 0) -20 -40
Presaturation offset (ppm)


Figure 2-6. Example CEST spectra, with the %Decrease calculation shown.


-601 -80









CHAPTER 3
RESULT S

Relaxation Agents

Multimodal Quantum Dots

Gadolinium based contrast agents have been shown to cause favorable changes in

relaxation properties [47] and the overwhelming maj ority of contrast enhanced clinical exams are

performed with gadolinium complexes [56]. They are therefore a logical starting point for

contrast agent investigation at high magnetic fields. Covalently attaching Gd-capturing ligands to

CdS:Mn/ZnS quantum dots, should result in a multimodal contrast agent that produces strong

MRI contrast and optical activity from the paramagnetic Gd-chelate conjugated quantum dots.

In order to evaluate contrast p potential as a function of field strength, the relaxation times were

measured for Gd quantum dots, dysprosium chelate conjugated quantum dots and a dysprosium

standard (Dy-DTPA-BME) at 4.7, 11.1, 17.6 and 21.1 T.

Spin-lattice relaxation times (T1) for all three agents are shown in Figure 3-1. The T1

relaxation times for both the dysprosium quantum dot and standard decrease with field strength,

but increase for the gadolinium quantum dot. Spin-spin relaxation times (T2 and T2 ) for the

agents are shown in Figure 3 -2 and 3-3 respectively. T2 times remain approximately the same

for quantum dots, but decrease slightly for the dysprosium standard. The T2' times remain

approximately the same for the dysprosium quantum dots, but decrease with field strength for the

gadolinium quantum dots and the dysprosium standard.

Iron Oxides

Iron oxides have also been shown to induce sizeable changes in relaxation

properties [62,65,89]. The true T2 relaxation values are independent of field strength above

0.5 T [90,91] and were therefore not investigated. Similarly, the strong effect of iron oxides on









T2 negates the need to evaluate T1 and results in the elimination of signal (negative contrast).

The T2* VaUeS were measured (Figure 3 -4) at 14.1 and 21.1 T. The data collected and used to

calculate the T2* VaUeS are included in Appendix A.

PARACEST Agents

Multiple Lanthanide lon Complexes

Three europium (Figure 2-2), two ytterbium and two thulium (Figure 2-3) PARACEST

agents were investigated in this study. One europium complex (Eu-2) has previously been

shown to generate contrast at lower field strengths [68,69], and therefore was expected to yi eld

similar (if not better) results at higher field strengths. Thulium and Ytterbium were not useful as

PARACEST contrast agents at lower field strengths, but were predicted to reach the slow-to

intermediate exchange requirement (Eqn 1-5) at higher field strengths [50].

Significant contrast was generated for all three europium compounds at different offsets for

field strengths equal or greater than 14.1 T. Figure 3-5 shows this effect at 17.6 T. With RF

presaturation applied at 53ppm, Eu-2 generates enough contrast to vi sibly identify in the

presaturated image (Figure 3-5 A). However, the difference images (Figure 3-5 C and D)

further elucidate this contrast. Eu-3 also demonstrates significant contrast at both 65 and 68

ppm, but this i s best seen in the difference images. Eu-1 generates the least contrast of the

europium complexes, but the difference images show a visible difference between the

surrounding water and the Eu-1 tube at 68ppm. Contrast to noise measurements indicate CNR

values of 8, 54, and 35 were calculated for Eu-1, Eu-2 and Eu-3 respectively at 17.6 T.

Additional images are included in Appendix B.

The ytterbium and thulium compounds were tested at lower magnetic field strengths (14.1

and 17.6 T) without any significant contrast generation, sample images are included in

Appendix B. These four compounds were imaged also at 21.1 T, and the results are shown in










Figures 4-5 thuliumm) and 4-6 (ytterbium). Eu-2 was included in both phantoms as a positive

control, to ensure the presaturation pulse was strong enough to elicit contrast. Both thulium

complexes had the greatest contrast generation with a presaturation applied at 520 ppm. Both

Tm-1 and Tm-2 (Figure 3-6 C) are visibly distinguishable from the surrounding bulk water

(where the noi se is not), but the contrast generation i s not significant. The two ytterbium

complexes had different optimal offsets, with Yb-1 generating the most contrast at 225 ppm, and

Yb-2 at 210 ppm. As with the thulium complexes, the ytterbium complexes are visibly

distinguishable from water (Yb-1 in Figure 3 -7 D and Yb-2 in 3-7 C) but again without

significant contrast generation.

Field Strength

As previously mentioned, PARACEST contrast is predicted to increase with field strength.

The results indicate an increase in contrast generation for all three Eu complexes with increased

magnetic field strength (Figure 3-8). There was significant increased contrast between 4.7 and

17.6 T, and an additional increase at 21.1 T. The difference images at 17.6 and 21.1 T display

the tunable contrast enhancement related to the PARACEST agent and saturation offset (Figure

3-9). The optimal offsets for each agent varied slightly; they were~-65 to~-53 to~-67 ppm for

Eu-1, Eu-2, and Eu-3 respectively. The CEST spectra also illustrate the increase in CEST effect

with field strength for 11.75 T (Figure 3-10-A), 17.6 T (Figure 3-10-B) and 21.1 T (Figure 3-10-

C). This effect is highlighted by comparing the CEST spectra for Eu-2 at all field strengths

(Figure 3-10-D). Additional images for the field dependence results are included in Appendix B.

Concentratio n

Eu-2 was shown to have the greatest contrast generation potential, at all tested field

strengths, so it was chosen for further studies. The concentration was serially diluted from 100

mM to 0.4 mM, and each dilution was included in the concentration phantom (Figure 3-11). The









contrast generated was dependent on the power supplied by the RF saturation pulse. At the high

concentrations, low power (8 CIT) was needed to generate contrast (Figure 3-12). With higher

power (128 CIT), even the low concentrations were able to generate significant contrast (Figure 3-

13). The CNR for each concentration was calculated from difference images with 128 CIT and

indicates an increase in contrast generation with concentration (Figure 3-14). The CEST spectra

(Figure 3-15) also show increased CEST effect (shown as the decrease in the normalized bulk

water signal at the offset of the bound water protons) with increasing concentration, agreeing

with the imaging results. Additional images for the concentration results are included in

Appendix B.

pH

Because PARACEST is a chemical exchange dependent phenomenon, it is sensitive to

both pH and temperature. Ten mM solutions of Eu-2 at different pHs were tested to verify the

viability of contrast generation in different environments. A solution containing labeling media

was also tested to see if there was any effect on the contrast due to the presence of ions in the

media (which would then be likely in the cells) or the po ssible restriction of water movement by

the agarose.

The first pH phantom (Figure 3-16) included a solution at pH 3, a solution at pH 7, and a

solution of 50 % ddH20 and 50 % supplemented cell labeling media (SM; Chapter 2.5) at pH 7.

This phantom was imaged for verification of contrast generation for different two pHs as well as

an ionically rich environment. At a presaturation offset of +56 ppm at 17.6 T the image shows a

significant decrease in signal for all three tubes compared to the surrounding water (Figure 3-17

A). The difference image further demonstrates the contrast generation, but also shows that the

contrast is greater in the pH 7 solutions than the solution with a pH of 3 (Figure 3-17 C). The









difference images for +/- 53, 56 and 59 ppm were corrected (in MATLAB) to represent CNR

maps (Figure 3-18). These corrected difference images demonstrate that all three solutions were

able to generate contrast at all three offsets. These images also show that there is a shift of the

optimal offset with pH. The Eu-2 at a pH of 3 has the greatest contrast generation at the offset of

53ppm, while both solutions at pH of 7 generate more contrast at 56ppm.

The second pH phantoms tests a larger range of pHs (from 3 13), and the difference

images taken at 14.1T indicate contrast generation potential for pH 3, 7, and 10, but not 13

(Figure 3-19). These images similarly demonstrate a shift in the optimal offset, but also show

that the pH of 10 has a lower overall potential than 3 or 7 (which both have comparable

maximum CNR values). This was further investigated by generating a CEST spectrum. The

close up of the 71-5 1 ppm region of the CEST spectrum shows that the minima of both pH 3 and

pH 7 are approximately equal and result in a 45% decrease in signal (Figure 3-20). The sample

with pH 10 does not have as distinct a dip as the pH 3 or 7 sample, but has a 5% decrease in

signal compared to water. The pH of 13 sample is essentially indistinguishable from water at all

points.

This pH sample was also tested at 17.6 T, and the results agree with the 14.1 T data. A

comparison of the two field strengths show contrast generation for pH 3, 7 and 10, and a similar

shift in maximum contrast potential and offset with pH (Figure 3-21). Both field strengths show

no contrast generation for the pH 13 sample. Additional images for the pH results are included

in Appendix B.

Temperature

CEST spectra were taken for a 10 mM sample of Eu-2 at 14, 20, 26, 32 and 38 OC and the

results are shown in Figure 3-22. The close up of the region from 35 to 75 ppm clearly show the









shift in the spectra with temperature (Figure 3-22 B). The warmer temperatures cause the

exchange to speed up, which causes the dip to broaden and shift towards bulk water.

Conversely, the colder temperatures cause the dip to sharpen and shift away from the bulk water.

Individual spectra for the temperature results are included in Appendix B.

Effect of Signal to Noise

Increasing the number of acquisitions should increase the signal to noise ratio as the square

root of number of scans [44]. To test the effect of this increase on the CEST effect, the CEST

spectra was taken for a 10 mM sample of Eu-2 with 1, 2, 4, 8, 32 and 64 scans and are shown in

Figure 3 -23. The close up of the region from 50 to 59 ppm shows an increase in CEST effect

with increase in scans. Imaging experiments yielded similar results, showing an increased CNR

with increasing number of acquisitions (Figure 3 -24). Individual spectra for the multiple

acquisition results are included in Appendix B.

Presaturation Power and Duration

Both the power and the duration of the presaturation pulse affect the amount of contrast

generation. The power was tested by imaging the same (serial dilutions) phantom at 14.1 T and

varying the power from 8 to 128 CIT. The difference image from the +/- 53 ppm images show

the change in contrast (Figure 3 -25) and the CNR calculations indicate a nonlinear relationship

between the power of the RF saturation pulse and the resulting contrast (Figure 3 -26).

The duration of the presaturation for imaging was 2 s, because previous studies had

published good contrast generation with that presaturation time [68,69]. The saturation time

consisted of a loop of 1 ms pulses (2000 for the 2 s presaturation). The number of 1 ms pulses

was tested at 14.1 T by monitoring the bulk water signal as a function of number of presaturation

pulses applied at 53 ppm at 26 oC. The results show a decrease in bulk water signal with the










increase in number of presaturation pulses (Figure 3 -27). The exponential increase in saturation

can be used to determine the apparent exchange [92]. Additional temperatures were measured

and show similar results. These and additional results are included in Appendix B.

Cell Labeling

The first cell labeling solution was not pH balanced prior to cell labeling which resulted in

total cell death. The second solution was balanced to a pH of 7, and was not toxic to the cells.

The labeled cells were imaged at 17.6 T, and the results in the offsets of interest are shown in

Figure 3 -28. No viable contrast was generated. Additional images are included in Appendix B.

Simul atio n

The CESTFIT [87] program uses the modified Bloch equations (Appendix C) to simulate

the CEST effect. This program was adapted to simulate the effect of several variables, including

field strength, T1 relaxation times, chemical shift difference and concentration. It was also used

to fit variable temperature data. The MATLAB code which was used to create the following

figures is included in Appendix C, and is summarized in Appendix C. The code for each specific

simulation is included in the remaining sections of Appendix C.

Relaxation Times

The effect of both the T1 and T2 relaxation times were simulated (Figures 3-29 and 3-30

respectively). The MATLAB code is included in Appendix C. Increasing T1 causes an increase

in the CEST effect, but also increases the apparent direct saturation of water. Increasing T2 has

no apparent effect on the CEST effect, but shows a broadening in the saturation around bulk

water.

Chemical Shift Difference

The effect of altering the chemical shift difference between bound and bulk water was

simulated (Figure 3 -31). The MATLAB code is included in Appendix C. Increasing Aco









resulted in an increase in the distance between the two inverse peaks, but no apparent effect on

the amount of CEST.

Chemical Exchange Rates

To simulate the behavior of different PARACEST compounds, the chemical exchange

rates were varied (by varying Cb the transition rate of the spins leaving bound water, also the

inverse of the lifetime of a proton in the bound pool zM.) The results of the simulation (Figure

3-32) indicate an optimum chemical exchange rate within a given set of variables (concentration,

field strength, presat power, T1, T2 and chemical shift difference). The MATLAB code is

included in Appendix C.

Presaturation Power

The effect of increasing presaturation power was simulated (Figure 3 -33). The MATLAB

code is included in Appendix C. Increasing power resulted in an increase in CEST effect, but

also resulted in a broadening of the saturation around bulk water.

Concentratio n

The concentration varied by increasing the ratio of bulk to bound water protons (Mobulk and

Mgbound). Mgbulk WaS set to one and MObound WaS incrementally increased from 0.0001 to 0.001.

The results of the simulation are shown in Figure 3-34 and the MATLAB code is included in

Appendix C. Increasing concentration resulted in an increased CEST effect.

Temperature Fit

The modified CESTFIT simulation shows the agreement between the variable temperature

data and the fit, for 14, 20, 26, 32 and 3 8 OC, with R2 ValUeS of 0.994, 0.993, 0.991, 0.997 and

0.998 respectively (Figure 3-35). The T1 relaxation times (Table 3-1) were measured via

inversion recovery, and these values were used in the fit. MATLAB code and the individual

temperature/fit plots are included in Appendix C.









Number of Acquisitions Fit

The modified CESTFIT simulation shows the agreement between the variable data and the

fit for spectra taken with 1, 2, 4, 8, 32 and 64 scans, with R2 ValUeS of 0.994, 0.989, 0.990, 0.991,

0.985 and 0.989 respectively (Figure 3-36). All spectra were obtained at 26 oC. There is an

increase in the CEST effect with increased number of acquisitions. MATLAB code and the

individual spectra/fit plots are included in Appendix C.

The discussion and conclusions of these results are presented in the next chapter.















* Dy Quantum Dot
r Dy-DTPA-BMA
* Gd Quantum Dot


2000-












0-


Field Strength (Tesla)


Figure 3-1. Quantum Dot T1 relaxation times. The T1 relaxation of gadolinium quantum dots
(red), dysprosium quantum dots (blue), and Dy-DTPA-BMA (green) shown at
magnetic field strengths of 4.7, 1 1.1, 17.6 and 21.1 Tesla.

Dy Quantumn Dort
230 1 u n um D r
I D~y-DTPAb-BM A4


Field Strength (Tesla)


Figure 3 -2. Quantum Dot T2 relaxation times. The T2 relaxation of gadolinium quantum dots
(red), dysprosium quantum dots (blue), and Dy-DTPA-BMA (green) shown at
magnetic field strengths of 4.7, 1 1.1, 17.6 and 21.1 Tesla.












rr Dy Quantum Dot
A Dy-DTPA-BMA.
* Gd Quantum Dot


Field Strength (Tesla)


Figure 3 -3. Quantum Dot T2' relaxation Times. The T2'relaxation times of gadolinium quantum
dots (red), dysprosium quantum dots (blue), and Dy DTPA-BMA (green) shown at
magnetic field strengths of 4.7, 1 1.1, 17.6 and 21.1 Tesla.








250 -


200 -


* 20 rnM
S50 mM
* 100 mMl


150 -


100 -


50 -


14 15 16 17 18 19
Magnetic Field Strength (T)


20 21 22


Figure 3 -4. Iron oxide T2' relaxation times. The T2' relaxation times for ferumoxide at magnetic
field strengths of 14.1 and 21.1T.









A B C D3



53ppm






65ppm :





68ppm ~u 1~ ul
Eu



Figure 3-5. Europium PARACEST images at 17.6T. A) Images taken with presaturation offset
at 53, 65 and 68 ppm (as indicated). B) Images taken with presaturation at -53, -65
and -68 ppm. C) Difference images taken to show positive contrast as B A. D)
Difference images taken to show negative contrast as A B.


