Evaluation of Hahn, CPMG, and Combined Spin
Echo Analysis at 8 Tesla MRI
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree
Doctor of Philosophy in the Graduate School of The Ohio State University
By
Chastity Diane Shaffer Whitaker, M.S.
* * * *
THE OHIO STATE UNIVERSITY
2004 APPROVED BY DISSERTATION COMMITTEE:
Professor Petra Schmalbrock, Advisor
Professor Douglas Scharre
Professor David Beversdorf Advisor
Professor Philip Smith Biophysics Program Copyright by
Chastity DS Whitaker
2004 ABSTRACT
Magnetic Resonance Imaging is a non-invasive technique that has been developed for its excellent depiction of soft tissue contrasts. Instruments capable of ultra-high field strengths, ≥7 Tesla, were recently engineered and have resulted in higher signal-to-noise and higher resolution images.
Total iron content within the brain is not homogenous. Age and a number of neurological conditions (such as Alzheimer’s, Parkinson’s, Multiple Sclerosis, etc.) may influence the distribution. The ability to non-invasively detect the distribution of iron may be useful for diagnosis and for assessment of the effectiveness of treatments. Iron is also capable of indirectly influencing the signal in an MRI study by dephasing spins. This may then lead to an increase in relaxation rates. However, the standard transverse relaxation rate is not the most sensitive measure of iron content and other methods, such as gradient echo, are plagued with artifacts (example: air-tissue susceptibility signal loss and distortion).
To increase the sensitivity of high field spin echo analysis to the distribution of iron content, we have analyzed Hahn and CPMG spin echoes with the Carr and Purcell spin echo signal equation. This resulted in an intrinsic T2
ii that describes the relaxation of the tissues and a susceptibility and diffusion dependent term (γ2G2D) that may be correlated with paramagnetic content.
Experimentation includes the accuracy of Hahn, CPMG, and Combined
Spin echo calculations in an inhomogeneous B1 field and the influence of macromolecular content on T2 and γ2G2D. We also examined Alzheimer’s disease using in vitro, in situ, and in vivo subjects with these techniques.
Independent correlation to iron content is accomplished with ICP-Mass
Spectroscopy.
We found that reliable relaxation measurements are found within image regions of ±20° of a nominal 90° flip angle. The paramagnetic content is linear with intrinsic T2 values quadratic with γ2G2D. Gradient and diffusion effects are not independent and a static regime model may be appropriate for describing
γ2G2D. In vitro imaging of brain material results in similar relaxation rates to in situ imaging if the tissue is imaged immediately following autopsy.
iii Dedicated to
Steve & Beverly Shaffer and Matthew & Caleb Whitaker
iv ACKNOWLEDGMENTS
Be forewarned - this is going to take a while.
First, of course, I would like to thank my parents, Steve and Beverly
Shaffer. Without them, I would never have accomplished all that I have. Their belief in me, that I can do anything that I put my mind to, has pushed me in continuing in those rough times when quitting sounded so easy. Their faith in
God has set an example to me of how believing in him to provide all that you need. Also to my brother, Steven. His youthful point-of-view always reminds me that life is suppose to be fun and that problems always seem bigger than they really are. Steven has a passion for life and friends that I admire so much.
Next there are my in-laws. Charles and Shirley have always included me and made me feel as though I was naturally part of the family. There was never any doubt on their part as to what my future holds, and that I would do exactly what I had set my mind to. My sister-in-law, Amy, whose energy is beyond anything I have ever seen. She exhausts me just watching her bounce and chatter about. That energy will be a great benefit as she begins to shape young minds. Finally, Chad & Chrissy, for their dedication to their profession of saving
v lives and always providing helpful second opinions for medical concerns. You two will make great parents when you decide the time is right.
To my advisor, Petra Schmalbrock, who had taken me in when I was academically ‘homeless’ and in desperate need of finding someone to be my mentor. You have taken a chance on me and I hope that I have not disappointed
(besides the conservative view points and desire to be one of those awful lawyer types). Just remember to have faith in people.
My partner in crime and statistical crutch – Martina Pavlicova. You have an amazing ability focus on the final goal (not the current crisis) and have an incredible intelligence. You will be extremely successful, I’m quite sure.
To the department of Radiology, the staff (Heidi, Gail, Bill, Lindsey) and physicians (Chakeres, Slone, Christoforidis, Borekas) who have always provided help and information when needed, I’m eternally grateful.
Drs. Scharre and Beversdorf – the two of you have always been there for my work. Even when things were not going as well as we anticipated, you stood by my work and kept offering to help in any way possible. I truly appreciated all that you have done.
But who can forget some of my most ‘favorite’ people ever? Trong-kha
Truong, Chad Mitchell, Ryan Gilbert, Johannes Heverhagen, and Jim Ibinson.
We have suffered together, cried together (well, you have listened to me cry), cursed, laughed, etc, etc. Who can count all of the fru-fru (or cheap-a-chino) that we have drank, chocolate that Chad has (I mean we have) eaten, or number of times we have beat the RF lab printer? Your friendships will always be one of
vi my fondest memories of the graduate school experience and I can only pray that you all have the success and happiness that you deserve. TKT – just remember to speak out more. I know it is hard, I deal with it everyday. Chad, you have such a strong faith and conviction. You are an inspiration to maintaining your beliefs and strong sense of family. ‘The Doctor Is In…5¢ please,’ should be on the couch near Ryan’s office. Or perhaps, ‘Will fix Ebay lemons.’ Thanks for your optomism. I will only say thanks to Johannes so that there is not further suspicion to contribute to TKTs MS Windows desktop. Finally, Jim (and your
‘big, happy family’). The four of you have given Matt, Caleb, and I so much help and advice throughout the last year. I miss living so close to you guys!! We will eventually call a truce on the competition of number of degrees…maybe.
Then there are those silent partners that we already miss so dearly. Drs
Algaze and Ibrahim have shown that attaining the PhD is possible if you can endure. Hendrik and Klauss were always available for a run to the Café Oasis, happy hour at Don Pablos, or just a chat. I miss you all.
The support of the Biophysics program was phenomenal. Susan Hauser is so dedicated to the success of every student, and we take her for granted every day. Thanks so much for all for always dropping your work just to find that
999 number for me the day that my schedule is due. Dr. Clanton is one of the most positive and supportive directors that I have seen. You strive to help each one of us succeed and usually at the expense of added stress to your life. I’m so happy that you are allowing yourself to pass on some of the resonsibilities to other members of the faculty.
vii Now, for the two most important men in my life. Caleb, you are just a few months old, but you have made such an impact on my life. Never before have I felt so many emotions and so much love – maybe it is all the hormones, anti- depressants, and alcohol talking, but I’m sure maternal instinct has something to do with it. That silly person that I use to be when I did not want children never knew what it meant to feel fulfillment in life, until recently. I pray that I do not disappoint you as a parent, do not embarrass you too much in front of your friends, provide a safe and happy home, and do not cause so much damage that years of therapy cannot cure. If you are anything like your father and I then you will surely understand the sarcasm in that statement. And Matt…you always ask me why I love you. I never know how to put it into words – imagine that I don’t know what to say. You are my source of strength and self-confidence. While I act like a feminist with everyone else, you know exactly how weak and frail I am.
You are so supportive, loving, understanding, and intelligent. And I strive to make our life together happier than the day before.
Finally, to my Father in Heaven. I know I disappoint you often, but you have an everlasting love and forgiveness. Please do not give up on me.
viii VITA
June 12, 1977…………………..…………Born – Morgantown, WV
1999…………………………………………B.S. Chemistry & Biology, Bridgewater
College, Bridgewater, VA
1999-2000………………………………….Graduate Teaching Assistant
2000-present……………………………….Research Assistant, The Ohio State
University
2002…………………………………………Graduate Teaching Assistant
2003…………………………………………M.S. Biophysics, The Ohio State
University
PUBLICATIONS
1. Whitaker CDS (2001) What Recourse? – Liability for Managed-care
Decisions and the Employee Retirement Income Security Act. Review of
Mariner W. N Engl J Med. 2000;343:592-596. Journal of Allied Health
30(2) 133.