Figure 3 -6. Thulium based PARACEST agents at 21.1T. A) Image without presaturation shows
where each contrast agent is located, and also the lack of inherent contrast in Eu-2
and Tm-1. Both thulium agents show a slight chemical shift artifact, and Tm-2 shows
a slight T1 weighting (as evidenced by the increased signal). B) The difference image
between +/-5 3ppm, shows significant contrast generation by Eu-2. C) The difference
image between +/- 520ppm, shows the most contrast that was generated by both Tm-
1 and Tm-2.






















Figure 3 -7. Ytterbium based PARACEST agents at 21.1T. A) Image without presaturation
shows where each contrast agent is located, and also the lack of inherent contrast in
Eu-2 and Yb-2. Both ytterbium agents show a slight chemical shift artifact, and Yb-1
shows a slight T1 weighting (as evidenced by the increased signal). B) The difference
image between +/-53ppm, shows significant contrast generation by Eu-2. C) The
difference image between +/- 210ppm shows the most contrast that was generated by
Yb-2. D) The difference image between +/- 225ppm shows the most contrast that
was generated by Yb-1.



60-


50-



M *Eu2
W- *Eu3







~ 0 5 01 02
MantcFedSrnt T






field~i strength.~t (T



























Figure 3 -9. Difference images of europium complexes at 17.6 and 21.1 T. Difference images at
17.6 T with presaturation offsets of A) 67, B) 65 and C) 54 ppm, and at 21.1 T with
presaturation offsets of D) 67, E) 65 and F) 54 ppm. The images illustrate the
increase in contrast with field strength, as well as the shift of optimum offset with
agent.













1'1



vl
d
Q) 0.5




'3 0.0
L4
Q)
fiS




CP
-d
e,
N
.3



o


Eu-1
y Eu-2


SEu-1
+ Eu-2
Eu-3


I
-50 -100


-50 -100


Figure 3-10. CEST spectra with variable magnetic field strength. CEST spectra shown for three
europium complexes taken at A) 11.75 T, B) 17.6 T and C) 21.1 T. CEST spectra
shown for Eu-2 at all three field strengths.


Figure 3-11. Eu-2 serial dilutions. Concentrations from 100 mM to 0.4 mM locations indicated.


Offset (ppm)








R


51ppmL~LYsl~




53ppmn





55ppm



Figure 3 -12. Eu-2 concentration phantom images at 14.1 T, with a presaturation power of 8 CIT.
A) Images taken with presaturation offset at 51, 53 and 55 ppm (as indicated). B)
Images taken with presaturation offset at -51, -53 and -55 ppm. C) Difference
images taken as A B. D) Difference images taken as B A.









B


Figure 3-3 u- certain hnomiae a 41 ,wt a peartionpwro









Figre3-1indi2ccaed).tio B)ao Images taken1 T with presaturation ofsta 5-3ad-5ppm.r C)

Difference images taken as A B. D) Difference images taken as B A.


















I..'C-~C--r
-
C'I
r'
1
*
r'
.*
C




r

r'
r
r


r
r
i
r


j

i
i


10 20 30 40 50 60

Concentration (mM)


70 80 90


Figure 3 -14. Concentration versus contrast for serial dilutions of Eu-2 at 14.1

presaturation power of 64 CIT and a total presaturation time of 4 s.
calculated from the difference image at +/- 53ppm.


T, with
CNR was













0.9



0 0.7

0 6





S0.2

.S0.1





0


100mM
- s50mh1

12..5mM

- L6.mM



01.4mM4

noise


100 80 60 40 20 0 -20 -40 -60 -80
Presaturation offset (pprn)



Figure 3 -15. CEST spectra at 14.1 T for Eu-2 serial dilutions. The spectra further illustrate the
increase in contrast generation with concentration.


Figure 3 -16. Eu-2 (10 mM) at two different pH' s, and in SM. A) Eu-2 at a pH of 3. B) Eu-2 at
a pH of 7. C) Eu-2 in a 50% water 50% SM solution at a pH of 7.





















Figure 3-17. Eu-2 pH- phantom at 17.6 T. A) Image taken with presaturation at 56 ppm.
B) Image taken with presaturation at -56 ppm. C) Difference image of B A.


Figure 3 -18. Difference images of Eu-2 pH phantom at 14.1 T color enhanced to represent the
corresponding CNR. A) CNR map at 53 ppm. B) CNR map at 56 ppm. C) CNR
map at 59 ppm.










































-- ~--


Figure 3-19. Eu-2 at pH values from 3 13 at 14.1T. A) Image taken with no presaturation,
with pH of 3, 7, 10 and 13 labeled. All solutions were 8 mM Eu-2, and presaturation
power was 64 CIT. Difference images demonstrating change in contrast with
presaturation offset for the different pHs at presaturation offsets of B) 51 C) 53 D) 55
E) 57 and F) 59 ppm.


-~-- -~-- ~rl~----- ~~


IL


pH 10
-* pH 13
pH 3
SpH 7
"Cwater
-* noise:


~...............1 \F ...............~ 1. ........ p -~...............T


71 69 67 65 63 61 59 57
Presatura~tiosn Offset (ppm)


55 53 51


Figure 3 -20. Presaturation offset versus normalized signal intensity for Eu-2 at multiple pHs at
14.1 T.



















0 ppm 53 ppm 55 ppm 57 ppm 59, ppm
















Figure 3 -21 Difference images of Eu-2 at multiple pHs at 14.1 and 17.6 T. A) Image without
presaturation indicating the location of the samples with pH of 3, 7, 10 and 13 at 14.1
T. B) Image without presaturation indicating the location of the samples with pH of
3, 7, 10 and 13 at 17.6 T. C) Image taken with 32 CIT presaturation at Oppm followed
by difference images taken 53, 55, 57 and 59 ppm at 14.1 T. D) Image taken with 32
CIT presaturation at Oppm followed by difference images taken 53, 55, 57 and 59 ppm
at 17.6 T. The blue circles and connecting lines highlight the contrast of pH 3.














Vr0.8 -!r





P 0.6



0.4





100 50 0 -50 -100
Presat~urat-ion Offset (ppm)
A








"0.8-


0.7 -- 10

-200C
o~sC-J If~ 260C
~v I 320C

0.5
70 65 60 55 50 45 40 35
Presaturation Offset (ppm)



Figure 3 -22. CEST spectra for Eu-2 at multiple temperatures. A) Full CEST spectra taken from
100 to -100 ppm, for Eu-2 at 14, 20, 26, 32 and 38 oC. B) A close up of the region
from 75 to 35 ppm for Eu-2 showing the shift in maximum contrast potential with
offset and temperature.
















S0.8-



S0.6-



~30.4 --



2 0.2 --




100 80 60 40 20 0 -20 -40 -60 -80 -100

Presaturation Offset (ppm)

~0.66


S0.64


60 0.62 -


S0.6 -

~~-1
~30.56 -



S0.54-

0.52
50 51 52 53 54 55 56 57 58 59
Presaturation Offset (ppm)


Figure 3 -23. CEST spectra for Eu-2 with increasing number of scans. A) Full CEST spectra
taken from 100 to -100 ppm, for Eu-2 with 1, 2, 4, 8, 32 and 64 scans. B) A close up
of the region from 75 to 35 ppm for Eu-2 showing the change in maximum contrast
potential with increasing number of scans.











25 -



20 -



15 -



10



5


c
~r
c


_r
C
c


7


Number ofAquisitions


Figure 3 -24. Number of acquisitions versus CNR.


Figure 3 -25. Varying presaturation power difference images. The difference images of the serial
dilution phantom at 14.1 T, with presaturation offsets of + /- 53 ppm, and a
presaturation power of A) 8 CIT, B) 16 CIT, C) 64 CIT and D) 128 CIT.




Table 3-1. T1 Relaxation values for 10 mM Eu-2 at multiple temperatures, measured at 14. 1 T.
Temperature (OC) 14 20 26 32 38
Ti Relaxation Time (s) 3.1 3.5 4.0 4.4 5.1





















2000
+~ 4(000


10 20 30 40 50 60 70
Presaturaition Powe~tr (plT)


Figure 3 -26. Presaturation power versus CNR for serial dilutions of Eu-2 at 14. 1 T. The CNR
values were calculated from the 100 mM concentration, at 14.1 T, and the number of
presaturation pulses was either 2000 (blue) or 4000 (red).


O 500 1000 1500 2000 2500 3000 3500
Number of 1 ms Presaturation Pulses


4000


Figure 3 -27. Bulk water signal as a function of number of presaturation pulses. The bulk water
signal (in blue) was measured with the given number of presaturation pulses at the
offset of 53ppm, at 26 oC. The fit to an exponential decay is shown in red.






































Figure 3 -28. Cell labeling image results at 17.6 T. A) Image taken with presaturation offset of
55 ppm, with the tube contents indicated: 1) PARACEST labeled cells 2) labeling
media 3) unlabeled cells in agarose and 4) unlabeled media. B) Image taken with
presaturation offset of -55 ppm. C) The difference image taken as B A. D) The
difference image taken as A B. E) Image taken with presaturation offset of 57 ppm.
F) Image taken with presaturation offset of -57 ppm. G) The difference image taken
as F E. H) The difference image taken as E F. I) Difference image taken as A E
to show no contrast difference in the surrounding water between 55 and 57 ppm. J)
Difference image taken as B F to show no contrast difference in the surrounding
water between 55 and 57 ppm. K) and L) are tripilot images which validate the tube
assignment (1-4) in A.















S0.8




V10.4 --




S0.2

O

0
30 20 10 0 -10 -20 -30
Presaturation Offset (kHz)


Figure 3 -29. Simulated T1 relaxation variation. The T1 values are indicated, and show an
increase in CEST effect and a broadening of the saturated bulk water with increased


S0.8




V10.4




S0.2


Presaturation Offset (kHz)


Figure 3 -3 0. Simulated T2 relaxation variation. The T2 ValUeS are indicated, and show no effect
on amount of CEST but show a broadening of the saturated bulk water with decreased
T2.















\I \I -10000
0 .8t -I III -iI I II 115000
U IIII U II I II 1---20000
.~O .............25000
0.6 -4
--30000
I II 135000
S0.4- ---1400000
--45000
.0 --50000
18 0.2
--55000


60 40 20 0 -20 -40 -60
A Fresaturadion O set (l~z)






L1

r30.8 10





10. 2 4-
B Prijaturtion ff---3000z

Figre -3. Smuate Bo vritio. co ales ndcatd in z) A)Vaueso3500 ro
0.4 0 _o -4000sowasitaayfo uk ae ihinrae o )Vle
of~o ro 100 o 500~ ho a imla shftawy fomwaerb---4500 lstat
thecoaesenc ofth tw peksat ow -5000es













1-



a,0.8 -





V10.4 -



4 0.2


~0.


-200
-400
-1000
3 000
-8000
10000


I I I I


-40 -60 -80


100 80 60 40 20 0 -20
Presaturation Offset (ppm)


-100


Figure 3 -32. Simulated variation in chemical exchange. Transition rate of spins out of the
bound pool of protons (Cb) indicated. The simulation shows the increase, and then
decrease in CEST effect indicating a maximum chemical exchange value.














S0.8

V1- 00







S0.




waer


S0.8




V10.4



OL


80 60 40 20 0 -20 -40 -60 -80 -100
Presaturation Offset (ppm)


Figure 3-34. Simulated concentration variation. Concentration varied by increasing the ratio of
bulk to bound water protons (Mgbulk/bound). The legend indicates the value of Mgbound
given MObulk = 1. IHCTreSing concentration shown to increase CEST effect.















I~~~ _GCPIo 14data
S0.8t -1C ap~~14 fit
I F~ 120 data
20 ft
0.6
I ~ I~ 26 data

0.4 32 data
32 fit
S0.2t O 38 data



100 80 60 40 20 0 -20 -40 -60 -80 -100
Presaturation Offset (ppm)

Figure 3 -3 5. Variable temperature data and fit. The fit and the data agree with R2 ValUeS Of
0.994, 0.993, 0.991, 0.997 and 0.998 respectively for 14 OC, 20 oC, 26 OC, 32 OC and
38 oC.













I 1

10.8 2


S0.6
1 4 fit

S0.4~ 8 fit


0~ .2t 32 fit

-64 fit

100 80 60 40 20 0 -20 -40 -60 -80 -100
Presaturation Offset (ppm)


Figure 3 -36. Variable number of acquisitions data and fit. The fit and the data agree with R2
values of 0.994, 0.989, 0.990, 0.991, 0.985 and 0.989 respectively for 1, 2, 4, 8, 32
and 64 scans respectively.









CHAPTER 4
DISCUSSION

Relaxation Agents

Multimodal Quantum Dots

Gadolinium agents are traditionally used as T1 relaxation agents; by shortening the T1

relaxation time of the surrounding water the agents cause positive contrast. At clinical magnetic

field strengths (1.5 or 3 T) gadolinium is an effective relaxation agent [56]. However at higher

field strengths the T1 times were shown to increase. This results in a decrease in efficacy of

gadolinium quantum dots as T1 contrast agents with increased field strength. The T1 relaxation

times from dysprosium quantum dots decreased with field strength, indicating an increase in

contrast generation potential. However, the observed decrease still resulted in T1 values at high

fields (11 21 T) that were comparable to the gadolinium values at low fields (4.7 T). The

dysprosium standard decreased the T1 relaxation times with increased field strength.

The T1 results indicate a slight advantage for the dysprosium standard as a T1 relaxation

agent at high fields. The contrast generated at high fields with this standard was better than the

contrast generated by the gadolinium quantum dots at low fields. However, both quantum dots

did not gain significant contrast generation at higher fields. The gadolinium quantum dot

actually decreased in contrast generation, and while the dysprosium increased in contrast with

increased field strength, it never generated more contrast then the gadolinium quantum dot at 4.7



The results of the spin-spin relaxation measurements generally showed a small to

nonexistent decrease in the T2 and T2* times for all the agents. The dysprosium standard had the

largest decrease in both relaxation times, while both quantum dots had only a slight decrease. A

decrease in spin-spin relaxation times would indicate an increase in contrast generation potential.









However, iron oxides have already been shown to have the largest effect on relaxation properties

per unit metal [93] and would therefore be the more likely choice for T2 COntrast generation.

Overall, the contrast generation of T1 relaxation agents did not benefit from going to higher

magnetic field strengths. The T1 relaxation properties di d not improve for the gadolinium

quantum dots, and the dysprosium improvement wasn't significant when compared to

gadolinium at low fields. While the gadolinium or dysprosium quantum dots could be used as T2

relaxation agents at high field strengths, iron oxides would likely be more efficient hence the

better choice. At low field strengths, traditional T1 relaxation contrast agents, including those

based on gadolinium, have proven their utility. However to utilize the increase in signal

resulting from higher magnetic field strengths, a different type of contrast may prove more

useful .

Iron Oxides

Iron oxides have been used for non-targeted contrast generation in clinical studies [65] and

also in cell labeling. At lower field strengths there is a minimum number of labeled cells

required for detection [94] but at high fields the detection limits are lowered [93] and single cell

detection is possible [95,96]. They also benefit from the most change in signal per unit metal

[93]. Their contrast generation potential was shown to have little dependence on field strength.

Iron oxides are a good choice for contrast agent at high field strengths, but have several ultimate

problems for cellular or molecular imaging.

One maj or problem is that the iron oxides create a negative contrast which appears as a

hypointensity on the image. However it may be difficult to distinguish the hypointensity from

the iron oxides from other sources of negative image contrast artifacts, e.g., hemorrhage, air,

metallic devices [97,98]. One study found that labeled human umbilical vein endothelial cells

that were injected post mortem could not be distinguished from other areas of









hemorrhage/microvascular obstruction [66]. This inability to determine the source of the

hypointensity can lead to a high incidence of false positives when using iron oxides.

The iron oxide label remains after cell death, which may allow uptake of the label by

macrophages. It would become difficult to determine if the hypointensity was due to the living

labeled cell or a macrophage. And lastly, even if the label remained in a living cell, cellular

division would lead to label dilution or distribution (in the case that the label remained with only

one cell).

Despite its negative contrast and other possible problems, due to the magnitude of change

in contrast per unit metal, iron oxides remain a viable option for cellular and molecular imaging,

including at high field strengths.