ix FIELDS OF STUDY
Major Field: Biophysics
x TABLE OF CONTENTS
Page Abstract ...... ii
Acknowledgments ...... v
Vita ...... ix
Table of Contents...... xi
List of Tables...... xiv
List of Figures ...... xvi
List of Symbols ...... xxii CHAPTERS Introduction...... 1
Introduction to Alzheimer’s Disease ...... 5 Histology and Pathology...... 7 Genetics...... 14 Molecular Biology and Oxidative Stress...... 16 Other Mechanisms ...... 20 Other Diseases ...... 22 Introduction to the effects of Iron...... 25 Metalloproteins...... 27 NeuroAnatomical Localization of Iron...... 28 Localizing Iron ...... 34 Imaging iron ...... 38 Introduction to MRI...... 38 Dephasing Mechanisms...... 44 Magnetic Homogeneity...... 44 Imaging Gradients ...... 45 Macroscopic Susceptibility ...... 46 Mesoscopic Susceptibility...... 46 Microscopic Susceptibility...... 49
xi Bloembergen-Purcell-Pound Theory ...... 49 Dephasing to Relaxation ...... 50 Moving to 8 Tesla...... 50 Free Induction Decay ...... 52 FID Pulse Sequence...... 52 T2* ...... 53 T2...... 57 γ2G2D...... 63 FDRI...... 65 GESFIDE ...... 66 Phase Imaging ...... 68 Imaging Alzheimer’s Disease...... 72 Volumetrics ...... 72 Spectroscopy ...... 74 Magnetization Transfer...... 74 Relaxation Time Measurements...... 75 Magnetic Resonance Microscopy...... 76 Gradient Echoes...... 76 Spin Echoes ...... 77 Accuracy of T2 Measurements at High Field...... 79 Methods and Results ...... 90 Ball Phantom ...... 98 4 L Nalgene Bottle...... 103 1.5 L Plastic Container ...... 109 In situ Cadaver ...... 115 Discussion...... 121 Macromolecular Effects on T2...... 125 Methods ...... 127 MRI Methods...... 129 Results ...... 132 Low Field Hahn Analysis ...... 132 High Field Analysis...... 136 Discussion...... 140 In vitro Study of Hippocampus...... 148 Methods ...... 148 Results ...... 152 Observation of Anatomy ...... 152 Cytoarchitecture and Cellular Layers...... 154 Initial Exploration of Methods Described in the Literature...... 157 T2 and T2*...... 157 Iron Manipulation Results ...... 160 Field Dependent R2 Increase (FDRI) ...... 160 Phase Imaging...... 162
xii Combined Hahn and CPMG...... 163 Mixed Effects Analysis...... 164 Bootstrap Analysis ...... 169 Conclusions...... 171 In situ Study of Alzheimer’s Disease ...... 173 Methods ...... 174 Subject Population...... 174 MRI Data Collection...... 175 MRI Image Analysis...... 177 Brain Tissue Preparation ...... 180 Results ...... 182 Image Appearance ...... 182 Transverse Relaxation and Combined Analysis Maps ...... 189 Correlation to Iron Content ...... 195 Assessment of Combined Spin Echo Fit Analysis & Iron Content Measurements...... 195 Comparison of In Situ Cadaver and In Vitro Brain Slice Combined Fit Results ...... 196 Comparison of In Situ R2 and γ2G2D with Mass Spectroscopy....202 Application to AD...... 206 Utility of Techniques ...... 218 Conclusions...... 219 In vivo Study of Normal Controls ...... 222 Methods ...... 222 Results ...... 224 Image Appearance ...... 224 Relaxation Time and Combined Analysis Maps ...... 227 Correlation to the Hallgren Equations for Total Brain Iron ...... 234 Conclusions...... 236 Conclusions ...... 238 Future Work ...... 240 List of References...... 244
xiii LIST OF TABLES
Table Page
6.1 fn(θ) for echos number 1-4 of a CPMG sequence 86
6.2 Relative T2 values for regions of interest in situ at 90° and 45° nominal flip angle 120
7.1 All 0.7T and 1.5 T Hahn spinn data for relaxivity (R2) 135
7.2 High field data resulting from combined spin echo fit of Hahn and CPMG data 140
7.3 Least squares results for γ2G2D as a function of BSA concentration for various Gd-DTPA concentrations 146
8.1 Details of the subject cohort 149
8.2 Mean T2 value for AD and Normal Controls for all regions of the hippocampal complex 157
8.3 Mean T2* values for AD and Normal Controls for all regions of the hippocampal complex 159
8.4 FDRI results comparing 1.5 T to 8 T 161
8.5 Mixed Effects Model results for R2 168
8.6 Mixed Effects Model results for γ2G2D2 169
8.7 Threshold levels for the normal controls 170
9.1 Summary of subject population 175
9.2 Summary of in situ Hahn, CPMG, Intrinsic T2 and γ2G2D2 data for cortical and medial temporal lobe gray and white matters 193
xiv 9.3 Correlation fit results from T2 and γ2G2D with total iron content measured by mass spectroscopy 204
10.1 Summary of in vivo subject population 223
10.2 Hahn, CPMG, and Combined spin echo analysis values for gray and white matter in vivo averaged over the age range 229
10.3 Hallgren age-dependent total iron calculation results for in vivo sujects 234
10.4 Least squares results from the correlation of total brain iron to Hahn, CPMG, Intrinsic T2, and γ2G2D 236
xv LIST OF FIGURES
Figure Page
2.1 Biel’s staining of a section of AD tissue. 9
2.2 Braak and Braak diagram of the amyloid plaque burden throughout the stages of Alzheimer’s Disease. 12
2.3 Braak and Braak diagram of the distribution of NFT burden. 13
3.1 Graphed Hallgreen equations for total brain iron as a function of age. 30
3.2 Sample section from a liver specimen stained with Perls’ stain. 36
4.1 Diagram demonstrating the tipping of net magnetization out of the longitudinal plane into the transverse plan by an angle, α. 41
4.2 Loss of coherent net transverse magnetization, T2. 42
4.3 Magnetic Suceptibility. 43
4.4 Magnetic gradients used in the localization of spins 45
4.5 Showing the partial volume effects of a sphere of radius, r, influencing a volume that is one million times its actual volume. 48
4.6 Diagram of a pulse sequence used in sampling the Free Induction Decay 53
4.7 T2* definitions 55
4.8 Simplified schematic of the gradient echo sequence 57
4.9 Differences in the measurement of T2 and T2* 58
4.10 Simplified schematic of the Hahn spin echo sequence 59
xvi 4.11 Example CP spin echo 60 4.12 Diagram of the refocusing of spins with the CP spin echo in the rotation plane 61
4.13 Diagram of the CPMG spins 62
4.14 Schematic of the GESFIDE pulse sequence 67
4.15 A) Magnitude in situ image, B) Phase image resulting from image in A) 69
4.16 Figure 1 from Dashner 71
6.1 Diagram (simplified) of CPMG sequence 81
6.2 Diagram (simplified) of Hahn sequence 82
6.3 Phase diagram of a perfect Hahn spin echo 83
6.4 Phase diagram of a CPMG spin echo where there is a perfect 90° but imperfect 180° RF pulses 84
6.5 CPMG sequence diagram shown again, but with the Majumdar crusher gradients applied to the slice direction 87
6.6 Sample in vivo B1 map 90
6.7 96well sample images 94
6.8 T2 dependence of flip angle for 96well phantom 96
6.9 A) Scatter plot of Hahn T2 dependence on flip angle, B) Histogram of the Hahn T2s 97
6.10 Demonstration of the truncation artifacts in the grid-like pattern of the 96well phantom from the Hahn SE short TE image 98
6.11 A) Sample Hahn SE image of the ball phantom, B) Hahn T2 map, C) CPMG T2 map, D) Flip angle map, E) Receive sensitivity map for first data series. 100
6.12 T2 dependence on flip angle scatter plots 101 6.13 Histograms from Hahn and CPMG spin echos with long (2500 ms) and short (400 ms) TRs 102
xvii 6.14 Images from 1.5 L Nalgene bottle experiments 104
6.15 Continued from Figure 6.14 105
6.16 T2 scatter plots for the two 1.5 L Nalgene Bottle data repeat studies 106
6.17 Hahn spin echo data for A) Attenuation setting for a nominal flip angle of 90° and B) Nominal flip angle of 45° 107
6.18 Hahn spin echo decay patterns for 0.5mM Gd-DTPA phantom at various flip angles 108
6.19 CPMG spin echo decay patterns for 0.5mM Gd-DTPA phantom at various flip angles 108
6.20 A) Spin echo sequence image. B) Hahn, C) CPMG, D) Flip angle, and E) Receive sensitivity maps 111
6.21 T2 dependence on flip angle scatter plots 113
6.22 A) Hahn and B) CPMG T2 scatter plots with flip angle. C) Intrinsic T2 scatter with flip angle and D) γ2G2D2 scatter 114
6.23 A) Spin echo image where regions of flip angle <70° and >110° are shaded and B) regions where 1st CPMG echo signal is greater than the 2nd. 117
6.24 Distributions from a gray and white matter ROI 119
7.1 Structure of DTPA with coordination to Gadolinium 128
7.2 0.7T R2 versus Gd-DTPA concentration 134
7.3 Demonstration of the inability of a mono-exponential decay to fit high field Hahn and CPMG data 137
7.4 A) Relaxivity ploy of R2 versus Gd-DTPA for 12% BSA. B) γ2G2D2 for the same voxels as in A) 139 7.5 Diagram of speed of diffusion past field perturber (contrast agent) 144