PARACEST Agents

Multiple Lanthanide lon Complexes

The results showed contrast generation for all three europium complexes, with Eu-2 > Eu-

1 > Eu-3. Little contrast was shown for the ytterbium and thulium complexes, even at 21.1 T.

There are several possible explanations for this observation. First, the predicted values of AcozM

at 11.75 T for thulium and ytterbium are relatively low (4.7 and 1.9 respectively) when compared

to europium (60.0) [72]. At 21.1 T these values should still be greater, but they are

comparatively small, which could explain the low contrast generation of these four complexes

even at high fields. Additional factors which may explain the low contrast generation of the

ytterbium and thulium complexes is concentration, pH and temperature. Ten mM europium

complex solutions exhibited significant contrast generation, but thi s may not have been

significant concentration for the other lanthanide complexes. Increasing the concentration of

these agents should increase the contrast generation.









Overall, europium complexes were shown to successfully generate contrast, which

increased with field strength. The other lanthanide agents were largely unsuccessful, but this

may be attributed to poor optimization. The presaturation, temperatures and concentrations were

optimized for europium complexes, and offset was the only factor which was adjusted for the

additional complexes. However, both ytterbium and thulium complexes were not predicted to

generate as much contrast as europium, for the given parameters. Ytterbium and thulium have

both been used by various groups [73,75,99-105] as PARACEST agents for multiple purposes

including contrast generation [99-103], pH [73,75] or temperature [104] monitoring, or detection

of small molecules [105].

While europium agents yielded the best results in this investigation, other lanthanide

agents remain a promising alternative.

Field Strength Dependence

The three europium complexes produced increased contrast with increasing magnetic field

strength, in good agreement with theory (AcozM 1) [72]. The imaging experiments indicated

increased CNR with field strength. There was a significant increase in contrast between 4.7 and

17.6 T, and an additional increase at 21.1 T. The greatest amount of contrast generation was

produced by Eu-2 which had a 38% increase in CNR between the field strengths of 17.6 and 21.1

T (from 3 8 to 54) while Eu-3 showed a 48% increase between 17.6 and 21.1 T (from 25 to 37).

Eu-1 is unique in that CEST contrast is only evident at the field strengths of 17.6 and 21.1 T

(CNR = 8 and 9 respectively) but not at the lower fields that would include clinical field

strengths of 1.5 and 3 T. The CEST images at the maximum contrast for each europium

complex at 21.1 T are shown in Figure 3, which di splays the tunable contrast enhancement









related to the PARACEST agent and saturation offset. The optimal offsets for each agent were

53, 65 and 68 ppm for Eu-2, Eu-1, and Eu-3, respectively.

CEST spectra of all three compounds collected at 1 1.75, 17.6 and 21.1 T show optimal

offsets for each complex: Eu-1 and Eu-3 both have optimal offsets near 65 ppm whereas the

optimal presaturation offset for Eu-2 is near 53 ppm. The spectra also demonstrate a distinct

difference in contrast generation potential for each agent. At all field strengths, Eu-2 generated

the largest CEST contrast, followed by Eu-3 and Eu-1. For each agent, the spectra at the

different field strengths show the increase in contrast from 11.75 to 17.6 to 21.1 T. The CEST

spectra ofEu-2 at the three field strengths shown on one plot demonstrate the normalized bulk

water signal at the offset of bound water decreases from 0.80 (1 1.75 T) to 0.78 (17.6 T) to 0.75

(21.1 T) (indicating an increase in contrast generation).

Both the decreased water signal in the CEST spectra and increased CNR at higher fields

are consistent with expectations that the bound water shifts (Amo) will be larger while the bound

lifetimes (zM) are unaffected, thus expanding the intermediate exchange requirement, zum~c > 1.

Most of the existing Eu(III)-based agents have a bound water peak near 50 ppm at room

temperature, corresponding to Amo = 10,000 Hz at 4.7 T and a requirement for zM > 100 Cps. This

requirement is rather stringent for lanthanide complexes and consequently only a few Eu(III)

complexes meet this condition. However, this same imaging experiment performed at 21.1 T

(Amo = 45,000 Hz) would require a Eu(III)-complex having a zM > 22 Cps, a more accessible value

to achieve experimentally. This condition may be the case with Eu-1, which was unable to

produce contrast at low field strengths, but generated useful contrast at the higher field strengths.

The results of this study indicate that going to higher fields increases the contrast

generation potential for all tested lanthanide complexes.










Concentration Dependence

The serial dilutions of Eu-2 showed an increase in contrast generation with increased

concentration. The lowest tested concentration was 0.4 mM, which showed a 5% signal decrease

in the CEST spectra and a CNR of 2.2. The increase in contrast generation was nonlinear, and

appeared to level out at the highest concentrations. This indicates a maximum contrast

generation, which is physically logical because the contrast is a saturation of the water signal, the

physical maximum is complete water saturation. With higher power saturation, the lowest

concentration was visible. However, the amount of power used will be dependent on several

variables, not the least of which is power deposited in the sample.

Concentration was expected to increase the amount of contrast generation because there

are more lanthanide ions and therefore more sites for water to bind to, hence more shifted water

protons. The results agreed with this expectation and showed that increasing concentration

increases the amount of CEST contrast generated. However, concentrations above 25 mM result

in over a 50% decrease in signal, which is sufficient for a contrast to noi se ratio of over 50 (for a

presaturation power of 128 CIT). This fact could be used to either limit the amount of contrast

agent needed, or increase the concentration and lower the power.

pH Dependence

The results of the pH imaging illustrate a shift in the location of the optimal presaturation

offset as a function of pH. The results demonstrated the ability of the PARACEST agents to

maintain contrast in the presence of other ions (in the labeling media). The pH imaging also

indicates a difference in magnitude of contrast at a given offset for different pH values, which

has been shown in previous studies [73]. Contrast generation was achieved at pH values of 3, 7,

and 10, but not at 13. The optimal range of contrast generation was between pH 3 and 7, both of

which resulted in greater than 40% decrease in signal. There was a shift in the optimal offset









between the pH of 3 and 7, with a shift away from water with increasing pH. Although contrast

(~5% decrease) was generated at a pH of 10 it was not optimal, and did not increase beyond the

5% threshold. The decrease in contrast generation and the lack of contrast at the pH of 13

suggests a pH dependent chemical change that inhibits the CEST in extremely basic conditions.

It is noteworthy that during the making of the pH sample, the solution was first at pH of 3,

then taken to 13, then to 10 and then 7. This is important because it shows the stability of the

compound, and its ability to maintain contrast generation even after exposure to basic conditions.

It also shows that the basic pH did not destroy the complex (as evidenced by its ability to

generate maximum contrast at pH 7), and adds further weight to the argument that the basic pH

causes a change in exchange or confirmation rather than complete annihilation of the complex in

extremely basic conditions.

The overlap of the contrast generation offsets between pH of 3 and 7 indicate that this

europium complex may not be optimal for measurement of pH by itself. This is evidenced by

the contrast generation at 55 ppm, where both pH 3 and 7 produced ~40% signal decrease, so it

would not be possible to differentiate between the two. However, these results indicate the

utility of this complex for contrast generation across a range of pH values, most notably at pH 7,

which is the normal pH of most cellular and living systems. Given that it also works at pH 3,

this makes it a good possibility for cellular labeling, because often the end location of the label is

unknown. If it is endocytosed, it may be put into a compartment with an acidic pH. This

complex would still work as a contrast agent in that case, because it has been shown not only to

generate contrast in acidic environments, but also to maintain stability.

Temperature Dependence

CEST spectra from temperature studies indicate contrast generation potential at all tested

temperatures ranging from 14 OC to 38 OC (287 to 312 K). The CEST spectra also show an









increase in contrast generation at the lower temperatures. They illustrate a broader width of

useful presaturation offsets at the higher temperatures, and a shift of the optimal offset with

temperature. These results agree with previously documented effects of temperature on

PARACEST agents [106].

The results are also consistent with expectations of chemical exchange as a function of

temperature. Chemical exchange is expected to increase with temperature, and this would

theoretically result in a broadening of the peaks, and a trend towards coalescence. Both of these

effects were seen with increased temperature. The last observed effect of temperature was an

increase in T1 relaxation times.

Effect of Signal to Noise

There was increased contrast generation with increased number of acquisitions. The signal

to noise ratio is known to increase with the square root of the number of acquisitions [44], so the

increase in SNR is expected. CNR would be expected to increase by the same amount (with the

square root of the number of acquisitions) as SNR because it is the difference of SNR. The

increase in CEST effect is likely due to the increase in SNR with increased number of

acquisitions.

The imaging experiments highlight this effect. The results showed and increase in CNR

with number of acquisitions. For each number of acquisitions, the observed CNR value was

used to calculate the predicted corresponding CNR values for all other number of acquisitions.

Figure 4-1 shows that the predicted CNR values are within the +/- 2 of the observed CNR values,

indicating that the gains in CNR are due to the gains in SNR obtained by increasing number of

acquisitions. The calculations for this experiment are included in Appendix B.









Presaturation power and Length

Increasing the presaturation power clearly resulted in an increase in contrast generation.

The results indicated that the gain in contrast was not limited, meaning there was no point at

which additional power caused a decrease in contrast. The amount of power was limited by both

the imaging system and the amount of heat tolerated by the sample. The amount of sample

heating was similar b between sy stems, but the amount each coil was capable of supplying without

arcing differed vastly between systems.

The amount of contrast also increased with longer saturation times, as indicated by the

decrease in signal with the increase of presaturation pulses. The gain in contrast appeared to

approach a maximum (with minimum signal) as the time of presaturation increased. This

maximum is expected as the system reaches steady state, at which point any additional bulk

water proton saturation will correspond to an earlier saturated proton losing its saturation,

resulting in no gain in contrast. The difference between 2000 and 4000 pulses was small in both

the imaging and spectral experiments, but in all cases except the image at 4 CIT 4000 pulses

resulted in more contrast generation than 2000 pulses. This indicates that a gain in contrast may

be possible by increasing the presaturation time from 2 to 4 seconds. The resultant gain in most

cases will not be large enough to justify effectively doubling the total scan time. However in

systems which are limited to low powers, increasing presaturation time can be used to achieve

comparable contrast.

Cell Labeling

The results of the cell labeling experiments did indicate successful contrast generation.

The labeling media was included in the phantom as a positive control to ensure the presaturation

was sufficient to generate contrast. The labeling media was easily identifiable, and generated

significant contrast. The cells in this phantom were imaged in solution (not agarose) and were









allowed to sink to the bottom of the tube. The cells di d not generate significant contrast. There

are several reasons this may be the case. The most obvious explanation is that the labeling did

not work, and the cells retained no contrast agent. An alternate explanation is that the cells did

uptake the label, but once in the cell the contrast agent was no longer able to generate contrast.

The ultimate destination of the contrast agent within the cell is unknown, and thus the water

exchange is also unknown. As previously discussed, this contrast is highly dependent on the

chemical exchange of the contrast agent with its surrounding water. If the cell

compartmentalized the contrast agent into an area with extremely restricted water exchange, the

contrast would not be generated.

Lastly, there may have been contrast generation, but thi s contrast i s calculated by the

difference between the area with the contrast agent and the area without contrast (water). Each

phantom is constructed to include ddH20 for comparison. In this phantom, the ddH20 generated

contrast. This indicates a high probability that the labeling media leaked into the surrounding

water. Because all of the tubes in this phantom were constructed of open ended capillary tubes

closed with clay, there is chance that they all leaked into the surrounding water. In addition to

elucidating the possibility of leaking, the contrast in the surrounding water negated the ability to

make a CNR, because the SNRwater was not valid. There may have been some contrast

generation in the cells, but the amount was negligible compared to the labeling media and the

surrounding water.

Previous studies used Eu-2 to generate contrast in a cell pellet [69]. The same labeling

protocol was followed in this study, but the cells were allowed to sink to the bottom rather than

creating a cell pellet. There are two possible explanations why this study failed to generate

contrast. The first is that each cell contains only a minute amount of contrast agent. Allowing









the cells to remain somewhat dispersed in solution may have caused the contrast to be dispersed

as well, which would result in very low concentration of the contrast agent in each the imaging

voxel. As shown previously, there is a minimum amount of contrast agent needed to produce

detectable contrast, and the cell solution did not meet this threshold. Alternately, the cells may

have restricted the amount of chemical exchange possible for the contrast agent, which would

also negate its ability to create contrast. The cell pellet formation either localized all the cells

increasing the total concentration or possibly destroyed the cells releasing the contrast into

solution, allowing for free chemical exchange. In either case, this contrast agent would not be

optimal for molecular imaging, as either it does not have a high enough concentration per cell for

cellular imaging, or its chemical exchange is no longer favorable within cells. As such, this

agent was determined not to be optimal for cell labeling studies.

Simul atio n

MATLAB simulations were made to see the effect of different variables on the resulting

CEST spectra for a PARACEST agent. Each simulation was done allowing only one variable to

change, while holding others constant, in order to pinpoint the effect of the initial variable. By

evaluating the effect of several variables, the optimal values can be determined. This

information can be used for future studies, to determine what agents have the greatest potential.

Relaxation Times

The results of the simulation indicated that an increase in T1 increases the contrast

generation potential. This increase in contrast illustrates the effect of longer T1 values increasing

the efficiency of CEST. This can be derived from the modified Bloch equations resulting in

Equation 4-1 [87].


Z = (4-1)









Z is the equilibrium bulk water magnetization, which is proportional to the observed water

signal, z is the lifetime of a proton in the bulk water, and T1 is the T1 relaxation time of the bulk

water. This equation predicts that longer the T1, the lower the magnetization, which would result

in a greater decrease in the bulk water signal. This also theoretically makes sense, because the

longer T1 would allow protons to retain their saturation longer, which would allow for longer

presaturation times increasing the amount of total saturation.

The simulation of changing T2 indicated that decreasing T2 TOSulted in an increase in width

of the bulk water peak in the CEST Spectra, with little effect of the CEST effect. This illustrates

that with short T2 ValUeS, there is greater direct saturation of bulk water farther off resonance.

In reality, it is generally not possible to increase or decrease the relaxation values of a

given sample/system without the addition of other contrast agents. This could result in a change

in the amount of viable contrast generated by PARACEST agents. However, knowing the effect

of the relaxation parameters on di fferent contrast agents would allow selection of specific targets

or tissues which would most benefit from this type of contrast. For example, knowing that

systems with large T1 values would benefit from PARACEST agents, areas of the brain like the

ventricles with documented T1 values of 2.77 s at 17.6 T [45] would be likely be a better target

than tissues with shorter values like the corpus callosum (T1 of 1.88 s at 17.6 T [45]). Also, T1

was found to increase with temperature, so if the sample system permits higher temperatures, the

CEST effect may be increased with sample heating with careful attention to the effect of

temperature on chemical exchange (which also increases). The limited effect of changing T2 On

the amount of CEST contrast generation indicates that tissues with varying T2 WOuld still be

good candidates for PARACEST contrast, however the increased direct saturation of bulk water










with decreased T2 ValUeS indicates that tissues with very short T2 WOuld need a PARACEST with

a larger Amo to compensate.

Chemical Shift Differences

The simulation showed that changing Aso alone only effected the location of the bound

water peak. But the shifts closest to water (Figure 3-31-B) show how could be beneficial. For a

contrast agent with a Aso close to water, the bulk and bound water peaks may be close enough

together so as to be indistinguishable. Going to higher fields will increase Amo, and allow the two

peaks to be resolved.

Chemical Exchange Rates

Increasing the chemical exchange rate resulted in an increase in contrast generation to a

point, and then a decrease. This indicated that there is an optimal value for the chemical

exchange, which is consistent with expectations. When the exchange is too slow, the exchange

will not meet the requirement that Amo zM > 1, and no contrast will be generated. Likewise, if the

exchange is very slow, it just meets the requirement, and a small amount of contrast will be

generated. At this point, increasing the chemical exchange will allow greater contrast to be

generated. However, once the chemical exchange can become too fast, which is shown in the

simulation by the decrease in CEST with exchange rates of 8000 and 10000. Both of these

simulations also show an increase in the width of the bulk water saturation. As the chemical

exchange goes from intermediate to fast, the effect of the contrast agent changes. When the

exchange is slow to intermediate, the contrast effects only the bound pool of protons, and this

pool can exchange with bulk water. However, when the exchange becomes very fast, the effect

is then seen as bulk water effect. Thi s bulk water effect can be utilized for other types of contrast

generation, as with gadolinium agents which have very fast exchange and are efficient T1










relaxation agents. But for PARACEST contrast, this increase in chemical exchange rate to fast

exchange would be detrimental, as eventually it would no longer be possible to differentiate (and

saturate) the bound water pool.