7.6 Summarized Weisskoff and Kennan simulation results 144
7.6 γ2G2D2 results versus Gd-DTPA for each BSA concentration 145
7.7 Data from Figure 7.6 reorganized into γ2G2D2 versus BSA concentration for each Gd-DTPA concentration 142
xviii 7.8 Intrinsic R2 (1/T2) versus Gd-DTPA where slope is equal to R2 147
8.1 Demonstration of the ROI selection 151
8.2 Sample Hahn spin echo image demonstrating typical contrast and SNR 153
8.3 Cresyl violet staining of the hippocampus and entorhinal cortex 155
8.4 Sample histological staining results 156
8.5 Sample gradient echo image (TE=15 ms) of the hippocampus specimen 159
8.6 A) Gradient echo magnitude image corresponding to B) phase image 162
8.7 Combined analysis distributions 165
8.8 Distributions for A) Hahn and B) CPMG hippocampus data 166
8.9 Sample data from Mixed Effects Model 167
9.1 Demonstration of Hahn, CPMG, and Combined Fit for white matter, gray matter, and the globus pallidus 178
9.2 Selected ROIs used for assesment of T2 distributions in AD and normal controls demonstrated on one in situ case 179
9.3 Example Hahn spin echo images for all TEs 183
9.4 Typical image with TE=50ms 184 9.5 A) TE of 50ms spin echo image of the neocortical area. B) Combines T2 and C) γ2G2D2 maps 186
9.6 Example 1.5 T image showing that gray and white matter contrast are consistent through the brain, unlike 8 T 187
9.7 Signal to noise comparison for the various Hahn TEs. 188
9.8 Sample data from 8 T from subject N 190
9.9 Iron oxide data for combined analysis 187
9.10 Example data for mass spectroscopy 198
xix 9.11 Example slice image and maps 200
9.12 Comparison of the intrinsic T2 and γ2G2D2 in in situ cadaver and In vitro brain slice 201
9.13 In situ data for combined analysis 203
9.14 Hallgren equations compared to the mass spectroscopy results 206
9.15 Hahn distributions for gray matter in situ subjects 208
9.16 Hahn distributions for white matter in situ subjects 209
9.17 CPMG distributions for gray matter in situ subjects 210
9.18 CPMG distributions for white matter in situ subjects 211
9.19 Intrinsic T2 distributions for gray matter in situ subjects 212
9.20 Intrinsic T2 distributions for white matter in situ subjects 213
9.21 γ2G2D distributions for gray matter in situ subjects 214
9.22 γ2G2D distributions for white matter in situ subjects 215
9.23 Example of the thresholding technique 216
9.24 Sample bootstrap results for cortical gray and white matter ROIs. 217
10.1 Sample in vivo spin echo image with TR/TE=1500/50ms 225
10.2 Signal to noise comparison of in vivo data 227
10.3 Sample calculated maps from in vivo data 228
10.3 Sample distributions from all subjects for a cortical gray matter area 211
10.4 Hahn distributions for gray and white matter 230
10.5 CPMG distributions for gray and white matter 231
10.6 Intrinsic T2 distributions for gray and white matter 232
10.7 γ2G2D distributions for gray and white matter 233
xx 10.8 Correlation between ICP-MS and the Hallgren age dependent equations 235
10.5 Signal to noise comparison of in vivo data 214
xxi LIST OF SYMBOLS h – h/2π •OH – hydroxyl free radical AC – alternating current AD – Alzheimer’s Disease ADRDA – AD and Related Disorders Assocation APOε – cholesterol metabolims protein gene that is a risk factor for AD APP – amyloid precursor protein ATP – adenosine triphosphase Aβ (β-amyloid) – amyloid peptide B0 – magnetic field in the z-direction B1 – radiofrequency magnetic field BBB – blood brain barrier BOLD – blood oxygen level dependent BSA – bovine serum albumin CA – cornu Ammonis, the four subfields of the hippocampus CDC – Center for Disease Control CERAD – criteria CJD – Creutzfeldt-Jakob disease CP – Carr and Purcell spin echo sequence CPMG – Carr, Purcell, Meiboom, and Gill spin echo sequence CSF – cerebral-spinal fluid D – 1) diffusion constant or 2) difference term in the Kolmogorov-Smirnov test DAB – diaminobenzadine DC – direct current DNA – deoxyribonucleic acid
xxii DS – Down’s syndrome DTPA – diethyltriamine pentaacetic acid f – attenuation factor based on B0 and B1 maps by Sled FAD – familial Alzheimer’s disease FDRI – field dependent R2 increase Fe(II) – ferrous ion, Fe2+ Fe(III) – ferric ion, Fe3+
Fe4[Fe(CN)6]3 – ferric ferrocyanide
FeCl3 – ferric chloride FID – free induction decay fn – function derived by Majumdar that is dependent on flip angle and describes the adjusts to the magnetization FOV – field of view FWHM – full width at half maximum G – gradient (due to susceptibility) Gauss – unit of magnetic field, 10,000 gauss = 1 Tesla Gd – gadolinium GESFIDE – gradient echo sample of FID and echo h – Planck’s constant
H2O2 – hydrogen peroxide HDPE – high density polyethylene
HNO3 – nitric acid ICP-MS – inductively coupled plasma mass spectroscopy ICP-OES – inductively coupled plasma optical emission spectroscopy IDL – interactive data language IP – intraparenchymal vessels k – Boltzmann’s constant
K4Fe(CN)6 – potassium ferrocyanide KCl – potassium chloride kDa – kilo-Dalton or 1000 Daltons. A Dalton is equivalent to molecular weight (g/mol) for proteins.
xxiii LM – leptomeningeal vessels longitudinal plane – plane in the z-direction LTP – long term potentiation M – magnetization m – mass M(χ) – susceptibility induced magnetization
M0 – initial magnetization MAP – microtubule-associated proteins MCI – mild cognitive impairment MRI – Magnetic Resonance Imaging MS – multiple sclerosis MT – magnetization transfer n – echo number or number of spins (see contex) NEX – number of excitations NFT – neurofibrillary tangle NIH – National Institutes of Health NINCDS – National Institutes of Neurological and Communicative Disorders NMDA – N-methyl-D-aspartate, a neurochannel NMR – Nuclear Magnetic Resonance NOE – nuclear Overhauser effect - O2 – superoxide -OH – hydroxide ion PC – perforating cortical vessels PD – Parkinson’s disease PET – positron emission tomography PHF – paired helical filaments ppb – parts per billion ppm – parts per million ppt – parts per trillion PrP – prion protein PS1 – presenilin 1
xxiv PS2 – presenilin 2 q – charge r – distance at which DB is measured from the iron particle r – unit vector in the directin of r R1 – 1/T1 R2 – 1/T2 R2 – relaxtivity, the slope of R2 vs. contrast agent concentration R2* – 1/T2* R2’ – 1/T2’ R2 – square of the Pearson product moment correlation coefficient RARE – rapid acquisition of refocused echoes RF – radiofrenquency RNA – ribonucleic acid ROI – region of interest ROS – reactive oxygen species RSI – research systems incorporated S – signal SAP – stress-activated protein SH3 – micro sample vial for Milestone Ethos Microwave Labstation SNR – signal to noise ratio SNR – signal to noise ratio STEAM – stimulated echo acquisition mode T – 1) tesla, 2) temperature T1 – longitudinal relaxation, spin-lattice relaxation T2 – irreversible transverse relaxation, spin-spin relaxation T2 – transverse relaxation T2* – transverse relaxation enhanced by the reversible dephasing effects T2’ – transverse relaxation due to the reversible contributions to dephasing TE – time of echo TEM – transverse electromagnetic resonator, a type of RF coil TR – time of repetition
xxv transverse plane – x/y-plane
Tx – attuation power setting z – unit vector in z-direction Χ2 – Chi square ΔB – change in magnetic field due to several factors explained in Chapter 3 α – 1) lower energy state for spins that is parallel to the main magnetic field B0, 2) flip angle β – higher energy state for spins that is antiparallel to the main magnetic field B0 φ – time dependent phase of a spin γ – gyromagnetic constant
µ0 – constant of permitivity of free space
µm – magnetic moment ν – frequency of light τ (tau) – 1) protein used in cytoskeleton that self-aggregates in Neurofibrillary tangles, 2) time in CP or CPMG sequence between the 90° and 180° pulses
ω0 – Lamor frequency
xxvi CHAPTER 1
INTRODUCTION
Magnetic Resonance Imaging (MRI) is a technique that was developed for its non-invasive nature and unique high resolution with soft tissue contrast by applying the principles of Nuclear Magnetic Resonance (NMR). It has since been proven to be extremely versatile for imaging everything from blood flow to ‘still’ images of the beating human heart. All of these advancements have allowed for greater diagnostic power for the physician and greater understanding of many diseases.