Presaturation Power

In agreement with the findings of the previous section, the simulation showed that an

increase in presaturation power resulted in an increase in contrast generation. However the

simulation was used to show the results of much higher powers than were actually tested. This

highlights one of the main benefits to using computer simulations: the ability to test parameters

which may cause damage in real life. The simulation showed that at very high powers, the gain

in contrast reaches an approximate maximum. It also shows an overall decrease in signal,

signifying broader saturation, and more power deposition into the system. Notably, it may

actually indicate an optimal value for the power. The decrease in the CEST effect on the positive

side of water levels out, and appears to reach a minimum. However, since most PARACEST

contrast is done as a difference image it is important to note that the water signal on the negative

side of water i s also decreasing, so the difference will actually decrease.

Concentratio n

The simulation shows an increase in CEST contrast with increasing concentration, in

agreement with expectations and previous findings. The concentration was simulated by

changing the ratio exchangeable protons to bulk protons. For molecular imaging, the

concentration will likely be limited by the amount of contrast agent which can be tolerated in a

cell or system. However, the simulation and the data show that contrast will increase with

concentration, so the highest possible concentration is desirable.










Temperature Fit

The values for T1 were measured for each of the temperatures at which data were collected,

and these values were used to fit the data. The chemical exchange rate, the T1 relaxation times,

and the Amo values were allowed to vary for the temperature fit. The results showed that the fit

and the data matched very well, with R2 ValUeS between 0.991 and 0.998. Upon further

inspection, the values of the chemical exchange from the fit increase with increasing

temperature, and the Aso also shifts towards water.

Number of Acquisitions Fit

The number of acquisitions data and fit were in good agreement with R2 ValUeS between

0.985 and 0.994. However, upon closer inspection, the only variable which changed between fits

in the simulation was the chemical exchange rate and the RF irradiation. There is no physical

reason that an increase in number of acquisitions would increase the chemical exchange rate, and

the RF irradiation was not changed between acquisitions. There is also a visible difference

between the fit and the data at the points farthest from water, especially with 32 and 64

acquisitions. The simulation assumes approximate steady state, which is likely not actually

obtained. The success of thi s simulation to fit the data shows its versatility, but also illustrates its

fallibility, as the values for chemical exchange and irradiation are not accurate. It also illustrates

the importance of carefully considering the values of resulting variables for feasibility, before

accepting the results.

Conclusions

The overall purpose of thi s study was to optimize contrast agents for use in hi gh magnetic

fields. The results showed several important results, most notably the fact that there is no one

perfect contrast agent for high magnetic fields. The best contrast agent will depend on several

variables, not just the increased magnetic field strength.









Traditional T1 contrast agents used in this study were shown to decrease in efficacy at high

field strengths. This does not negate their utility, especially at lower magnetic field strengths.

Because of their positive contrast and low toxicity profiles, gadolinium based contrast agents will

likely remain one of the more prominent contrast agents. However, because of their decreased

efficacy at high magnetic fields, they would not be an optimal choice for ultra high field MRI.

Traditional T2 COntrast agents were shown to slightly increase efficacy at high fields, but

they will likely suffer from higher susceptibility artifacts at high field strengths and a high

incidence of false positives. That being said, iron oxides maintain especially great promise for

further use in cellular and molecular imaging, including ultra high field MRI.

PARACEST contrast agents are relatively new type of contrast, but hold great promise,

especially at higher field strengths. This study showed an increase in contrast generation for

current europium based contrast agents. It also showed the ability of different contrast agents

that were unable to generate contrast at lower magnetic field strengths to generate PARACEST

contrast at ultra high magnetic fields. There are several other factors contributing to a

PARACEST contrast agent's utility for cellular and molecular imaging, which will be discussed

further below. However, the overall conclusion is that the PARACEST agents have great

potential for use in cellular and molecular imaging using ultra high field MRI.

MRI benefits from increased SNR at high fields. PARACEST contrast enj oys the same

benefit, but also the added benefits of increased Aco. One study [45] found that ultra high fields

increased the T1 relaxation values in all measured areas of tissue in mouse brain. PARACEST

contrast would benefit from this increase in T1 with increased CEST efficiency. The iron oxides

would benefit from the added SNR, but may suffer from increased susceptibility artifacts leading









to image distortion especially in the T2 weighted images which would best enhance the iron

oxide contrast [107].

While PARACEST results in a negative contrast, it is often taken as a difference image

with one image taken on the resonance of the bound water pool and the other on the opposite

side of the bulk water peak. Areas of hypointensity intrinsic to the image would appear dark in

both of the image, and thus cancel out of the difference image. As such, PARACEST agents

avoid the inability to differentiate between inherent hypointensity and hypointensity caused by

the contrast. In this aspect, PARACEST agents have the advantage over iron oxides.

PARACEST agents are detectable at concentrations down to 0.4 mM and generate greater

contrast with increased concentration, but iron oxides maintain the largest contrast generation

potential per unit metal. In this sense, for applications with very limited amount of contrast

agent, iron oxides would likely be the better candidate.

PARACEST agents are dependent on chemical exchange, which itself is effected by

variables including temperature, pH- and surrounding chemical environment (presence of small

molecules). This leads to a sensitivity of PARACEST agents to these variables, in some cases

[73,74,77,79,108-1 11,104-106] meaning that the contrast agent can be used to monitor those

variables. This ability to monitor pH, temperature and small molecules adds physiological

information too otherwise purely anatomical images, a good first step towards molecular

imaging.

The utility of a given PARACEST agent is going to depend not only on the pH and

temperature, but also the field strength (as previously discussed) and the presaturation power and

duration. Increased duration of presaturation will initially yield good improvements in contrast

(up to ~2s), but eventually will give diminishing returns relative to the increase in time for









imaging. And the power will also give good returns for increased power levels, but is limited by

the ability of the system to apply the power efficiently (and homogenously), and by the ability of

the sample to tolerate a certain amount of power deposition. Additionally the amount of

PARACEST contrast will be highly sensitive to movement. If the contrasted portion of the

sample moves at all between scans, the difference image will no longer be valid.

The general conclusion of this study is that iron oxides and PARACEST agents are both

viable options for high field MRI. Each has advantages, and should both be considered

depending on the intended use. Further discussion for future uses is included in the final chapter

of this study.














.9 20 2 scans
--- 4 scans
15 scans

16 scans
S --- 32 scans
c,10 -
[/;;;;;;; 64 scans
previous scan
u 5- ITF CNR



0 20 40 60

NTumber ofAquisitions


Figure 4-1. Number of acquisitions versus CNR. Each series is the data point indicated, with the
other points calculated based on a gain in SNR proportional to the number of
acquisitions relative to that point. The error bars illustrate that the predicted gains are
within +/- 2 in most cases, which is within the error of the measurement.









CHAPTER 5
FUTURE PERSPECTIVES

Magnetic Resonance Imaging is both noninvasive and nondestructive. It has proven its

utility as a medical imaging modality, and is currently used to diagnose a myriad of conditions

ranging from va scular di seases [1 12-1 1 5] to cancers [1 16-1 19] (and more). And these studies

are all done at 1.5 or 3 T. With the increased resolution afforded by higher magnetic field

strengths, it is easy to conclude that higher field imaging could be an even more powerful

diagnostic tool. That being said, the contrast agents currently used in medical imaging are

optimized for the lower field strengths. As higher fields are becoming more and more available,

the need arises to evaluate the current contrast agents at high fields, and investigate possible

alternatives.

First, gadolinium has been the gold standard for medical imaging contrast for years.

Although recently iron oxides have come into favor for liver imaging [61,64,65], gadolinium

remains the choice for pretty mu ch everything else (from brain scans to lumber/thoracic spinal

imaging) with an estimated 30% of all MRI scans done with gadolinium based contrast

enhancement [47]. It was the obvious starting point for MRI contrast investigation.

Unfortunately the contrast of the gadolinium complex did not improve, and in fact decreased in

efficacy at higher field strengths.

However, gadolinium still has the benefit to easily obtained positive contrast at lower field

strengths. It is therefore is still being investigated by others as a cell labeling contrast agent.

One study used poly ethylene glycol (PEG) coated liposomes loaded with Gd3+ chelates to

successfully target inflamed endothelial cells in vitro, and concluded the results implied possible

future use for disease diagnosis and therapeutic efficacy tracking [120]. Other studies

investigated several routes for cell labeling with gadolinium contrast agents, including via









transfection agents[121], receptor targeting [122,123]i and pinocytosi s (cells incubated in media

with a high concentration of contrast agent) [124,125]. Aime et al. concluded that MRI cell

visualization was possible when a minimum concentration of Gd chelates was met (10'-10"

chelates/cell) [67]. Gadolinium agents have also been used to evaluate gene therapy for

experimental brain tumors [126]. The general conclusion of these studies was that further

investigation of cell tracking studies using gadolinium-based agents was warranted.

It is the conclusion of this study that gadolinium agents will still provide an excellent

choice for contrast generation at lower fields, but when moving to ultra high fields there will

likely be better alternatives, including iron oxides and PARACEST agents.

Ferumoxides are usually used to create a negative contrast, with the previously discussed

di advantages associated with hy pointensity, namely the inability to di stinguish b between

hypointensity created by contrast and hypointensity inherent in the sample/patient. However, the

greatest signal change per unit metal [93] is a huge advantage, and therefore causes a lot of

interest for cell labeling. This study showed that there was some improvement in T2* with

increased field strength, so they remain viable options for high field contrast generation.

Multiple groups have stated that iron oxides are the best candidates for molecular imaging

via MRI [63,93]; a very strong claim. But there are various studies which seem to back up that

claim. Weissleider et al. used iron oxide nano particles conjugated to polyclonal IgG antibody to

image induced liver inflammation and cancer in vivo [127] and myocardial infarction

ex vivo [128]. Other groups have found that it is possible to use iron oxides in MRI to target

specific processes including apoptosis [129], atherosclerosis [130] and amyloid plaque

deposition in Alzheimer's [131]. Zhu et al. used iron oxides to track the migration of neural stem

cells and showed that the label had no effect on the ability of the cells to differentiate into









astrocytes and neurons during migration [132]. Several studies have come out investigating the

use of monoclonal antibodies for tumor specific iron oxide particles [133-135]. Shapiro et al.

has reported single cell detection using iron oxides [96,136,88].

Also, notably several groups have introduced pulses sequence which causes iron oxides to

have positive contrast [137-139]. This is accomplished through spectrally selective excitation

and refocusing of spins in the vicinity of the SPIO [137], steady state free precession imaging

[138] or inversion of the magnetization in conjunction with a spectrally-selective on-resonant

saturation of water [139]. They benefit from the hyper intensity, but the resulting signal to noise

is not optimal. With work, however, this type of imaging may prove to be a viable alternative to

gadolinium at higher fields.

PARACEST agents are relatively new, but this study shows a substantial improvement

with increasing field strength. Aime et al. reported cell labeling with multiple PARACEST

agents, and showed the ability to highlight contrast in either agent at will [69]. Exogenous

europium based agents with two phenylboronate moieties as the ligands have recently been used

to monitor the amount of glucose in livers [140]. In addition to detecting enzyme activity [106],

Yoo and Pagel have reported protease responsive PARACEST agents [141] and peptide linked

PARACEST agents for molecular imaging [142].

There has been increased research in CEST recently, including advances such as

'GLYCOCEST', 'LIPOCEST' and 'DIACEST'. GLYCOCEST uses the hydroxyl groups on

glycogen to create CEST contrast as a means to monitor glycogen [143]. LIPOCEST utilizes

liposomes to create more favorable exchange rates and chemical shifts in order to have more

lanthanide ions available for use in PARACEST contrast [100,144,145]. DIACEST was

introduced with a fast screening mechanism to determine viability of polypeptides for CEST










contrast generation, and then demonstrated the ability to highlight each of several polypeptides

independently [146]. All of these mechanisms would benefit from increased contrast at higher

fields.

Most importantly, the overall goal is a movement towards molecular or cellular imaging.

This is beneficial because currently MRI provides fantastic anatomical information, but little to

no metabolic information. MRI should be able to provide this dual functional-anatomical

information imaging in the future, and optimized contrast agents will likely play a huge role.

However, the type of agent is going to depend on the goal of the individual study. This is not

new; currently medical doctors choose between gadolinium and iron oxide contrast agents

depending on the patient and the area of interest. Scientists have the options for similar choices,

even at high fields. There is lot to gain by going to high fields, especially as the larger magnets

become more accessible. And at this point, iron oxides and PARACEST agents make the best

contrast.









APPENDIX A
IRON OXIDES DATA

Single slice multi spin multi echo images were taken at constant TR Of either 400 ms or

2000 s, with variable TE in Order to calculate T2'. Those images are included in Figures A-1 and

A-2 for 4.7 T, Figures A-3 and A-4 for 14.1 T and Figures A-5 and A-6 for 21.1 T. The TE

values are indicated in the figure caption.


Figure A-1. Iron oxide images at 4.7 T. TR = 400 ms. TE = A) 3.4 ms, B) 9 ms, C) 12 ms, D)
15 ms, E) 30 ms, F) 45 ms and G) 90 ms. The image quality was insufficient to make
T2* meaSurements from.






























Figure A-2. Iron oxide images at 4.7 T. TR = 2 s. TE = A) 3.4 ms, B) 6 ms, C) 9 ms, D) 12 ms,
E) 15 ms, F) 30 ms, G) 45 ms and H) 90 ms. The image quality was insufficient to
make T2* meaSurements from.























Figure A-3. Iron oxide images at 14.1 T. TR = 400 ms. TE = A) 2.5 ms, B) 5 ms, C) 10 ms, D)
20 ms, E) 30 ms and F) 60 ms. The contents of the tubes are clockwise from top:
ferumoxide 1/10 dilution, ddH20, 50 mM ferumoxide, 100 mM ferumoxide, 20 mM
ferumoxi de.
































Figure A-4. Iron oxide images at 14.1 T. TR = 2000 ms. TE = A) 2.5 ms, B) 5 ms, C) 10 ms, D)
20 ms, E) 30 ms and F) 60 ms. The contents of the tubes are clockwise from top:
ferumoxide 1/10 dilution, ddH20, 50 mM ferumoxide, 100 mM ferumoxide, 20 mM
ferumoxi de.


Figure A-5. Iron oxide images at 21.1 T. TR = 400 ms. TE = A) 3 ms, B) 6 ms, C) 9 ms, D) 12
ms, E) 30 ms, F) 45 ms and G) 90 ms. The contents of the tubes are clockwise from
right: ferumoxide 1/10 dilution, ddH20, 50 mM ferumoxide, 100 mM ferumoxide, 20
mM ferumoxide.






























Figure A-6. Iron oxide images at 21.1 T. TR = 2000 ms. TE = A) 3 ms, B) 6 ms, C) 9 ms, D) 12
ms, E) 15 ms, F) 30 ms, G) 45 ms and H) 90 ms. The contents of the tubes are
clockwise from right: ferumoxide 1/10 dilution, ddH20, 50 mM ferumoxide, 100 mM
ferumoxide, 20 mM ferumoxide.









APPENDIX B
PARACEST AGENTS DATA AND CALCULATIONS

Ytterbium, Thulium and Europium PARACEST

All four ytterbium and thulium complexes were images at 14.1 T prior to imaging at

21.1 T. While the results at 900 were very small at best, there were no distinguishable contrasts

generated by any of these complexes. Sample images from the region of interest (presaturation

on and off the bound water specific to the complex) are included in Figures B-1 B-6. Three

different europium complexes were imaged at various field strengths. The CEST spectrum for

all three europium complexes are included in Figure B-7.

Europium PARACEST Field Dependence

To determine the field dependence of the PARACEST agents the phantom containing all

three europium agents was imaged field strengths from 4.7 21 T and these results are shown in

the Results section. The calculations for the CNR values are shown in Tables B-1 B-4 for 4.7,

11.75, 17.6 and 21.1 T, respectively. This table shows the mean value and standard deviation

within each region of interest in the difference image with presaturation at the optimal offset

(from which the reported values were taken). The SNR for all non-noise values and the CNR for

all non-water values calculated from the mean and standard deviation.

Many of the other phantoms were tested at various field strengths, especially 14.1 and 17.6

T. When the power levels were the same, the results always indicated greater contrast at the

higher field strengths. The additional imaging from various field strengths is included in the

following appendices, under the type of phantom being imaged.

Europium PARACEST Concentration

The serial dilutions phantom which was presented in the main body of this investigation

was imaged many times, at different field strengths. The results indicated similar findings as









were presented, with increasing contrast generation with increased concentration. Figures B-8 -

B-10 show the results of these imaging experiments at field strengths of 14.1 T. Figures B-11

and B -12 show the imaging results at 17.6 T. Figures B-13 and B-14 show the imaging results at

21.1 T. The presaturation power ranged from 4 128 CIT as indicated in the figure captions.

Figure B -15 shows the CEST spectrum calculated from the images at 21.1 T. These

spectrum suffer from a lot of noise, likely due to the inhomogeniety of the presaturation and the

limited number of presaturation offsets.

Europium PARACEST pH

The pH phantoms which were presented in the main b ody of thi s investigation were

imaged many times, at different field strengths. The results indicated similar findings as were

presented, with a shift in optimal presaturation offset with pH, and viable contrast for pH values

of 3, 7 and 10. Initial findings indicated no contrast generation at pH 13, but later studies

showed contrast, which was attributed to sample degradation or instability in the extremely basic

solution. Figures B -16 B-23 show the results of these imaging experiments at field strengths of

14.1 T, with presaturation power ranging from 4 128 CIT. The presaturation duration was 2 s

except for Figures B-22 and B-23. Figures B-24 shows the imaging results at 17.6 T. Figures

B-25 and B-26 show the imaging results at 21.1 T. Figure B-27 shows the imaging results at

17.6 T for the pH phantom including the ion rich environment.

Europium PARACEST Temperature

The CEST spectra of a 10 mM Eu-2 sample was taken at 14, 20, 26, 32 and 36 oC. The

cumulative results are presented in the main body of the text, but they are individually shown in

Figures B-28 B-32 respectively.










Europium PARACEST Signal to Noise

The individual spectra for each of the multiple acquisition experiments are include as

Figures B-33 B-38 for NS = 1, 2, 4, 8, 32 and 64 respectively.

Europium PARACEST Presaturation Power and Duration

The amount and duration of the applied RF presaturation power was tested on numerous

occasions with both the serial dilutions phantom and the second pH phantom. In all cases, when

tolerated by the system, higher powers resulted in greater contrast generation. The data was

presented in the main body of the text, and also throughout this Appendix (B), as indicated by

the labeled presaturation power, or number of presaturation pulses. Figure B-39 shows the effect

of increasing power and duration of presaturation on the CNR for the serial dilution phantom,

and further illu states the resulting increase in contrast generation.












53 ppm


+I


Figure B -1. Thulium complexes at 14.1 T. A) Images taken with the presaturation power
applied on the positive side water at the shift listed. B) Images taken with the
presaturation power on the opposite side of water at the shift listed. C) Difference
images taken to create positive contrast as B A. C) Difference images taken to
create negative contrast as A B. Complexes labeled as 1) Tm-1 10 mM, 2) Tm-2
10 mM and 3) Eu-2 40 mM.


450 ppm






475 ppm






500 ppm










53 ppm


Figure B -2. Ytterbium complexes at 14.1 T. A) Images taken with the presaturation power
applied on the positive side water at the shift listed. B) Images taken with the
presaturation power on the opposite side of water at the shift listed. C) Difference
images taken to create positive contrast as B A. C) Difference images taken to
create negative contrast as A B. Complexes labeled as 1) Yb-1 10 mM, 2) Yb-2 10
mM and 3) Eu-2 10 mM.


450 ppm





475 ppm





500 ppm











53 ppm


500 ppm






520 ppm






530 ppm IC~




Figure B -3. Thulium complexes at 17.6 T. A) Images taken with the presaturation power
applied on the positive side water at the shift listed. B) Images taken with the
presaturation power on the opposite side of water at the shift listed. C) Difference
images taken to create positive contrast as B A. C) Difference images taken to
create negative contrast as A B. Complexes labeled as 1) Tm-1 10 mM, 2) Tm-2
10 mM and 3) Eu-2 40 mM.











53 ppm


500 ppm PlSI





520 ppm






550 ppm 1




Figure B -4. Thulium complexes at 17.6 T. A) Images taken with the presaturation power
applied on the positive side water at the shift listed. B) Images taken with the
presaturation power on the opposite side of water at the shift listed. C) Difference
images taken to create positive contrast as B A. C) Difference images taken to
create negative contrast as A B. Complexes labeled as 1) Tm-1 10 mM, 2) Tm-2
10 mM and 3) Eu-2 40 mM.









































Figure B -5. Ytterbium complexes at 17.6 T. A) Images taken with the presaturation power
applied on the positive side water at the shift listed. B) Images taken with the
presaturation power on the opposite side of water at the shift listed. C) Difference
images taken to create positive contrast as B A. C) Difference images taken to
create negative contrast as A B. Complexes labeled as 1) Yb-1 10 mM, 2) Yb-2 10
mM and 3) Eu-2 10 mM.


53~ ppm


-


200 ppm






208 ppm~






216 ppmn










53 ppm


200 ppm PP s





210 ppm





225 ppm 3




Figure B -6. Ytterbium complexes at 17.6 T. A) Images taken with the presaturation power
applied on the positive side water at the shift listed. B) Images taken with the
presaturation power on the opposite side of water at the shift listed. C) Difference
images taken to create positive contrast as B A. C) Difference images taken to
create negative contrast as A B. Complexes labeled as 1) Yb-1 10 mM, 2) Yb-2 10
mM and 3) Eu-2 10 mM.

















I I






55 -
-0 Eu- I






B 'I






-90 -70 -!0 -30 -10 10 30 50 70 803
Presaturation Offset (ppm)


Figure B-7. Europium complexes CEST spectra at 14.1 T. Spectra acquired by taking the signal
intensity within regions of interest in images with the presaturation offsets indicated
on the x axis.

Table B-1. CNR calculations for 4.7 T.
offset (ppm) Mean Std.Dev. SNR CNR error
Eu-1 65 1086 100157 0 0 1.5
Eu-2 53 -15971 93411 -0.2 0 1
Eu-3 68 69946 10667 1.4 1 1
Water 53 -2919 97646 -0.1
Water 65 16351 98777 0.2
Water 68 18202 94921 0.3
Noi se 53 848 70454
Noi se 65 2669 68 143
Noi se 68 -1787 74657










Table B-2. CNR calculations for 17.6 T.
offset (ppm) Mean Std.Dev. SNR CNR error
Eu-1 65 20845 3659 8.4 7.9 1.5
Eu-2 53 91207 4498 39 39 2
Eu-3 68 67368 4116 30 25 2
Water 53 400 3669 0.4
Water 65 -185 3234 0.5
Water 68 10761 3414 5.2
Noi se 53 98 1535
Noi se 65 -180 1598
Noi se 68 181 1602


Table B-3. CNR calculations for 21.1 T.
offset (ppm) Mean Std.Dev. SNR CNR error
Eu-1 65 16099 3320 8.4 8.7 2
Eu-2 53 96201 3202 53 54 1.8
Eu-3 68 70910 2907 37 37 1.5
Water 53 -2982 3038 -1
Water 65 -111 3301 -0.3
Water 68 6418 1187 -0.7
Noi se 53 84 1822
Noi se 65 4 1922
Noi se 68 51 1931













51ppm


Figure B-8. Serial dilutions at 14.1 T. Presaturation power was 16 CIT. A) Images taken with the
presaturation power applied on the positive side water at the shift listed. B) Images
taken with the presaturation power on the opposite side of water at the shift listed. C)
Difference images taken to create positive contrast as B A. C) Difference images
taken to create negative contrast as A B. The concentrations are labeled as 1) 100
mM, 2) 25 mM, 3) 50 mM, 4) 3 mM, 5) 6 mM, 6) 12.5 mM, 7) 0.4 mM, 8) 0.8 mM
and 9) 1.6 mM.











51ppm






53ppm






55ppm


D


Figure B -9. Serial dilutions at 14.1 T. Presaturation power was 32 CIT. A) Images taken with the
presaturation power applied on the positive side water at the shift listed. B) Images
taken with the presaturation power on the opposite side of water at the shift listed. C)
Difference images taken to create positive contrast as B A. C) Difference images
taken to create negative contrast as A B. The concentrations are labeled as 1) 100
mM, 2) 25 mM, 3) 50 mM, 4) 3 mM, 5) 6 mM, 6) 12.5 mM, 7) 0.4 mM, 8) 0.8 mM
and 9) 1.6 mM.


g *


g'


a8












51ppm mYY




53ppm. ~





55ppm




Figure B-10. Serial dilutions at 14.1 T. Presaturation power was 64 CIT. A) Images taken with
the presaturation power applied on the positive side water at the shift listed. B)
Images taken with the presaturation power on the opposite side of water at the shift
listed. C) Difference images taken to create positive contrast as B A. C) Difference
images taken to create negative contrast as A B. The concentrations are labeled as
1) 100 mM, 2) 25 mM, 3) 50 mM, 4) 3 mM, 5) 6 mM, 6) 12.5 mM, 7) 0.4 mM,
8) 0.8 mM and 9) 1.6 mM.












53ppm





























Figure B -1 1. Serial dilutions at 17.6 T. Presaturation power was 16 CIT. A) Images taken with
the presaturation power applied on the positive side water at the shift listed. B)
Images taken with the presaturation power on the opposite side of water at the shift
listed. C) Difference images taken to create positive contrast as B A. C) Difference
images taken to create negative contrast as A B. The concentrations are labeled as
1) 50mM, 2) 25mM, 3) 100 mM, 4) 12.5 mM, 5)6 mM, 6)3 mM, 7) 1.6 mM,
8) 0.8 mM and 9) 0.4 mM.












53ppm i tll ~ B































Figure B-12. Serial dilutions at 17.6 T. Presaturation power was 32 CIT. A) Images taken with
the presaturation power applied on the positive side water at the shift listed. B)
Images taken with the presaturation power on the opposite side of water at the shift
listed. C) Difference images taken to create positive contrast as B A. C) Difference
images taken to create negative contrast as A B. The concentrations are labeled as
1) 50mM, 2) 25mM, 3) 100 mM, 4) 12.5 mM, 5)6 mM, 6)3 mM, 7) 1.6 mM,
8) 0.8 mM and 9) 0.4 mM.











47ppni


Sl ppni EP1BIB




53ppni





55ppn1~1-i




Figure B -13. Serial dilutions at 21.1 T. A) Images taken with the presaturation power applied
on the positive side water at the shift listed. B) Images taken with the presaturation
power on the opposite side of water at the shift listed. C) Difference images taken to
create positive contrast as B A. C) Difference images taken to create negative
contrast as A B. The concentrations are labeled as 1) 0.4 mM, 2) 0.8 mM,
3) 1.6 mM, 4) 3 mM, 5) 6 mM, 6) 12 mM, 7) 100 mM, 8) 25 mM and 9) 50 mM.











55ppni


57ppni sfiP1 1




59ppni





61ppni~




Figure B -14. Serial dilutions at 21.1 T. A) Images taken with the presaturation power applied
on the positive side water at the shift listed. B) Images taken with the presaturation
power on the opposite side of water at the shift listed. C) Difference images taken to
create positive contrast as B A. C) Difference images taken to create negative
contrast as A B. The concentrations are labeled as 1) 0.4 mM, 2) 0.8 mM,
3) 1.6 mM, 4) 3 mM, 5) 6 mM, 6) 12 mM, 7) 100 mM, 8) 25 mM and 9) 50 mM.