The Ohio State University has developed the world’s only functioning 8 Tesla (T) whole-body MRI at the Center for Advanced Biomedical
Imaging. This was a collaboration of several university departments and continues to involve groups with projects ranging from engineering and the school of medicine to veterinary medicine and food sciences. While imaging is possible, the system and underlying contrast mechanisms at high field are not yet fully understood. Therefore, several researchers are working on various aspects of the instrument to increase the knowledge base. It would then follow that these routine experiments could lead to improved clinical care.
1 Alzheimer’s disease (AD) is a cortical dementia characterized by a gradual decline in memory and at least one other cognitive domain (aphasia, apraxia, agnosia, and executive dysfunction), not due to other nervous system disease, drugs, delirium, or psychiatric conditions, and causing impairments in activities of daily living (American Psychiatric Association, 1995). Pathologically, there is an accumulation of neuritic plaques extracellularly and neurofibrillary tangles intracellularly. With the baby boom generation approaching the retirement age, there is an urgent need to find a treatment to slow the progression and/or to cure the disease. The financial burden for care of these patients is great, and day-to-day care is often very stressful for family members because death is preceded by years of decline and dependence. The death of former President Ronald Reagan has renewed interest in possible, but controversial, treatment methods, such as stem cell research. One would also expect there to be additional congressional dollars directed to the NIH (National
Institutes of Health) for advancing AD research.
The initiation of the work described in this dissertation was not to find a treatment nor a cure, but to aid in developing a means of measuring progression of disease. Currently, diagnosis is only confirmed by post mortem pathological evaluation.
This document has been set forth in a manner to facilitate the understanding of this research. There is an introduction to Alzheimer’s dementia.
This includes a description of the understanding of the disease to date, including
2 genetic and molecular hypotheses for the etiology of the dementia. Much of this chapter includes the literature review that was the impetus for this work.
Following the discussion of AD, there is a description of the role of iron. This includes a basic chemical description of the forms it takes within the human body, how its distribution and concentration changes throughout the lifetime of healthy individuals, and the methods that are used for quantitative analysis of iron (total content and localization). This includes a brief description of NMR theory and the development of MRI. The special circumstances around the engineering of the 8 T and the technical advantages for higher field strengths are also discussed. Chapter 3 concludes with the mechanisms of iron effects on
MRI signal.
Next, Chapters 4 and 5 are dedicated to a literary review of
Magnetic Resonance Imaging methods and their application to Alzheimer’s disease, respectively.
Chapters 6 and 7 examine the methodology in simple and complex phantoms. High field MRI presents issues that are not necessary new, but are more troublesome and currently without solution. One such issue is to accurately measure relaxation rates in the presence of B1 inhomogeneities. Chapter 6 is concerned with the dependence of T2 measurements on B1 inhomogeneities in two spin echo techniques. Chapter 7 is in parallel with work performed by Chad
Mitchell where the effects of macromolecules on relaxation measurements of contrast agents at various field strengths were measured. This Chapter is focused specifically on transverse relaxation rates. 3 Chapters 8 through 10 provide a summary of the work that tests the hypothesis that increased iron in AD can be detected non-invasively by MRI.
This begins with an in vitro study with small sections of brain tissue and development of a protocol (Chapter 8). Next, in situ imaging was used to adjust the protocol, compare results to in vitro brain slice imaging, and to obtain independent iron measurements (Chapter 9). Finally, preliminary in vivo data with correlation to calculated iron values for healthy subjects (Chapter 10).
The concluding chapters are to present the final remarks regarding all studies (Chapter 11) and any open questions that remain to be investigated
(Chapter 12).
In summary, the specific aims of this work include:
1. Develop a basic understanding of AD, MRI, and in vivo iron,
2. Evaluate the accuracy of T2 measurements in the presence of B1 field
inhomogeneities,
3. Evaluate how macromolecules affect T2 relaxation, and
4. Evaluate the effectiveness of high field MRI to measure iron levels in normal
and diseased brain tissues.
4 CHAPTER 2
INTRODUCTION TO ALZHEIMER’S DISEASE
Alzheimer’s disease (AD) is a neurodegenerative process that is characterized by the accumulation of extracellular of beta-amyloid plaques and intracellular filamentous tangles of a protein tau (τ). At this time, AD is the most common neurodegenerative disorder with more than 4 million people currently diagnosed and estimates of 14 million diagnosed nation-wide by 2050
(www.alz.org). First described by Alois Alzheimer in his 1907 (English translation by Stelzmann in 1995) characterization of a 51-year-old woman (Alzheimer et al.,
1995), Dr. Alzheimer described a patient that first experienced extreme jealousy of her husband and progressed to experience memory loss, feelings of helplessness, confusion, and was finally bed-ridden until her death. The disease is clinically characterized by the progressive loss of memory, impaired or poor decision making, changes in personality, and trouble with abstract thought. It is observed predominately in individuals over the age of 60. The Alzheimer’s
Association (www.alz.org) has created a list of symptoms for family members to be aware of in their aging parents. These include (Rebok et al., 1994):
• Memory impairment
5 • Impairment of another area of cognition
• Impairment on mental status testing
• Unclouded consciousness
• Absence of other conditions that would cause dementia
Clinical diagnosis here at OSU is based on the NINCDS (National
Institute of Neurological and Communicative Disorders) and ADRDA (AD and
Related Disorders Association) criteria (McKhann et al., 1984). Below is a simplified chart from McKhann indicating criteria for the diagnosis of the disease.
• Probable AD
Dementia established by neuropsychological testing
(MMSE)
Deficits in two or more areas of cognition
Worsening of memory
No disturbance of consciousness
No other disorders present
Other criteria supportive of probable AD
Development of aphasia, apraxia, or agnosia
Impairment of daily activities
Family history of AD
Onset between the age of 40 and 90
• Possible AD 6 Presence of dementia without other identifiable cause
If other disorder present, it cannot explain dementia
• Definite AD
Clinical criteria documented
Histological evaluation (postmortem or biopsy)
There are certain risk factors that lead to an increased likelihood of onset of dementia. This includes age, genetics, cerebrovascular risk factors, head trauma (Newman et al., 1995)). Of those listed, the leading factor is age, especially after 65 years. There is also an increased risk for a patient with
Down’s syndrome.
Familial disorders include those for which there is a genetic defect within a lineage that leads to an increased chance of the offspring of an afflicted individual developing this same phenotype. While there has been extensive investigation into the genetic mutations that may lead to the onset of AD, 85-90% of all AD cases have no family history of the dementia (Chapman et al., 2001).
The genetic mutations that are under current investigation will be discussed in detail later in this chapter.
HISTOLOGY AND PATHOLOGY
While AD is currently diagnosed in a clinical setting with up to 90% accuracy, it is not a definite diagnosis until a postmortem evaluation of the brain tissue confirms the presence of plaques and tangles. Histopathological evidence is matched with that established by The Consortium to Establish a Registry for 7 Alzheimer’s Disease (CERAD; Mirra et al., 1991). This method consists of an estimation of the neuritic amyloid plaque burden by a pathologist, who can then correlate those results to the age (some plaques are more common with increased age) of the subject and any history of dementia that is present in the clinician notes. The staining method is not standardized (usually a silver stain such as Bielschowsky’s). Also, a minimum of five regions of the brain are sampled for study, and the patient score for plaque burden is based on the site most heavily afflicted.
8 PlaqueTangle Filled Neuron
Figure 2.1: Biel’s staining of a section of AD tissue. Large spheres are plaques while the tear- drop objects are NeuroFibrillary Tangle (NFT) loaded neurons.
β-Amyloid (Aβ) deposition is a common occurrence in aging; however, it becomes problematic in Alzheimer’s due to the profound accumulation into neuritic plaques with reactive glial cells (Isobe et al., 2000).
There are four distinguishable types of amyloid plaque and two theories behind their development. The first theory suggests that each of the four types of amyloid plaques have different pathways of formation that are unrelated. The other theory suggests that amyloid plaques may develop and mature into the
9 next level depending on the duration of dementia. Amyloid plaques may be found in the following forms (Armstrong, 1998).:
• diffuse: Aβ is non-aggregated and usually does not contain
dystrophic neurites or paired helical filaments
• primitive: Aβ is aggregated with paired helical filaments
• classic: high aggregation of Aβ with a ring of dystrophic
neurites
• compact: core of Aβ without the ring of neurites
Primitive plaques are often found within a healthy patient. Despite their origin, in order to have confirmed Alzheimer’s a patient must show profound accumulations of classic/compact types of plaques.