-) 0Lq;~.8 h 100 mM
-25 mM
50 mMi
2 0.6 .12.5 mM
b,~ ~6.3 mMi
~~~5~ 1. 3.1mM
4i~ 1.65 mMi
-0.4
--0.8 mMv
W I I Ikl0.4 mM

0.2 water
0 noise




101 0 -101

Presatut.ration ODffset (ppm)

Figure B -15. CEST spectrum of Serial Dilutions at 21.1 T. Spectrum are calculated from
images.









51 ppm


53 ppm UJ U1~
O O


SS ppm O" 8
O O)


579 ppm O0 *o
O)'1 O*


59 ppm O~ *0
OO*


6 Ippm l 11 O0
JOO

Figure B -16. Variable pH phantom at 14. 1 T. Presaturation power was 32 CIT, presaturation
offsets from 51-61 ppm. A) Images taken with the presaturation power applied on the
positive side water at the shift listed. B) Images taken with the presaturation power
on the opposite side of water at the shift listed. C) Difference images taken to create
positive contrast as B A. C) Difference images taken to create negative contrast as
A B. pH values are labeled.










63 ppm


65 ppm O
O I O


67 ppmn 0 O"
O O


69 ppm O0 O0
O O


71 ppm 0
3O ~O ~~e

Figure B -17. Variable pH phantom at 14.1 T. Presaturation power 32 CIT, presaturation offsets
from 65 71 ppm. A) Images taken with the presaturation power applied on the
positive side water at the shift listed. B) Images taken with the presaturation power
on the opposite side of water at the shift listed. C) Difference images taken to create
positive contrast as B A. C) Difference images taken to create negative contrast as
A B. pH values are labeled.












14 ppm 0 I






ss ppm 8*7 go





OO *



6 1 ppm to 7 O, *





30 0L *!L

Figure B -18. Variable pH phantom at 14.1 T. Presaturation power 64 CIT, presaturation offsets
from 51 61 ppm. A) Images taken with the presaturation power applied on the
positive side water at the shift listed. B) Images taken with the presaturation power
on the opposite side of water at the shift listed. C) Difference images taken to create
positive contrast as B A. C) Difference images taken to create negative contrast as
A -B. pH values are labeled.













63 ppm


65 ppm O OT







69 ppm O0 O0;' i




7 1 ppm lo3 O,






Figure B -19. Variable pH phantom at 14.1 T. Presaturation power 64 CIT, presaturation offsets
from 65 71 ppm. A) Images taken with the presaturation power applied on the
positive side water at the shift listed. B) Images taken with the presaturation power
on the opposite side of water at the shift listed. C) Difference images taken to create
positive contrast as B A. C) Difference images taken to create negative contrast as
A- B. pH values are labeled.










51 ppm 8










55 ppm 8T 8












61 ppm 8o 9 O






Figure B -20. Variable pH phantom at 14.1 T. Presaturation power 128 CIT, presaturation offsets
from 51 61 ppm. A) Images taken with the presaturation power applied on the
positive side water at the shift listed. B) Images taken with the presaturation power
on the opposite side of water at the shift listed. C) Difference images taken to create
positive contrast as B A. C) Difference images taken to create negative contrast as
A- B. pH values are labeled.












63 ppm


5s ppm CIr




67 ppm O" O *
O O1 *


69 ppm O0 O0 *
O Oc1 *






Figure B -21. Variable pH phantom at 14.1 T. Presaturation power 128 CIT, presaturation offsets
from 65 71 ppm. A) Images taken with the presaturation power applied on the
positive side water at the shift listed. B) Images taken with the presaturation power
on the opposite side of water at the shift listed. C) Difference images taken to create
positive contrast as B A. C) Difference images taken to create negative contrast as
A B. pH values are labeled.











57 ppm Q CQI E
SO *Ld



59 ppm '8 O *
*II O *



6 1ppm Il3 OC *



Figure B -22. Variable pH phantom at 14.1 T. Presaturation power 128 CIT, presaturation offsets
from 51 61 ppm, presaturation time 4 s. A) Images taken with the presaturation
power applied on the positive side water at the shift listed. B) Images taken with the
presaturation power on the opposite side of water at the shift listed. C) Difference
images taken to create positive contrast as B A. C) Difference images taken to
create negative contrast as A B. pH values are labeled.









63 pp~m E ~



65 ppm 89 O *
O*


67 ppm O ~ 0 *



69 ppm O0 O0 *+


O *0

~O O *

Figure B -23. Variable pH phantom at 14.1 T. Presaturation power 128 CIT, presaturation offsets
from 65 71 ppm, presaturation time 4 s. A) Images taken with the presaturation
power applied on the positive side water at the shift listed. B) Images taken with the
presaturation power on the opposite side of water at the shift listed. C) Difference
images taken to create positive contrast as B A. C) Difference images taken to
create negative contrast as A B. pH values are labeled.









6 '


53 ppm 10 C





S55ppm O C0







57 ppm



59 ppm ~9100



Figure B -24. Variable pH phantom at 17.6 T. Presaturation power 32 CIT, presaturation offsets
from 53 59 ppm. A) Images taken with the presaturation power applied on the
positive side water at the shift listed. B) Images taken with the presaturation power
on the opposite side of water at the shift listed. C) Difference images taken to create
positive contrast as B A. C) Difference images taken to create negative contrast as
A B. pH values are labeled.











47 pp~m


51 ppm p





53 ppm p JI






55 ppm 3 r




Figure B -25. Variable pH phantom at 21.1 T. Presaturation offsets from 47 55 ppm.
A) Images taken with the presaturation power applied on the positive side water at the
shift listed. B) Images taken with the presaturation power on the opposite side of
water at the shift listed. C) Difference images taken to create positive contrast as B -
A. C) Difference images taken to create negative contrast as A- B. pH values are
labeled.


49 ppm




















59 ppm



















Figure B -26. Variable pH phantom at 21.1 T. Presaturation offsets from 57 73 ppm.
A) Images taken with the presaturation power applied on the positive side water at the
shift listed. B) Images taken with the presaturation power on the opposite side of
water at the shift listed. C) Difference images taken to create positive contrast as B -
A. C) Difference images taken to create negative contrast as A- B. pH values are
labeled.


57 ppm












47 ppm


50 ppm









56 ppm








Figure B -27. Variable pH with media phantom at 17.6 T. Presaturation offsets from 47 -
56 ppm. A) Images taken with the presaturation power applied on the positive side
water at the shift listed. B) Images taken with the presaturation power on the opposite
side of water at the shift listed. C) Difference images taken to create positive contrast
as B A. C) Difference images taken to create negative contrast as A B. pH
values are labeled as 1) pH 3, 2) pH- 7 in media and 3) pH 7.



















S0.8-



S0.6-




0.4








0
100




Figure B -28.


80 60 40 20 0 -20 -40 -60 -80 -100
Presaturat~ion Offset (ppm)


CEST spectrum of 10 mM Eu-2 at 14 OC.


I I I I


100 80 60 40 20 0 -20 -40 -60 -80
Presaturat~ion Offset (ppm)


Figure B-29. CEST spectrum of 10 mM Eu-2 at 20 OC.























S0.6-













1,


40 20 0 -20 -40 -60 -80
Presaturation Offset (ppm)


of 10 mM Eu-2 at 26 oC.


0.8E


0L
100


80 60 40 20 0 -20 -40 -60 -80 -100
Presaturation Offset (ppm)


Figure B-31. CEST spectrum of 10 mM Eu-2 at 320C.


















S0.8-




S0.6-




0.4-




S0.2-




0
100 80 60 40 20 0 -20 -40 -60 -80 -100
Presaturation Offset (ppm)


Figure B-32. CEST spectrum of 10 mM Eu-2 at 38 oC.


' 1


S0.8



S0.6



~30.4



O


80 60 40 20 0 -20 -40 -60 -80 -100

Presaturation Offset (ppm)


Figure B -33. CEST spectrum of 10 mM Eu-2 for one acquisition.














~1


S0.8-



S0.6-



~30.4-



2 0.2-

o

0
100 80 60 40 20 0 -20 -40 -60 -80 -100
Presaturation Offset (ppm)


Figure B -34. CEST spectrum of 10 mM Eu-2 for two acquisitions.


~30.4-





S0.


100 80 60 40 20 0 -20 -40 -60 -80 -100

Presaturation Offset (ppm)


Figure B-35. CEST spectrum of 10 mM Eu-2 for four acquisitions.



































ill


ill


100 80 60 40 20 0 -20 -40 -60 -80 -100

Presaturation Offset (ppm)



Figure B -3 6. CEST spectrum of 10 mM Eu-2 for eight acquisitions.




1-



0.8-



S0.6-








S0.




100 80 60 40 20 0 -20 -40 -60 -80 -100

Presaturation Offset (ppm)



Figure B -37. CEST spectrum of 10 mM Eu-2 for 32 acquisitions.
































80 60 40


-40 -60 -80 -100


20 0 -20
Presaturation Offset (ppm)


Figure B-3 8. CEST spectrum of 10 mM Eu-2 for 64 acquisitions.

70


60-

*128,2
50o 128,4
I I~ 128,4,n
40 64,2
a, I I /I 1 64,4
32,2
30 32,4

8,2
2 0C I 8,4



101


0 20 40 60 80 100 120
Concentration (mM)

Figure B -3 9. Presaturation power and duration effect on CNR. The legend indicates the
presaturation power (CIT) followed by the number of 1 ms presaturation pulses
(2x1000 or 4x1000); n indicates that 'derive gains' was set to off to test the power
calculation within the Paravision Program.









APPENDIX C
MATLAB SIMULATION

MATLAB Code Format

MATLAB code can be utilized by typing commands directly into the command box, or

through scripts, or .m files, which can be edited to run through a series of commands

automatically. M files are generally quicker, and can be saved and rerun. They may also refer to

other scripts. The MATLAB code used for the simulations was generally in the form of m files.

The code is listed as it appears in the m file, and if one script calls another, the additional scripts

are included after the first, and are separated by a solid line break.

Modified Bloch Equations

The Bloch equation (Eq. C-1) describes the relationship between the change in

magnetization (dM/dt), the static magnetic field(Be) and the starting magnetization (Mle)[147] (in

vector notation).


= 717, x Me, (C-1)
dt

This equation can be modified for chemical exchange to describe the resulting

magnetization[87,1 48,149].


d. = (0 mi)M ulk k .,1"o (C-2)
dt ; i '

dhlbound
= -(c- m~)M foundd k~ l/... *r + {o d (C-3)
dt

dhlbulk
= -(0 m)M k .,11 on 1Mi~zulk (C-4)
dt

dhlrbound
=- m)Mbound k bon v"~:*r d ... 1.1I (C-5)
dt










du A i '+ii ,n (C-6)
dt 7bulk +(r .* .*J 1


dh"dt TI hond 1bun -1


Bulk and bound are used to describe the properties of their respective pools of water, ao is the

frequency of RF irradiation, mi is the nutation rate of the RF irradiation, Cbulk refers to the

transition rate of the spins leaving the bulk pool and Cbound refers to the transition rate of the

spins leaving the bulk pool. The transition rates (Cbound or bulk) are equal to 1/Tzboundorbulk) where z

is the lifetime of a proton in the bound or bulk water pool. Mgbound and MObulk are proportional to

the number of protons in the bound or bulk water pool and refer to the thermal equilibrium z

magnetizations. Also for both bound and bulk water proton pools Equations 1-8 and 1-9 apply.


k,= -+ C (1-8)


k,= -+ C (1-9)


T1 and T2 refer to the spin-lattice and spin-spin relaxation times.

These equations (Eqn. 1-2-7), are the basis for the CESTFIT simulation[87]. This program

was modified to model and fit the simulations in this appendix. The following code shows the

modified Bloch equations (Eqn. 1-2 7) for simulating a two pool exchanging system.

ss2pool.m
function [Za] = ss2pool(params)
% global data
Za=zeros(1,241); %201
MO a = 1;
MOb = .0003636;
M~c = .000;
wl = 512 *2 *pi;
wa = -138 2 pi;
wb = 19760 2 pi;
%Tla = 1;









T2a = .2;
%Tlb = .1;
T2b = .1;
Cb=params(3 );
Cc=params(4);
Cab= Cb*"MOb/MOa;
Cac= Cc*M~c/MOa;
Ca=Cab+Cac;
kl a = (1/params(1)) + Ca;
k2a = (1/T2a) + Ca;
klb = (1/params(2)) + params(3);
k2b = (1/T2b) + params(3);
wctr=0 ;
for ww =-3 0000:25 0:30000 %-25 000
w-ww *2 *pi;
wctr = wctr+1;
Maxterm = [-k2a params(3) -(wa -w) 0 0 0];
Mbxterm = [Cab -k2b 0 -(wb -w)O 0 0 ;
Mayterm = [(wa w) 0 -k2a params(3) -wl 0 ];
Mbyterm = [0 (wb -w) Cab -k2b 0 -wl ];
Mazterm = [0 0 wl 0 -kla params(3)];
Mbzterm = [0 0 0 wl Cab -klb ];
blochmat = [Maxterm; Mbxterm; Mayterm; Mbyterm; Mazterm; Mbzterm];
outvec = [0 0 0 0 -MOa/params(1) -M~b/params(2) ]';
mags = blochmat\outvec;
Za(wctr) = mags(5)/M~a;
end

The Bloch Equations can be treated in a similar manner to include a third pool of

exchanging protons. The resulting equations lend themselves to a similar simulation as follows:

ss3.m
function [Za] = ss3(params)
% global data
Za= zeros(1,481);
MO a = 1;
MOb = .0003636;
M~c = .0007272;
wl = 512 *2 *pi;
wa = -138 2 pi;
wb = params(5) 2 pi;
wc = -133* 2 *pi;
T 1a = 2;
T2a = .88;
Tlb = .1;









T2b = .5;
Tlc= .003;
T2c = .003;
Cb=params(3 );
Cc=params(4);
Cab= Cb*"MOb/MOa;
Cac= Cc*M~c/MOa;
Ca=Cab+Cac;
kl a = (1/params(1)) + Ca;
k2a = (1/T2a) + Ca;
klb = (1/params(2)) + params(3);
k2b = (1/T2b) + params(3);
klc = (1/Tlc) + params(4);
k2c = (1/T2c) + params(4);
wctr=0 ;
for ww =-60000:250:60000
w-ww *2 *pi;
wctr = wctr+1;
Maxterm = [-k2a params(3) params(4) -(wa -w) 0 0 0 0 0];
Mbxterm = [Cab -k2b 0 0 -(wb -w) 0 0 0 0];
Mcxterm = [Cac 0 -k2c 0 0 -(we -w) 0 0 0];
Mayterm = [(wa w) 0 0 -k2a params(3) params(4) -wl 0 0];
Mbyterm = [0 (wb -w) 0 Cab -k2b 0 0 -wl 0];
Mcyterm = [0 0 (we -w) Cac 0 -k2c 0 0 -wl];
Mazterm = [0 0 0 wl 0 0 -kla params(3) params(4)];
Mbzterm = [0 0 0 0 wl 0 Cab -klb 0];
Mczterm = [0 0 0 0 0 wl Cac 0 -klc];