The second pathological marker in diagnosis of AD is the neurofibrillary tangle (NFTs). Although these do not play a role in the experiments described in this dissertation, there is a considerable amount of literature focusing on this feature of the dementia, and so they will be described here. In short, these filaments are the result of dysfunctional tau, a 352-441 amino acid protein (six isoforms; chromosome 17) belonging to a group of proteins called microtubule-associated proteins (MAPs; Tolnay et al., 1999). The role of these proteins is to construct the cytoskeleton framework for the cell and to allow the transport of intracellular components. When tau is phosphoralated, it may then polymerize and result in improperly formed microtubules and disruption of intracellular transport (St George-Hyslop, 2000b). The tau hypothesis for etiology of AD (Mandelkow et al., 1998) begins with the increased activity of 10 specific kinases (including the stress-activated protein, SAP, kinases; Spillantini et al., 1998), which leads to the hyperphosphorylation of tau. Tau can then no longer remain bound to microtubules so there is a breakdown of axonal transport and accumulation of paired-helical filaments (PHFs) of tau, which combine to form NFTs (Brion, 1998). The toxicity associated with the tangles and breakdown of transport then leads to the death of neurons.
In 1991, Braak and Braak (1991) described the progression of AD by locations of the accumulated plaques and tangles by examination of 83 brains. They classified the progression into six stages for neurofibrillary tangles and three stages for plaques.
11 Figure 2.2: Braak and Braak diagram of the amyloid plaque burden throughout the stages of Alzheimer’s Disease. Lightly shaded regions indicate scattered plaques while darker shading indicates heavy burden. (A) early stage, (B) middle stage, (C) late stage. The top and middle rows are lateral views with the middle row being a slice down the midline with anterior at the left and posterior at right. The bottom row is a view at the inferior side of the section shown in the middle. (Braak et al., 1991)
Amyloid plaque accumulation begins in Stage A with the isocortex of the frontal, temporal, and occipital lobes as well as the some layers of the entorhinal cortex. Stage B is characterized by medium density accumulations within a majority of isocortical areas with primary sensory areas void of plaques.
The hippocampal complex begins involvement in the CA1 region. The final step of plaque staging involves nearly all isocortical, subcortical areas, and some nuclei. As seen above, there are scattered plaques in a few areas of the brain initially, but as accumulation continues there is recruitment of other areas according to connectivity (Braak et al., 1991; Hof, 1997; Nagy et al., 1999;
Mesulam, 2000).
12 Figure 2.3: Braak and Braak diagram of the distribution of NFT burden (similar to Figure 2.2) (Braak et al., 1991).
Neurofibrillary changes (both tangles and threads) have shown greater correlation with dementia (Bierer et al., 1995; King et al., 1999). These changes have not been the focus of MRI studies of Alzheimer’s. Briefly, the six
Braak stages (Braak et al., 1991) are grouped into three categories. Stages 1-2 begin with the early onset of dementia and increased NFT pathology in the transentorhinal areas of the medial temporal lobe. Stages 3-4 progress through the limbic system with the transentorhinal being densely populated by NFTs.
Finally, stages 5-6 show expansion into the isocortical areas. There are
13 cytoarchitectural details that differentiate each group into two stages, but that description is beyond the scope of this discussion.
GENETICS
While most cases of AD are considered sporadic, there are several autosomal, mutations that have been shown to cause an increased predisposition to the development of early onset of AD. The manner in which the mutations aid in the development of AD pathology is not fully understood despite the strong correlation of age of onset with presence of allele. Therefore, only a description of the mutations can be made along with the resultant phenotypes.
The genes to be discussed include Amyloid Precursor Protein (APP), Apoε, and the presenilins.
The Amyloid Precursor Protein (APP) gene is located on chromosome 21,which is the same chromosome involved with Down’s syndrome.
This gene on chromosome 21 is believed to result in an early onset (typically 30 years) of AD in Down’s syndrome patients (Chapman et al., 2001). Translation of this gene results in a 65kDa transmembrane protein of unknown function
(Huang et al., 2000).
APP can undergo two enzyme activated cleavage reactions 1) α- secretase cleaves APP between positions 16 and 17 resulting in a nontoxic peptide, p3, or 2) β-secretase cleaves the N-terminal of the protein, which is then cleaved by γ-secretase to yield the amyloid β peptide, Aβ (Lannfelt et al., 1995).
14 This last cut of APP may result in two different peptides, either Aβ40 or Aβ42, depending on the number of amino acids remaining. Both may form amyloid plaques but the later is considered the most toxic causing more intense disruptions of metabolism, immunological responses, damaging organelles and biomolecules (St George-Hyslop, 2000a; St George-Hyslop, 2000b).
There are three mutations associated with APP that increase the production of the toxic versions of Aβ. The result of these mutations is symptomatic to patients prior to the age of 60 (Chapman et al., 2001).
Apoε is a protein involved in cholesterol metabolism transport and storage with genetic locus on chromosome 19 (St George-Hyslop, 2000a;
Chapman et al., 2001). There are 3 alleles of this gene of which a majority (45-
60%) of familial AD cases are at least heterozygous for the Apoε-4 allele
(Chapman et al., 2001). This gene is considered only as a risk factor for AD.
The presenilin 1 and 2 (PS1 and PS2) genes are located on chromosomes 14 and 1, respectively, and are expressed in neurons. Wolfe
(1999) showed that PS1 may be associated with the γ-secretase responsible for a cleavage pathway of Aβ. Presenilin mutations, along with the APP mutation comprise a small percentage of early onset Familial Alzheimer’s Disease (FAD) cases and have typical autosomal, dominant, Medelian traits and result in early onset of dementia (St George-Hyslop, 2000a).
15 MOLECULAR BIOLOGY AND OXIDATIVE STRESS
Since AD is mostly sporadic, other non-genetic explanations have been offered. Outside of the genetic realm within the nucleus, there are organelles that regulate cellular actions. One particular organelle has stood out in research on AD due to its ability to produce high levels of radicals necessary for its function. Known as the “powerplant” within the eukaryotic cell, mitochondria are the organelles that are responsible for the conversion of glucose into ATP via the Kreb’s cycle and electronic cascade chain. These biochemical pathways include many oxidation-reduction (redox) steps.
There has been increasing interest in the factors causing oxidative stress and its importance in a variety of diseases. Several metals have been investigated for a relation to AD as a source of oxidative stress. While a correlation was not found for many of these metals, a few continue to be investigated as a potential connetion to the onset of AD.
Nevertheless, it has been demonstrated that dementias, such as
AD, have increased oxidative stress in those areas pathologically involved.
Nunomura (1999a; 1999b) used antibodies against oxidized nucleoside of RNA and DNA in neuronal cell populations with AD. They found that there were increased numbers of oxidized nucleosides from RNA compared to DNA. They believed that the reason that DNA nucleosides were relatively unaffected compared to RNA was due to the protection of DNA by being enclosed within the nucleus and safe from oxidative stresses found within the cytoplasm.
16 Iron is a vital redox metal that is involved in many metabolic activities. However, it is also capable of facilitating the production of free Radical
Oxygen Species (ROS). Iron (II) will donate an electron to cleave hydrogen peroxide into the hydroxyl free radical and hydroxide (Halliwell, 1991; Smith et al., 1997a; Sayre et al., 1999; Sayre et al., 2000a; Smith et al., 2000a; Rottkamp et al., 2001).
Fe(II) + H2O2 → •OH + -OH + Fe(III) Equation 2.1
This reaction, known as the Fenton reaction, results in one of the most potent oxygen free radicals, the hydroxyl radical, by the oxidation of ferrous iron to split hydrogen peroxide (H2O2). The hydroxyl radical, •OH, reactivity depends only on time of diffusion (usually on the µsec scale) and can denature proteins, DNA, and initiate lipid peroxidation. These different products are evidence of oxidative stress (Halliwell, 1991). It has been shown that abnormal mitochondria are found in AD at levels up to three-times more than in cells of normal controls (Oshiro et al., 2000). As already stated, mitochondria are a potential source of oxidative stress due to all of the redox chemistry associated with the role of the organelle. It has been hypothesized by Rottkamp that
- superoxide, O2 , is converted to hydrogen peroxide within mitochondria.
Hydrogen peroxide may then easily cross the organelle membrane and diffuse
17 toward plaques and tangles that are rich in iron. Here it is possible for the
Fenton reaction to occur (Rottkamp et al., 2000; Smith et al., 2000a; Rottkamp et al., 2001).
Other evidence of iron in Alzheimer’s disease is found in histological, chemical, and magnetic resonance studies. Connor (1986; 1990;
1992a; 1992b; 1995) was one of the first to study metalloproteins and iron distributions within Alzheimer’s disease. Using antibodies against ferritin and transferrin (see discussion on these proteins in Chapter 3) and diaminobenzadine (DAB)-enhanced Perls’ staining for iron, Connor localized all three forms of iron within Alzheimer neurons. Observations included decreased levels of ferritin and transferrin and increased iron levels in AD compared to normal controls within the same areas. Considered together, this may indicate that iron mobility is decreased and ferritin loading factor is increased in AD.