blochmat = [Maxterm; Mbxterm; Mcxterm; Mayterm; Mbyterm; Mcyterm; Mazterm;
Mbzterm; Mczterm];
outvec = [0 0 0 0 0 0 -MOa/params(1) -M~b/params(2) -M~c/Tlc]';
mags = blochmat\outvec;
Za(wctr) = mags(7)/M~a;
end


This code was manipulated in order to vary different variables and simulate the effect, and

the changes are shown in the remainder of the Appendix.

Variables Relaxation Time

The effect of varying both T1 and T2 relaxation times were simulated using the following code:

dot 1cestcomp. m
N= [-30000: 100:30000];
c = [.2 .2 3000 0];









M1=ss2ipool(c);
c = [.5 .2 3000 0];
M2=ss2ipool(c);
c = [1.5 .2 3000 0];
M3 =ss2ipool(c);
c = [3 .2 3000 0];
M4=ss2ipool(c);
c = [5 .2 3000 0];
M5=ss2ipool(c);
plot(N,M1)
set(gca, 'xdir','revers e')
hold all
pl ot(N,M2)
set(gca, 'xdir','revers e')
pl ot(N,M3)
set(gca, 'xdir','revers e')
pl ot(N,M4)
set(gca, 'xdir','revers e')
pl ot(N, M5)
set(gca, 'xdir','revers e')
legend('.2', '.5', '1.5', '3', '5');


ss2ipool.m
function [Za] = ss2ipool(params)
Za=zeros(1,201);
MO a = 1;
MOb = .0007272;
M~c = .000;
wl = 128 2 pi; % wl/2pi =
wa = -138 2 pi;
wb = 19760 2 pi;
%Tla = 1;
T2a = .1;
%Tlb = .1;
T2b = .08;
Cb=params(3 );
Cc=params(4);
Cab= Cb*"MOb/MOa;
Cac= Cc*M~c/MOa;
Ca=Cab+Cac;
kl a = (1/params(1)) + Ca;
k2a = (1/T2a) + Ca;
klb = (1/params(2)) + params(3);
k2b = (1/T2b) + params(3);
wctr=0 ;


B1 Hz









for ww =-3 0000: 100:3 0000
w-ww *2 *pi;
wctr = wctr+1;
Maxterm = [-k2a params(3) -(wa -w) 0 0 0];
Mbxterm = [Cab -k2b 0 -(wb -w)O 0 0 ;
May term = [(wa w) 0 -k2a params(3) -wl 0 ];
Mbyterm = [0 (wb -w) Cab -k2b 0 -wl ];
Mazterm = [0 0 wl 0 -kla params(3)];
Mbzterm = [0 0 0 wl Cab -klb ];
blochmat = [Maxterm; Mbxterm; May term; Mbyterm; Mazterm; Mbzterm];
outvec = [0 0 0 0 -MOa/params(1) -M~b/params(2) ]';
mags = blochmat\outvec;
Za(wctr) = mags(5)/M~a;
end

dot2cestcomp.m
N= [-30000: 100:30000];
d = [.005 .1 3000 0];
M1=ss2iipool(d);
d = [.01 .1 3000 0];
M2= ss2iip ool(d);
d = [.02 .1 3000 0];
M3= ss2iipool(d);
d = [.05 .1 3000 0];
M4= ss2iipool(d);
d = [.5 .1 3000 0];
M5= ss2iipool(d);
d = [1 .1 3000 0];
M6= ss2iipool(d);
pl ot(N, M1)
set(gca, 'xdir', 'reverse')
hold all
pl ot(N,M2)
set(gca, 'xdir','revers e')
pl ot(N,M3)
set(gca, 'xdir','revers e')
pl ot(N,M4)
set(gca, 'xdir','revers e')
pl ot(N, M5)
set(gca, 'xdir','revers e')
pl ot(N,M6)
set(gca, 'xdir','revers e')
legend('.005', '.01', '.02', '.05', '.5', '1');

ss2iipool. m
function [Za] = ss2iipool(params)









Za=zeros(1,201);
MO a = 1;
MOb = .0007272;
M~c = .000;
wl = 128 2 pi; % wl/2pi = B1 Hz
wa = -138 2 pi;
wb = 19760 2 pi;
T 1a = 2;
T2a = params(1);
%Tlb = .1;
T2b = .08;
Cb=params(3 );
Cc=params(4);
Cab= Cb*"MOb/MOa;
Cac= Cc*M~c/MOa;
Ca=Cab+Cac;
kla = (1/Tla)+ Ca;
k2a = (1/T2a) + Ca;
klb = (1/params(2)) + params(3);
k2b = (1/T2b) + params(3);
wctr=0 ;
for ww =-3 0000: 100:3 0000
w-ww *2 *pi;
wctr = wctr+1;
Maxterm = [-k2a params(3) -(wa -w) 0 0 0];
Mbxterm = [Cab -k2b 0 -(wb -w)O 0 0 ;
Mayterm = [(wa w) 0 -k2a params(3) -wl 0 ];
Mbyterm = [0 (wb -w) Cab -k2b 0 -wl ];
Mazterm = [0 0 wl 0 -kla params(3)];
Mbzterm = [0 0 0 wl Cab -klb ];
blochmat = [Maxterm; Mbxterm; Mayterm; Mbyterm; Mazterm; Mbzterm];
outvec = [0 0 0 0 -MOa/T la -M~b/params(2) ]';
mags = blochmat\outvec;
Za(wctr) = mags(5)/M~a;
end




Variable Chemical Shift Difference

The effect of changing chemical shift difference (Aco) was simulated using the following code.

doomenavary. m
%A small amount of different offsets
figure('name', 'varying omega b')









N= [-60000:300:60000];
e = [3 .5 3000 100 10000];
M1=ssthreepool(e);
e = [3 .5 3000 100 15000];
M2=ssthreepool(e);
e = [3 .5 3000 100 20000];
M3 =ssthreepool(e);
e = [3 .5 3000 100 25000];
M4=ssthreepool(e);
e = [3 .5 3000 100 30000];
M5=ssthreepool(e);
e = [3 .5 3000 100 35000];
M6=ssthreepool(e);
e = [3 .5 3000 100 40000];
M7=ssthreepool(e);
e = [3 .5 3000 100 45000];
M8=ssthreepool(e);
e = [3 .5 3000 100 50000];
M9=ssthreepool(e);
e = [3 .5 3000 100 55000];
M10=ssthreepool(e);
plot(N,M1)
hold all
pl ot(N,M2)
pl ot(N,M3)
pl ot(N,M4)
pl ot(N, M5)
pl ot(N,M6)
pl ot(N,M7)
pl ot(N,M8)
pl ot(N,M9)
pl ot(N,M 10)
set(gca, 'xdir','revers e')
legend('1 0000', '1 5000', '20000', '25000', '3 0000','3 5000','40000','45000','50000','55000');


% for close up:
figure('name', 'varying omega b')
N= [-60000:300:60000];
e= [3 .5 3000 100 500];
Ml=ssthreepool(e);
e = [3 .5 3000 100 1000];
M2= ssthr eepool(e);
e = [3 .5 3000 100 2000];
M3= ssthreepool(e);
e = [3 .5 3000 100 2500];









M4=ssthreepool(e);
e = [3 .5 3000 100 3000];
M5=ssthreepool(e);
e = [3 .5 3000 100 3500];
M6=ssthreepool(e);
e = [3 .5 3000 100 4000];
M7=ssthreepool(e);
e = [3 .5 3000 100 4500];
M8=ssthreepool(e);
e = [3 .5 3000 100 5000];
M9=ssthreepool(e);
e = [3 .5 3000 100 5500];
M10=ssthreepool(e);
plot(N,M1)
hold all
pl ot(N,M2)
pl ot(N,M3)
pl ot(N,M4)
pl ot(N, M5)
pl ot(N,M6)
pl ot(N,M7)
pl ot(N,M8)
pl ot(N,M9)
pl ot(N,M 10)
set(gca, 'xdir','revers e')
axis([-10000 10000 0 1.1])
legend('1000', '1500', '2000', '2500', '3000','3500','4000','4500','5000','5500')

ssthreepool.m
function [Za] = ssthreepool(params)
Za=zeros(1,101);
MO a = 1;
MOb = .0003636;
M~c = .0007272;
wl = 256*2*pi; % switch to params(6) 2 pi if power variable;
wa = -138 2 pi;
wb = params(5) 2 pi;
wc = -133* 2 *pi;
%Tla = 2;
T2a = .88;
%Tlb = .1;
T2b = .5;
T1c= .003;
T2c = .003;
Cb=params(3);
Cc=params(4);









Cab= Cb*"MOb/MOa;
Cac= Cc*M~c/MOa;
Ca=Cab+Cac;
kl a = (1/params(1)) + Ca;
k2a = (1/T2a) + Ca;
klb = (1/params(2)) + params(3);
k2b = (1/T2b) + params(3);
klc = (1/Tlc) + params(4);
k2c = (1/T2c) + params(4);
wctr=0 ;
for ww =-60000:300:60000
w-ww *2 *pi;
wctr = wctr+1;
Maxterm = [-k2a params(3) params(4) -(wa -w) 0 0 0 0 0];
Mbxterm = [Cab -k2b 0 0 -(wb -w) 0 0 0 0];
Mcxterm = [Cac 0 -k2c 0 0 -(we -w) 0 0 0];
Mayterm = [(wa w) 0 0 -k2a params(3) params(4) -wl 0 0];
Mbyterm = [0 (wb -w) 0 Cab -k2b 0 0 -wl 0];
Mcyterm = [0 0 (we -w) Cac 0 -k2c 0 0 -wl];
Mazterm = [0 0 0 wl 0 0 -kla params(3) params(4)];
Mbzterm = [0 0 0 0 wl 0 Cab -klb 0];
Mczterm = [0 0 0 0 0 wl Cac 0 -klc];
blochmat = [Maxterm; Mbxterm; Mcxterm; Mayterm; Mbyterm; Mcyterm; Mazterm;
Mbzterm; Mczterm];
outvec = [0 0 0 0 0 0 -MOa/params(1) -M~b/params(2) -M~c/Tlc]';
mags = blochmat\outvec;
Za(wctr) = mags(7)/M~a;
end


Variable Chemical Exchange

The effect of changing chemical exchange rates, effectively simulating various

PARACEST compounds, was simulated by changing Cb and Ca using the following code.

varvCb.m
N= [-100:1:100];
paramss = [2 .1 200 0];
M1 =ss2pool(paramss);
paramss = [2 .1 400 0];
M2=ss2pool(paramss);
paramss = [2 .1 1000 0];
M3=ss2pool(paramss);
paramss = [2 .1 3000 0];
M4= ss2pool(params s);
paramss = [2 .1 8000 0];









M5=ss2pool(paramss);
paramss = [2 .1 10000 0];
M6=ss2pool(paramss);
plot(N,M1)
hold all
pl ot(N,M2)
pl ot(N,M3)
pl ot(N,M4)
pl ot(N, M5)
pl ot(N,M6)
set(gca, 'xdir','revers e')
legend('200', '400', '1000', '3000', '8000', '10000');

ss2pool .m
function [Za] = ss2pool(params)
Za=zeros(1,201);
MO a = 1;
MOb = .0003636;
M~c = .000;
wl = 512 *2 *pi;
wa = -138 2 pi;
wb = 19760 2 pi;
%Tla = 1;
T2a = .2;
%Tlb = .1;
T2b = .1;
Cb=params(3 );
Cc=param s(4);
Cab= Cb*"MOb/MOa;
Cac= Cc*M~c/MOa;
Ca= Cab+Cac;
kl a = (1/params(1)) + Ca;
k2a = (1/T2a) + Ca;
klb = (1/params(2)) + params(3);
k2b = (1/T2b) + params(3);
wctr=0 ;
for ww =-3 0000:300:3 0000
w-ww *2 *pi;
wctr = wctr+1;
Maxterm = [-k2a params(3) -(wa -w) 0 0 0];
Mbxterm = [Cab -k2b 0 -(wb -w)O 0 0 ;
Mayterm = [(wa w) 0 -k2a params(3) -wl 0 ];
Mbyterm = [0 (wb -w) Cab -k2b 0 -wl ];
Mazterm = [0 0 wl 0 -kla params(3)];
Mbzterm = [0 0 0 wl Cab -klb ];
blochmat = [Maxterm; Mbxterm; Mayterm; Mbyterm; Mazterm; Mbzterm];









outvec = [0 0 0 0 -MOa/params(1) -M~b/params(2) ]';
mags = blochmat\outvec;
Za(wctr) = mags(5)/M~a;
end

Variable Presaturation Power

The presaturation power was increased from 200 to 12000 Hz using the following code.

dopowervary.m
%/varying power
figure('name', 'varying power')
hold all
n=(-100:0.5:100);
for h= [200:200:600]
e= [2 .1 3000 100 35000 h];
m= ssthreepool(e);
pl ot(n, m)
end
for h= [2000:2000:12000]
ctr= ctr+1 ;
e= [2 .1 3000 100 35000 h];
m= ssthreepool(e);
pl ot(n, m)
end
set(gca, 'xdir', 'reverse')
legend ('200','400','600','2000','4000','6000','80''0000','l 2000','Location','SouthEast')

ssthreepool.m
function [Za] = ssthreepool(params)
%params6 = power
Za=zeros(1,101);
MO a = 1;
MOb = .0003636;
M~c = .0007272;
wl = params(6) 2 pi; %switch to 512 if power not variable
wa = -138 2 pi;
wb = params(5) 2 pi;
wc = -133* 2 *pi;
%Tla = 2;
T2a = .88;
%Tlb = .1;
T2b = .5;
Tlc= .003;
T2c = .003;
Cb=params(3);
Cc=params(4);









Cab= Cb*"MOb/MOa;
Cac= C c*Mc/MOa;
Ca= Cab+Cac;
kla = (1/params(1)) + Ca;
k2a = (1/T2a) + Ca;
klb = (1/params(2)) + params(3);
k2b = (1/T2b) + params(3);
klc = (1/Tlc) + params(4);
k2c = (1/T2c) + params(4);
wctr=0 ;
for ww =-60000:300:60000
w-ww *2 *pi;
wctr = wctr+1;
Maxterm = [-k2a params(3) params(4) -(wa -w) 0 0 0 0 0];
Mbxterm = [Cab -k2b 0 0 -(wb -w) 0 0 0 0];
Mcxterm = [Cac 0 -k2c 0 0 -(we -w) 0 0 0];
Mayterm = [(wa w) 0 0 -k2a params(3) params(4) -wl 0 0];
Mbyterm = [0 (wb -w) 0 Cab -k2b 0 0 -wl 0];
Mcyterm = [0 0 (we -w) Cac 0 -k2c 0 0 -wl];
Mazterm = [0 0 0 wl 0 0 -kla params(3) params(4)];
Mbzterm = [0 0 0 0 wl 0 Cab -klb 0];
Mczterm = [0 0 0 0 0 wl Cac 0 -klc];
blochmat = [Maxterm; Mbxterm; Mcxterm; Mayterm; Mbyterm; Mcyterm; Mazterm;
Mbzterm; Mczterm];
outvec = [0 0 0 0 0 0 -MOa/params(1) -M~b/params(2) -M~c/Tlc]';
mags = blochmat\outvec;
Za(wctr) = mags(7)/M~a;
end

Variable Concentration Simulation
For the simulation, the concentration was by changing the ratio of Mobound and Mgbulk

MObulk WaS kept at 1, while MObound WaS increased incrementally from 0.0001 to 0.001 using the

following code.

doconcvary.m
fi gure('name','varying concentrate on')
title('Vrarying concentration, concentration increase as the CESTffect gets bigger ')
hold all
n= [-100:1:100];
for h= [.0001:.0001:.001]i
ctr- ctr+1 ;
e= [2 .1 30000 15000 31800 800 h .00072];
m= ssthreepoolconc(e);
pl ot(n, m)









end
set(gca, 'xdir','revers e')
legend
('0.0001 ','0.0002','0.0003','0.0004','0.0005','0.00''.07,000''.09,001','Location','So
uthEast')

ssthreepoolconc.m
function [Za] = ssthreepoolconc(params)
% global data
%paramsl= tla not variable
%params2=tlb
%params3= Cb
%params4= Cc
%params5 = bound water shift
%/Params6=power wl
%params7= MOb
%params8= M~c
Za= zeros(1,101);
MO a = 1;
MOb = params(7);
M~c = params(8);
wl = params(6) 2 pi; %switch to 512 if power not variable
wa = -138 2 pi;
wb = params(5) 2 pi;
wc = -133* 2 *pi;
%Tla = 2;
T2a = .88;
%Tlb = .1;
T2b = .5;
T1c= .003;
T2c = .003;
Cb=params(3);
Cc=params(4);
Cab= Cb*"MOb/MOa;
Cac= Cc*M~c/MOa;
Ca=Cab+Cac;
kl a = (1/params(1)) + Ca;
k2a = (1/T2a) + Ca;
klb = (1/params(2)) + params(3);
k2b = (1/T2b) + params(3);
klc = (1/Tlc) + params(4);
k2c = (1/T2c) + params(4);
wctr=0 ;
for ww =-60000:600:60000
w-ww *2 *pi;
wctr = wctr+1;









Maxterm = [-k2a params(3) params(4) -(wa -w) 0 0 0 0 0];
Mbxterm = [Cab -k2b 0 0 -(wb -w) 0 0 0 0];
Mcxterm = [Cac 0 -k2c 0 0 -(we -w) 0 0 0];
Mayterm = [(wa w) 0 0 -k2a params(3) params(4) -wl 0 0];
Mbyterm = [0 (wb -w) 0 Cab -k2b 0 0 -wl 0];
Mcyterm = [0 0 (we -w) Cac 0 -k2c 0 0 -wl];
Mazterm = [0 0 0 wl 0 0 -kla params(3) params(4)];
Mbzterm = [0 0 0 0 wl 0 Cab -klb 0];
Mczterm = [0 0 0 0 0 wl Cac 0 -klc];
blochmat = [Maxterm; Mbxterm; Mcxterm; Mayterm; Mbyterm; Mcyterm; Mazterm;
Mbzterm; Mczterm];
outvec = [0 0 0 0 0 0 -MOa/params(1) -M~b/params(2) -M~c/Tlc]';
mags = blochmat\outvec;
Za(wctr) = mags(7)/M~a;
end

Temperature Fit MATLAB Code

Data was acquired on the 14.1 T magnetic of a 10 mM solution of Eu-2 at five different

temperatures. That data was input in MATLAB, and fit to the modified Bloch Equations using

fminsearch.m (an unconstrained nonlinear minimization (Nelder-Mead) program) which is

included in the MATLAB software. This function was used inside of the following code.

dotempl4 38.m
close all
clear all
load m050508tcorr %pre processed data from the 14.1 T
%define variables for fit for T=14
xdata=(al4(:,1));
ydatal14=(al4(:,2));
ydata=ydatal14;
%/For T=14
T1=3.1;
%abc=fmincon('expfitcestl 20808',[2.6 3000 200 1000 59],[],[],[],[],[0 0 0 0 45],[10 10000
10000 3000 75],[],[],xdata,T1, ydata);
abc=fminsearch('expfitcestl 20808',[3.1 3 000 200 100 55S],[],xdata,T1 ,ydata);
%/to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1, ydata);
%plot with points and fit
figure('name','Temperature fit')
hold all
plot(xdata,ydata, 'bo');
plot(xdata,y_fit,'b-');
set(gca, 'xdir','revers e')









axis([xdata(xval) xdata(1) 0 1.1])
abcl4=abc;
yfitl4=y_fit;
errl4=err;
R2 14=R2;
%14 values: [3.0687,20778 .0708,6935 .4489,860.8371 ,59.7442]

%define variables for fit for T=20
x dat a=(a 2 0(:,1));
ydata20=(a20(:,2));
ydata=ydata20;
T1=3.5;
abc=fminsearch('expfitcestl20808',[3 .0687 20778.0708 6935.4489 860.8371
59.7442],[],xdata,T 1,ydata);
abc=fminsearch('expfitcestl 20808',abc, [],xdata,T 1,ydata);
%/to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1, ydata);
%plot with points and fit
plot(xdata,ydata, 'go');
pl ot(xdata, y_fit,'g-');
set(gca, 'xdir','revers e')
axis([xdata(xval) xdata(1) 0 1.1])
%legend ('20 data','20 fit','Location','SouthEast')
abc20=abc;
yfit20=y_fit;
err20=err;
R2 20=R2;

%define variables for fit for T=26
xdata=(a26(:,1));
ydata26=(a26(:,2));