Techniques such as Inductively Coupled Plasma (ICP) Mass
Spectroscopy (Emmett, 1989; Beauchemin et al., 1998) and atomic absorption
(Crapper et al., 1978) have been used to evaluate the presence of several metals including Al, Fe, Co, Cu, and Na within the Alzheimer brain. Beauchemin specifically examined amyloid plaque cores and found that Al, Fe, and Zn levels were all elevated as compared to matrix-matched brain digest solutions by ICP-
MS. Cornett (1998) used Instrumental Neutron Activation Analysis of freeze- dried sections of brain (including frontal pole, temporal pole, inferior parietal, hippocampus, amygdala, cerebellum, and olfactory) and found that iron and zinc were significantly elevated in many of these regions for AD compared to normal 18 controls. Caution must be taken when considering some older quantitative iron studies due to the use of whole brain homogenates for analysis, which have shown that total iron in brain does not change. Connor and Beauchemin have shown that it is the localization of iron that has changed (Beauchemin et al.,
1998; Connor et al., 1992a).
A final, independent method for correlating iron with AD pathology is Magnetic Force Microscopy and related methods. Dobson (1996; 2001) suggested that the irons associated with AD plaques cannot be fully explained by the increases in ferritin loading (as suggested by Connor) but indicates that there is a biogenic magnetite formation on the plaque (see also Kala et al., 1996). He hypothesized that magnetite crystal is formed by overloading ferritin’s capability to oxidize ferrous iron into the ferrihydrite crystalline structure for storage.
Magnetite, a crystalline of Fe3O4, has ferromagnetic properties resulting in a stronger magnetic dipole than the ferritin counterpart. (Dobson, 2001). These types of structures have been seen in other species such as fish and in theory may aid in their seasonal navigation. Furthermore, the crystalline structure of the of iron deposits in normal human brain and meninges are prismatic in form and are not equivalent to the octahedral structure seen in geological origin
(Kirschvink et al., 1992). Closer examination of histological staining, consideration of chelator effectiveness, binding properties of iron, and redox activity have all lend further evidence to a non-exclusive ferritin based metal accumulation within Alzheimer’s brain tissue (Smith et al., 1997a; Sayre et al.,
2000a; Sayre et al., 2000b). 19 OTHER MECHANISMS
Amyloid and tau have been the primary focus of many studies of
AD. However, there are several laboratories that believe that these hallmarks are the result of other fundamental changes within the cell and are not the cause of dementia. Some have even gone as far as to hypothesize that the plaques are a self defense mechanism that sequesters toxins in attempts to remove their availability to continued damage.
There are two other hypotheses that will be briefly discussed.
These include glutamate toxicity and prion disease.
The NMDA (N-Methyl-D-Aspartate) neuroreceptor is linked to classical conditioning and plays a large role in synaptic plasticity, which is important in memory (Squire et al., 2000). Glutamate is the neurotransmitter, but this alone cannot activate the receptor. Mg2+ blocks the receptor channel and can only be removed if the neuron cell membrane is depolarized simultaneously with the binding of glutamate. This allows a Ca2+ influx and initiates a cascade of reactions that sets up a Long Term Potentiation (LTP). LTP occurs when neurons possess increased synaptic strength and sensitivity to stimuli. It is a current hypothesis that memory formation occurs through LTP (Squire et al.,
2000).
In AD, there is a shift in calcium levels that some researchers have associated with NMDA. Early studies into a possible NMDA link to AD have suggested that excessive activity of the NMDA system would be found. 20 However, after investigations in mice, rats, and monkeys these scientists were forced to settle for results of hypoactivity (Tsai et al., 2002). More recently,
(Olney et al., 1997) and (Farber et al., 1998) have published work describing a mechanism for AD involving an initial NMDA hyperactivity followed by a hypoactive state. Olney (1997) proposes that hyperactivity of the NMDA channel precedes hypoactivity. He believes that oxidative stress and other metabolic disruptions caused the Mg2+ to be removed from the channel indefinitely. This allows abnormal currents of ions to flow through the cell membrane causing chronic symptoms within the neuron. Amyloid deposition has also been shown to enhance NMDA's reactivity to glutamate.
A new drug for the treatment of AD was approved by the FDA less than one year ago. Memantine is an uncompetitive antagonist of the NMDA channel (Cummings 2004). With its low affinity for the NMDA channel, it blocks low levels of glutamate from neuroactivation. However, it does not completely shut down this neuronal network. In clinical trials, Memantine has been tested independently as well as with the actylcholinesterase inhibitors that have been traditionally used for the treatment of AD. These studies have shown increased performance on MMSE (mini-mental state examination) in patients receiving the drug as compared to placebo.
Another proposed mechanism for AD involves prions. According to the Center for Disease Control (CDC), prions are infectious, proteinaceous particles that lack nucleic acids (the building blocks of genetic materials such as
DNA). The protein is an abnormal isoform of a normally occurring cellular protein 21 exhibiting an increase in the beta sheet component (Prion Protein, PrP→PrPSC).
The most commonly known prion diseases are not those that have gained media coverage, including Mad Cow Disease, but rather the very deadly and poorly understood Creutzfeldt-Jakob Disease (CJD). Prions are extremely species specific making transgenic studies difficult.
Castellani (2004) cites many similarities between the diseases caused by prions and AD including prevalent affliction of an aged group, free radical damage, presence of amyloid plaques and neurofibrillary tangles, and genetic polymorphisms that influence disease occurrence. He also makes reference to studies that investigated copper binding of both amyloid and prion protein (PrP) and implicated the use of these proteins to control oxidative stress.
Checler (2002) observes that both Aβ and PrP are cleaved by related proteases in similar fashions.
OTHER DISEASES
Prior to conclusion of this chapter it is important to note that other diseases could be investigated by high field MRI other than AD with the techniques that will be discussed. These include, but are not limited to,
Parkinson’s Disease, Huntington’s Disease, Down Syndrome (Trisomy 21), and multiple sclerosis.
Parkinson’s Disease (PD) is characterized by loss of dopamine producing neurons within the substantia nigra. Clinically this is characterized by loss of motor function and a resting tremor and rigidity (Watt, 1996). 22 Pathologically, there is an accumulation of Lewy bodies and intracellular accumulations of α-synuclein and similar proteins in the cytoplasmof the cerebral cortex (Jellinger, 1999). Like AD, PD has been associated with oxidative stress by the Fenton reaction in the presence of increased levels of iron (Castellani et al., 2000) and the addition of high levels of H2O2, a byproduct from the breakdown of dopamine (Jellinger, 1999). In vivo techniques that have been used in the study of PD include looking at decreases in MRI relaxation times associated with iron (Bartzokis et al., 1999; Graham et al., 2000) and 18F-DOPA
(precursor to dopamine) PET uptake (Kaasinen et al., 2001).
Huntington’s disease (HD) is an autosomal, dominant genetic disease (Sharp et al., 1996; Reddy et al., 1999). The HD gene on chromosome
4 encodes the Huntingtin protein (350 kDa) of unknown function. Patients that develop this disorder possess an abnormally high number of CAG repeats in the
HD gene. That is, in the normal control there is 6-35 repeats while the HD patient will have 40-121 repeats. Clinically the patients demonstrate chorea, emotional disturbances, and some atrophy of the brain particularly in the caudate and putamen. While this disease is still under investigation and some theories involve oxidative stress, it does not appear that increased iron content is linked to this disease.
Down Syndrome (DS) occurs when there are three rather than two copies of chromosome 21. As mentioned previously, the APP gene of
Alzheimer’s is also located on chromosome 21. Many DS patients develop an form of Alzheimer’s with high levels of oxidative stress (Nunomura et al., 1999a; 23 Nunomura et al., 2000) and even non-demented DS patients eventually exhibit neuroanatomical changes similar to AD (Kesslak et al., 1994). Many of the AD arguments could be applied for studying DS.
Finally, in multiple sclerosis (MS) there is a demyelination within white matter regions of the brain. The cells responsible for producing the myelin are oligodendrocytes and there is evidence that iron is involved in myelination processes. Staining has shown that there is a high level of iron in MS plaques as compared to normal control brain tissue and there is a decrease in the number of ferritin binding sites (Beard et al., 1993; Levine et al., 2004). Drayer (1987) gives an example of an MR study that has already observed signal differences in MS compared to normal controls (hyperintensities in white matter and hypointensities in some nuclei).
Alzheimer’s disease is a well-studied dementia with still many unanswered questions. This brief review of the basics of the chemistry, pathology, and genetics will make discussions in the proceeding chapters on magnetic resonance more meaningful in their relevance to diagnosis AD.
24 CHAPTER 3
INTRODUCTION TO THE EFFECTS OF IRON
Metals are those elements on the periodic table that lie to the left of the metalloid series. They are characterized by having luster, conducting electricity, being malleable and ductile, and forming cations.
The alkali and alkali earth metals (Ebbing, 1996) are those within
Groups 1 and 2 of the periodic table. Those elements with valent electrons within the d-orbitals are known as transitional metals (Ebbing, 1996). They are located in the central portion of the periodic table in Groups 1B to 10B. Many of these elements have several oxidative states. That is, the electrons of most of the elements may rearrange between the s and d orbitals resulting in different positive charge states. For example, copper’s electron configuration is
1s2,2s2,2p6,3s2,2d9 There can be a loss of either 2 or 3 electrons yielding +2 or
+3 oxidative states (cuprous and cupric ions respectively).
The ability of transition metals to possess various oxidative states makes them extremely useful in several biological processes. For example, the electron transport chain involves iron and copper containing enzymes. These enzymes couple the energy from breakdown of glucose to the proton pumps
25 used in the making of ATP (Cowan, 1997). Without the multiple oxidation states of transition metals, such movements of electrons would not be possible.
Several metal ions are toxic in vivo above a concentration specific to that metal. Therefore, the body has found a way that to effectively reduce the toxicity of the metal by forming a protein capsule around the metal. In clinical practice, treatment of metal toxicity includes the use of a chemical chelator
(Caravan et al., 1999). Chelators are beneficial in that some will spontaneously bind metals. By contrast, the proteins require the presence of the metal for folding into the tertiary structure.
There is another reason for the protein shells around metals.
Metals are important chemically because they participate in redox reactions.
Freely diffusing metals could therefore participate in any reaction involving the exchange of electrons. This contributes to the toxic nature of the metal, which can lead to cell death. Proteins and chelators allow for regulation of these reactions (Halliwell, 1991) by limiting access of a reactant to the metal. Reaction happens only when two proteins possess the ability to bring their active sites (the region involved in the chemical reaction) physically close enough for the reaction to occur. Corresponding proteins will have complimentary features allowing the proteins to interact. These interactions are guided by electrostatic fields on the surface of the protein and complimentary shapes near the active site. This has become known as the ‘lock and key’ theory for enzymatic reactions (Stryer,
1995).
26 METALLOPROTEINS
There are two categories of metalloproteins containing iron: Heme and Non-heme. Heme proteins are those involving a porphyrin (Cowan, 1997).
Porphyrins can bind a variety of metals, but iron is most often seen with the porphyrins of the human body (magnesium for plants in chlorophyll). In the human body this allows for the transport of oxygen and is a cofactor of the hemoglobin and myoglobin proteins.
Those metalloproteins that do not contain a heme complex in the protein are known as non-heme. They therefore coordinate metals (in this case, iron) in a manner unlike the porphyrin. Two proteins commonly found in the body that fall in this category are ferritin and transferrin.
The primary purpose of ferritin is to maintain homeostatic levels of iron in vivo. That is, it is a storage protein that will sequester iron when cytosol iron levels are too high and release iron when levels are low. The protein is 450 kDa and approximately 12 nm in diameter. There are 24 subunits collectively called apoferritin that may contain up to 4500 iron molecules in the crystal form of ferrihydrite (Fe2O3). Ferrihydrite is antiferromagnetic (the magnetic domains are aligned opposite of one another; Bizzi et al., 1990; Vymazal et al., 1996a;
Cowan, 1997). The number of iron clusters within the core is known as the loading factor. This protein is predominately found within the liver and spleen, but significant levels are also found within the brain (Beard et al., 1993; Koeppen,
1995).
27 The function of transferrin is the transport of iron across the blood brain barrier (BBB). It is a globular protein of approximately 80 kDa with two lobes. Each of these lobes will bind one iron atom synergistically with a carbonate ion (Cowan, 1997). Transport into cytosol occurs via a membrane receptor where part of the protein is broken off and taken internally via endocytosis.
There are forms of iron that do not involve a protein, but do occur naturally. Magnetite is an iron oxide with alternating lattice of ferric and ferrous iron ions, and is paramagnetic (Kirschvink et al., 1992; Dobson, 2001). This free iron aggregate can be found in protists, mollusks, chordates, and fish. It is hypothesized to be involved in navigational migration. There has also been evidence of magnetite in the human brain and meninges that is not of the same crystal structure (prismatic) as geological magnetite (octahedral) and therefore is most likely to be biologically generated.
NEUROANATOMICAL LOCALIZATION OF IRON
Distribution of iron (outside the vascular system) is not homogeneous. Specifically, the brain does not have a constant concentration throughout. Beard (1993) and Koeppen (1995) both review the historical evolution of the study of brain iron and how concentrations of non-heme iron changes with age. Briefly, the basal ganglia have the highest concentrations with the globus pallidus, caudate nucleus, putamen, and substantia nigra in decreasing order of total iron. White matter also has a major contribution to total 28 brain iron. The accumulation of iron in white matter coincides with the myelination of neurons in neonates. Gray matter and the cerebellum are considered to have lower iron content. However, some investigators have noted that gray matter has considerably more histological staining for iron (typically
Perls’ stian) than white matter.
Aging, and some dementias have been shown to disrupt iron homeostasis and/or distribution. For example, the changes associated with AD can be found in Chapter 2. Changes associated with aging were quantified by
Hallgren in 1958 (1958). This chemical assay (chemical reaction followed by spectrophotometric light absorption) of 81 unfixed brain specimens resulted in total iron content as a function of age for eleven brain regions. These equations, graphed below in Figure 3.1, demonstrate the quick increase in iron for young children with an asymptote reached at approximately age 30. Many magnetic resonance papers have used these equations in order to estimate the iron levels for in vivo studies. Correlation of these values have been made to in vivo MRI measurements (see Chapter 4).
29 White and Gray Matter A Age Related Increase of Total Brain Iron 5
4.5
4
3.5
3
2.5
2
1.5 Total Iron (mg/100g)
1
0.5
0 0 10 20 30 40 50 60 70 80 90 100 Age (years) Frontal White Matter Cerebellar Cortex Prefrontal Cortex Temporal Cortex Parietal Cortex Occipital Cortex
Nuclei B Age Related Increase of Total Brain Iron
25
20
15
10 Total Iron (mg/100g)
5
0 0 10 20 30 40 50 60 70 80 90 100 Age (years)
Globus Pallidus Caudate Nucleus Putamen
Figure 3.1: Graphed Hallgreen equations for total brain iron as a function of age. A) Gray and white matter and B) Nuclei changes in iron.
30 IRON MEASUREMENTS
Total elemental content is a quantitative measurement that in practice is very difficult due to possible contamination. When the techniques that are discussed below were initially developed, contamination was not an issue.
However as detection levels become more sensitive (parts per billion and even parts per trillion) it is important to eliminate careless causes of contamination.
The instrument used for this dissertation research was mass spectroscopy, but others including optical emission spectroscopy and atomic absorption will be described for comparison.
The technique used in the in situ studies of Chapter 9 is inductively coupled plasma (ICP) mass spectroscopy (see notes from Olesik, 2002). This analytical technique for the measurement of total elemental content is extremely sensitive (measuring parts per trillion of nearly all elements). Also, there are fewer matrix (solution) effects than techniques using flames (i.e. atomic absorption spectroscopy). The elements are detected as mass per charge for each isotope (m/q) with most elements only singly ionized (+1).
Sample preparation involves complete cleavage of covalent bonds by digesting a small tissue sample in concentrated acid. This reaction occurs within a specialized microwave. There are other methods such as laser ablation in which a laser beam will blast particles off of sample into the nebulizer. After digestion the clear solution is pumped through a nebulizer, creating an aerosol of sample with argon gas. The spray chamber then discriminates droplets according to size, allowing those that are appropriate for the plasma torch to 31 pass. The droplets are heated by radiofrequency waves into a plasma flame, which atomizes and ionizes the sample.
There are two types of mass spectrometers. The simplest form is a quadrupole analyzer. The quadrupole mass spectrometer has a mass filter consisting of four electrodes, two with DC and two with AC electric potential. The voltages are set such that only one mass per charge may pass through the mass filter into the electron multiplier and detector.
A more sensitive instrument (higher mass per charge resolution) is the sector mass spectrometer (used in Chapter 9). The sector mass spectrometer was used for this dissertation research to reduce the chance of error in measurements due to spectral overlaps that are (eliminated with higher m/q resolution). The plasma is formed in the same manner as described before.
However, the sample then passes into a magnetic and an electronic sector. The magnetic sector is a small magnet, which deflects the ions according to their momentum. This is selective for the m/q to be measured. The electronic sector then deflects the sample according to kinetic energy to increase the resolution.