ydata=ydata26;
T1=4.0;
abc=fminsearch('expfitcestl20808',[3 .0687 20778.0708 6935.4489 860.8371
59.7442],[],xdata,T 1,ydata);
abc=fminsearch('expfitcestl 20808',abc, [],xdata,T 1,ydata);
%/to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1, ydata);
%plot with points and fit
plot(xdata,ydata,'ro');
pl ot(xdata, y_fit,'r-');
set(gca, 'xdir','revers e')
axis([xdata(xval) xdata(1) 0 1.1])
%legend ('26 data','26 fit','Location','SouthEast')
abc26=abc;
yfit26=y_fit;









err26=err;
R2 26=R2;

%define variables for fit for T=32
xdata=(a32(:,1));
ydata3 2=(a3 2(:,2));
ydata=ydata3 2;
T1=4.4;
abc=fminsearch('expfitcestl20808',[3 .0687 20778.0708 6935.4489 860.8371
59.7442],[],xdata,T 1,ydata);
abc=fminsearch('expfitcestl 20808',abc, [],xdata,T 1,ydata);
%/to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1, ydata);
%plot with points and fit
plot(xdata,ydata, 'co');
plot(xdata,y_fit,'c-');
set(gca, 'xdir','revers e')
axis([xdata(xval) xdata(1) 0 1.1])
%legend ('32 data','32 fit','Location','SouthEast')
abc3 2=ab c;
yfit32=y_fit;
err32=err;
R2 32=R2;

%define variables for fit for T=3 8
xdata=(a3 8(:,1));
ydata3 8=(a3 8(:,2));
ydata=ydata3 8;
%/F or T=3 8
T1=5.1;
abc=fminsearch('expfitcestl20808',[3 .0687 20778.0708 6935.4489 860.8371
59.7442],[],xdata,T 1,ydata);
abc=fminsearch('expfitcestl 20808',abc, [],xdata,T 1,ydata);
%/to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1, ydata);
%plot with points and fit
plot(xdata,ydata, 'mo');
pl ot(xdata,y_fit, 'm-');
set(gca, 'xdir','revers e')
axis([xdata(xval) xdata(1) 0 1.1])
legend ('14 data', '14 fit','20 data','20 fit', '26 data', '26 fit', '32 data', '32 fit', '38 data','38
fit', 'Location','SouthEast')
abc3 8=abc;
yfit38=y_fit;
err3 8=err;
R2 38=R2;









title(['l4degC. Rsquared value is ',num2str(R2_14), 20degC. Rsquared value is ',
num2str(R2_20) 26degC. Rsquared value is ', num2str(R2_26) 32degC. Rsquared value is ',
num2str(R2_32) 3 8degC. Rsquared value is ', num2str(R2_3 8)])

expfitcestl20808.m
function [ err, R2 ] = expfitcestl20808(args, xdata, Tla, ydata)
a= arg s(1);
b=args(2);
c=arg s(3 );
d=args(4);
e=args(5);
A= [abced e];
y_fit=fit(A,xdata,T la); %was ssthreepoolfit, but changed to fit for temp data)
err=norm(ydata-y_fit);
fitval = sum((y_fit-ydata).^'2);
ybar=mean(ydata);
SStot = sum((ydata-ybar).^'2);
R2=1-fitval/SStot;
assi gnin('ba se','y_fit', y_fit);

fit.m
function [Za] = fit(params,xdata,T 1) %was ssthreepool
%params = tla not variable
%paramsl=tlb
%params2= Cb
%params3= Cc
%params4 = wl
%Params5=bound water shift in ppm
%%%%params6= MOb
%%%%params7= MOc
Za= (xdata(:));
Za(:)=0;
num x= (xdata(:));
numx(:)= 1;
xval= sum(numx(:));
as signin('ba se','xval', xval);
MOa = 1;
MOb = .0003636;
M~c = .0007272;
wl = params(4) 2 pi;
wa = 4.7*600*2*pi;
wb = params(5) 600 2 pi;
wc = -133* 2 *pi;
T 1a = T 1;
T2a = .88;
%Tlb = .1;









T2b = .5;
Tlc= .003;
T2c = .003;
Cb=params(2);
Cc=params(3);
Cab= Cb*"MOb/MOa;
Cac= Cc*M~c/MOa;
Ca=Cab+Cac;
kla = (1/Tla)+ Ca;
k2a = (1/T2a) + Ca;
klb = (1/params(1)) + params(2);
k2b = (1/T2b) + params(2);
klc = (1/Tlc) + params(3);
k2c = (1/T2c) + params(3);
wctr=0 ;
%for 101.7ppm to -100.3 in -2 steps at 600MHz
for i=(1:1:xval)
ww = 600*xdata(i);
w-ww *2 *pi;
wctr = wctr+1;
Maxterm = [-k2a params(2) params(3) -(wa -w) 0 0 0 0 0];
Mbxterm = [Cab -k2b 0 0 -(wb -w) 0 0 0 0];
Mcxterm = [Cac 0 -k2c 0 0 -(we -w) 0 0 0];
Mayterm = [(wa w) 0 0 -k2a params(2) params(3) -wl 0 0];
Mbyterm = [0 (wb -w) 0 Cab -k2b 0 0 -wl 0];
Mcyterm = [0 0 (we -w) Cac 0 -k2c 0 0 -wl];
Mazterm = [0 0 0 wl 0 0 -kla params(2) params(3)];
Mbzterm = [0 0 0 0 wl 0 Cab -klb 0];
Mczterm = [0 0 0 0 0 wl Cac 0 -klc];
blochmat = [Maxterm; Mbxterm; Mcxterm; Mayterm; Mbyterm; Mcyterm; Mazterm;
Mbzterm; Mczterm];
outvec = [0 0 0 0 0 0 -MOa/params(1) -MOb/params(1) -M~c/Tlc]';
mags = blochmat\outvec;
Za(wctr) = mags(7)/M~a;

Number of Acquisitions Fit MATLAB Code

Data were acquired on the 14.1 T magnetic of a 10 mM solution of Eu-2 with 1, 2, 4, 8, 32

and 64 scans. The data was input in MATLAB, and fit to the modified Bloch Equations. The

data and the fits for each individual set are shown in Figures C-1, C-2, C-3, C-4, C-5, C-6 for 1,

2, 4, 8, 32 and 64 scans respectively. The fits and the figures were created using the following

code.









NSO50508.m
%create NS spectra graphs and recall processed data
clear all
load m050508 NScorr2
figure('name','Z spec increasing NS study')
hold all
plot(al(:,1l),al (:,2),'r-','linewidth',2);
plot(a2(:,1l),a2(:,2),'b-','linewidth',2);
plot(a4(:,1l),a4(:,2),'g-','linewidth',2);
plot(a8(:,1l),a8(:,2),'c-','linewidth',2);
pl ot(a3 2(:,1i),a3 2(:,2),'m-','linewi dth',2);
plot(a64(:,1),a64(: ,2), 'y-', 'linewidth',2);
set(gca, 'xdir', 'reverse')
axis([-101.7 100.3 0 1.05])
legend ('1 ','2','4','8','32','64','Location','East)
%and for the close up
figure('name','Close up NS study')
hold all
pl ot(al1(22:27,1),al(22: 27,2))
plot(a2(22:27,1 ),a2(22 :27,2))
plot(a4(22:27,1 ),a4(22 :27,2))
plot(a8(22:27,1 ),a8(22 :27,2))
plot(a32(22:27, 1),a32(22:27,2))
plot(a64(22:27, 1),a64(22:27,2))
axis([49.7 59.7 0.54 0.67])
legend ('1 ','2','4','8','32','64','Location','South~s'
%All expts performed at 26deg C so:
T1=4.0;

%define variables for fit for NS=1
xdata= (al (:, 1));
y data= (a 1(:, 2));
ydata= ydatal1;
%For NS=1
%abc=fmincon('expfitcestl20808',[2.6 3000 200 1000 59],[],[],[],[],[0 0 0 0 45],[10 10000
10000 3000 75],[],[],xdata,T1, ydata);
abc=fminsearch('expfitcestl20808',[3.9561 24930.5981 5903.144 822.1845
56.3 058],[],xdata,T 1,ydata);
%to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1, ydata);
%plot with points and fit
figure('name','NTS=1 fit')
title(['NS=1. Rsquared value is ',num2str(R2)])
hold all
plot(xdata,ydata,'ro');
pl ot(xdata, y_fit, 'r-');









set(gca, 'xdir','revers e')
axis([xdata(xval) xdata(1) 0 1.05])
legend (N\S 1 data',S 1\ Sfit','Location',' SouthEast')
abcl=abc;
yfitl =y_fit;
errl=err;
R2 1=R2;
% values: [3.9561,24930.5981 ,5903.144,822.1 845,56.3058;]

%define variables for fit for NS=2
x data= (a 2(:, 1));
ydata2= (a2(:,2));
ydata=ydata2;
%For NS=2
abc=fminsearch('expfitcestl20808',[3.9561 24930.5981 5903.144 822.1845
56.3058],[],xdata,T 1,ydata);
%/to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1,ydata);
%plot with points and fit
figure('name','NS=2 fit')
titl e([ NTS=2. Rsquared value i s ',num2 str(R2)])
hold all
plot(xdata,ydata, 'bo');
plot(xdata,y_fit,'b-');
set(gca, 'xdir','revers e')
axis([xdata(xval) xdata(1) 0 1.05])
legend (N\S 2 data',N\S 2 fit','Location',' SouthEast')
ab c2=abc;
yfit2=y_fit;
err2=err;
R2 2= R2;

%define variables for fit for NS=4
xdata=(a4(:,1));
ydata4=(a4(:,2));
ydata=ydata4;
%For NS=4
abc=fminsearch('expfitcestl20808',[3 .9561 24930.5981 5903.144 822.1845
56.3058],[],xdata,T 1,ydata);
%/to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1,ydata);
%plot with points and fit
figure('name','NS=4 fit')
titl e([N\TS=4. Rsquared value i s ',num2 str(R2)])
hold all
plot(xdata,ydata, 'go');









pl ot(xdata, y_fit,'g-');
set(gca, 'xdir','revers e')
axis([xdata(xval) xdata(1) 0 1.05])
legend (N\S 4 data',N\S 4 fit','Location',' SouthEast')
abc4=abc;
yfit4=y_fit;
err4=err;
R2 4=R2;

%define variables for fit for NS=8
xdata= (a8(:,1));
ydata8=(a8(:,2));
ydata= ydata8;
%/F or N S= 8
abc= fminsearch('expfitcestl20808',[3 .9561 24930.5981 5903.144 822.1845
56.3058],[],xdata,T 1,ydata);
%/to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1,ydata);
%plot with points and fit
figure('name','NS= 8 fit')
title([N\S=8. Rsquared value i s ',num2str(R2)])
hold all
plot(xdata,ydata, 'co');
plot(xdata,y_fit,'c-');
set(gca, 'xdir','revers e')
axis([xdata(xval) xdata(1) 0 1.05])
legend (N\S 8 data',N\S 8 fit','Location',' SouthEast')
abc8=abc;
yfit8=y_fit;
err8= err;
R2 8=R2;

%define variables for fit for NS=32
xdata=(a32(:,1));
ydata3 2=(a3 2(:,2));
ydata=ydata3 2;
%/F or N S=3 2
abc= fminsearch('expfitcestl20808',[3 .9561 24930.5981 5903.144 822.1845
56.3058],[],xdata,T 1,ydata);
%/to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1,ydata);
%plot with points and fit
figure('name','NS=32 fit')
title([N\S=32. Rsquared value is ',num2str(R2)])
hold all
plot(xdata,ydata, 'mo');










plot(xdata,y_fit,'m-');~
set(gca, 'xdir','reverse')
axis([xdata(xval) xdata(1) 0 1.05])
legend (N\S 32 data','NS 32 Sit','Location','SouthEast')
abc3 2=ab c;
yfit32=y_fit;
err32=err;
R2 32=R2;

%define variables for fit for NS=64
xdata=(a64(:,1));
ydata64=(a64(:,2));
ydata=ydata64 ;
%/F or N S= 64
abc=fminsearch('expfitcestl20808',[3 .9561 24930.5981 5903.144 822.1845
56.3058],[],xdata,T 1,ydata);
%/to get error
[err,R2]=expfitcestl 20808(abc,xdata,T 1,ydata);
%plot with points and fit
figure('name','NS= 64 fit')
title([N\S=64. Rsquared value is ',num2str(R2)])
hold all
plot(xdata,ydata,'yo');
pl ot(xdata, y_fit,'y-');
set(gca, 'xdir','revers e')
axis([xdata(xval) xdata(1) 0 1.05])
legend (N\S 64 data','NS 64 Sit','Location','SouthEast')
abc64=abc;
yfit64=y_fit;
err64=err;
R2 64=R2;

figure('name','number of scans fit data')
title('Number of scans data and fit')
hold all
plot(al(:,1l),al (:,2),'ro')%,'linewidth', 1.5)
plot(al(:,1l),yfitl,'r-')
plot(a2(:,1l),a2(:,2),'bo')%,'linewidth', 1.5)
plot(a2(:, 1),yfit2,'b-')
plot(a4(:,1l),a4(:,2),'go')%,'linewidth', 1.5)
plot(a4(:,1 ),yfit4,'g-')
plot(a8(:,1 ),a8(:,2),'co')%,'linewidth', 1.5)
plot(a8(:,1 ),yfit8,'c-')
plot(a32(:,1l),a32(:,2),'mo')%,'linewidth', 1.5)
plot(a32(:, 1),yfit32,'m-')
plot(a64(:,1l),a64(:,2),'yo')%,'linewidth', 1.5)










pl ot(a64(:,1),yfit64, 'y-')
s et(g ca, 'xdi r', 'rever se')
axis([-100.3 101.7 0 1.1])
legend ('1','1 fit','2','2 fit','4','4 fit','8','8 fit','32','32 fit','64','64 fit','Location','SouthEast')













S16


0.8 o




S0.4




-3 0.2

O

100 80 60 40 20 0
Presaturation Offset


Figure C-1. CEST spectrum and fit for one scan. R2



S16


0.8


S0.6


r30.4


-3 0.2


20 -40 -60 -80 -100
(ppm)


0.995.


100 80 60 40 20 0 -20 -40 -60 -80 -100
Presaturation Offset (ppm)


Figure C-2. CEST spectrum and fit for two scans. R2


0.989.





























100 80 60 40


20 0 -20 -40 -60 -80 -100
Presaturation Offset (ppm)


Figure C-3. CEST spectrum and fit for four scans. R2


0.990.


0.8




S0.4


23 0.2


S0.

100


80 60 40 20 0 -20
Presaturation Offset (ppm)


-40 -60 -80 -100


Figure C-4. CEST spectrum and fit for eight scans. R2 = 0.991.


NS data
NS 4 fit


NS data
NS 8 ft











S16


0.8




9 0.4




W 0.2

O

100 80 60 40 20 0
Presaturation Offset


Figure C-5. CEST spectrum and fit for 32 scans. R2








0.8




9 0.4


1 0.2

S0.


20 -40 -60 -80 -100
(ppm)


0.985.


O NS 64 data
-NS 64 fit


100 80 60 40 20 0 -20 -40 -60 -80 -100
Presaturaion Offset (ppm)


Figure C-6. CEST spectrum and fit for 64 scans. R2 = 0.989.










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205









BIOGRAPHICAL SKETCH

Heather Comnell graduated from Atlantic Community High School in the International

Baccalaureate Program in 1999, and earned the Bright Futures and National Merit Scholarships.

Heather Cornnell earned a Bachelor of Science in chemistry and a Bachelor of Science in

chemical engineering from the University of Florida in 2004. While in graduate school she

earned a UF Alumni fellowship and several scholarships and stipends including the Tillie, Jennie

and Harold Schwartz Foundation Scholarship in Biomedical Engineering and the ISMRM-

ESMRMB Educational Stipend. She worked as a teaching assistant for several graduate level

courses in the Department of Biomedical Engineering and also as a teaching assistant for

physical exam courses at the Harrell Professional Development Center in the College of

Medicine at UF. Through her course of study she presented her work on contrast agents at

several conferences, and gave invited oral presentations including her talk Characterization of

Paramagnetic Lanthanide lon complexes as MRI Contrast Agents as a Function of Magnetic

Field Strength" which was given at the Joint Annual International Society for Magnetic

Resonance in Medicine and European Society for Magnetic Resonance in Medicine and Biology

conference in Berlin, Germany in May 2007. She earned a Master of Engineering in biomedical

engineering from the University of Florida in 2007, and a Doctor of Philosophy in biomedical

engineering from the University of Florida in 2009.





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dMz bulkdt M bulkT bulk k bulkMz bulk CboundMz boundMy bulk dMz bounddt M boundT bound k boundMz bound CbulkMz bulkMy bound k T C k T C

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Journal of Magnetic Resonance Imaging: JMRI Chemical Society Reviews Applied Radiology, the Journal of Practical Me dical Imaging and Management U.S. Food and Drug Administration U.S. Food and Drug Administration Contrast Media & Molecular Imaging European Journal of Radiology Radiology Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology Radiology Der Radiologe Eur Heart J

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PAGE 202

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PAGE 203

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PAGE 204

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PAGE 205

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