Only the specified ions are allowed to pass to the multiplier and detector. This instrument is able to detect elements that are not resolvable by other instruments due to spectral interferences with the solvent or other chemicals within the matrix.
A technique related to ICP-mass spectroscopy (ICP-MS) is ICP- optical emission spectroscopy (ICP-OES, Olesik, 2002). While not used in the study of iron content (due to spectral overlaps), this technique is useful for the 32 analysis of many other elements. The formation of a plasma sample is similar to that described above. In OES the outer shell electrons of the sample are excited into higher energy states. When the sample relaxes back into the ground state, a wavelength of light that is emitted (hν) and detected. There are disadvantages to
OES as compared to MS. Most importantly is that spectra emitted in OES are not discrete values, like m/q. Therefore, mass spectroscopy leads to more precise measurements. Also, matrix contributions have a larger effect in MS compared to OES.
The final method to be discussed is atomic absorption spectroscopy (Haswell, 1991). This technique again begins with an acid dissolved sample that is entered into the instrument through a nebulizer (which also adds a fuel for ignition). However, in atomic absorption the sample is ignited to break the sample into non-charge elements in the ground state. There is a lamp source, specific to each element, that emits light of a specified wavelength.
The light absorbed is proportional to the concentration of the element in the sample (detection limit of parts per million). Each element to be analyzed requires that a different lamp making this a very costly method. In OES or MS, several elements can be analyzed at a time with little user involvement.
There are many other methods that can be used; however, these are common techniques in the study of Alzheimer’s Disease tissue.
33 LOCALIZING IRON
The distribution of iron also provides useful information. Chemical reactions on prepared tissues allow for a staining in areas with iron. Perls’ staining is one of the most popular for this sort of tissue evaluation and has many names (Prussian Blue, Gomori, etc) depending on the concentration of reactants.
It is possible to adapt this technique for identification of ferrous and ferric ions. It is also possible to amplify the signal with diaminobenzadine (see below).
Tissue samples must be prepared in a manner that will allow them to maintain their structure but not interfere with the chemistry. The Ohio State
University pathology department will harvest tissues within hours of expiration of the patient. Tissues are placed within a fixative solution for approximately three weeks. This is typically a 20% buffered formalin solution; however, in our studies, formalin was replaced by methanol (see below). In either circumstance, the tissues are embedded in Paraffin, sectioned at a 5 µm thickness, placed onto glass slides, and deparaffinized in xylene and graded alcohol.
Formalin has been shown in many histological and magnetic resonance studies to leach iron from tissues (Dobson et al., 1996; Lauffer 1992;
Vymazal et al., 1996b). This is due to the increased acidity of the fixative solutions over time. Other fixative solutions have been shown to maintain the tissue integrity and iron distrtibution (Methacarn, Methanol, Carnoy; Puchtler et al., 1970). Therefore, visualization of small iron deposits is easier when they are maintained with an alternative fixation method.
34 Perls’ staining involves the conversion of iron into a visible blue. An example of this staining is shown in Figure 3.2, which is from a control section of liver. The procedure (Smith et al., 1997b) includes incubation of prepared tissue slides for 15 hours in 7% potassium ferrocyanide (trivalent ion) or 7% potassium ferricyanide (divalent ion) and 3% hydrochloric acid (or incubate at 37°C for 1 hour). For staining of both oxidative states, the slides are placed into a 2% ammonium sulfide solution for 18 hours prior to staining with 10% ferricyanide.
Conversion is a double replacement reaction and is shown by Equation 3.1 (the ferric ferrocyanide is blue in color (Kiernan, 1999)).
4FeCl3 + 3K4Fe(CN)6 → Fe4[Fe(CN)6]3 + 12KCl Equation 3.1
The chemical reaction for Perl’s stain results in the formation of
Fe4[Fe(CN6)]3, which is the visible blue ferric ferrocyanide.
35 Figure 3.2: Sample section from a liver specimen stained with Perls’ stain. The liver is abundant with iron and so all cells were stained blue. This is often used as control for this staining procedure.
Diaminobenzadine (DAB) enhancement of the Perl’s staining method for detection of iron was developed in 1980 (Nguyen-Legros et al., 1980). DAB was used for the amplification of lightly stained regions of iron accumulation. This procedure has been used by several groups to detect small accumulations even after fixing tissues in formalin (Connor et al., 1992a; Connor et al., 1992b;
Dobson et al., 1996; Smith et al., 1997a; Perry et al., 1998; Smith et al., 2000a;
Smith et al., 2000b; Dobson, 2001). To perform this procedure, the Perl stained slides are incubated in 0.75 mg/mL 3,3’-diaminobenzidine (DAB) and 0.015% hydrogen peroxide for 5-10 minutes resulting in a brownish-black precipitant in the area of iron accumulations.
Localization of proteins, such as albumin, ferritin, transferrin, etc. requires an antibody or specific protein assay. These techniques are not discussed here. 36 This concludes the discussions of iron and its importance to the research within this dissertation. Iron homeostasis is vital to the well-being of living systems due toxicity that may result in cell death and is the center of investigations of diseases. Iron also provides a unique contrast in MR images that will be discussed in Chapter 4.
37 CHAPTER 4
IMAGING IRON
This chapter is dedicated to discussion of basic MRI acquisition methods. Emphasis will be placed on paramagnetic effects for tissue iron.
INTRODUCTION TO MRI
Magnetic Resonance Imaging (MRI) is the use of the Nuclear
Magnetic Resonance (NMR) to develop a spectrum or multi-dimensional image of an object. In the standard case, an image is the result of the how the water molecules of that object have interacted with the magnetic field and how those molecules were manipulated by the parameters that were set by the investigator running the experiment. While NMR is frequently used in chemical research for determination of compound structure via spectra, the basic principle is the same for MRI. Both involve a large magnet (typically superconducting), magnetic shims for adjusting the homogeneity of the magnetic field, magnetic field, gradient systems, radiofrequency (RF) coils for sending and receiving signal, and computers that are all coordinated by a central console unit. The hardware of the system will not be discussed further.
38 The reader is referred to either an organic chemistry book (such as
Solomons, 1996) for the basics of NMR or to a Magnetic Resonance Text
(Haacke et al., 1999; Vlaardingerbroek et al., 1999) for more information. The basic principles that will be used throughout this dissertation are discussed here.
Magnetic field is measured in either gauss or tesla (T) units. A tesla is defined as 10,000 gauss where a refrigerator magnet is about 50 gauss and the Earth's magnetic field is about 100 times less than that.
The nuclei of atoms are composed of protons and neutrons each with their own quantity called spin. In cases where the number of protons and neutron is both even, the spins cancel and the net magnetic moment of the nucleus is zero. In other cases, there is a net nuclear spin within a magnetic field and the nuclei can exist in more than one state. These nuclei are moving, positively charged particles that are thought of, in the classical sense, as spinning tops representing a magnetic moment that precess about an axis perpendicular to the magnetic field. Outside of the magnetic field, the orientation of these spins is random. However, once placed within the magnetic field, the spins will align either with or against the magnetic field. For those atoms with two possible spin states (1H, 19F, etc) one state (α) is the lower state parallel to the main magnetic field (B0) while another (β) is a higher spin state antiparallel to the field. It is possible for the spin to exchange between these two states by either absorption or release of energy.
In an MRI experiment, the sample is placed within the center of the magnetic field. The nuclei of interest will begin to precess in either one of the two 39 states (with the greatest population of spins possessing the lower spin state) according to the Boltzmann distribution.
NMR Spins≈Nhω0/2kT Equation 4.1
Where N is the total number of spins, h=Planck’s constant divided by 2π, ω0 is the Larmor frequency, k is the Boltzmann constant, and T is temperature.
Those spins within the energetically higher state (β) are MR active.
The nuclei frequency of precession (ω0, Larmor frequency) is directly related to the strength of the magnetic field, B0, with a proportionality constant called the gyromagnetic constant, γ (unique to each nuclei with hydrogen being equal to
42.56mHzT-1).
ω0=γB0 Equation 4.2
In order to obtain an NMR signal from the active nuclei, a radiofrequency pulse is applied to the sample at the nuclei’s Lamor frequency.
Depending on the radiofrequency pulse (i.e. length and intensity), the net magnetization can be deflected away from its axis parallel to B0 to any angle.
40 This angle of deflection away from B0 is the flip angle, α. By vector addition, this yields magnetization in the x-y plane (transverse plane) and some along the z- direction (longitudinal plane) depending on the chosen α. The net magnetization will then precess about α, as seen in Figure 4.1
α
B1
B0
Figure 4.1: Diagram demonstrating the tipping of net magnetization (Blue arrow) out of the longitudinal plane into the transverse plane by an angle, α. The dotted lines represent the relative amounts of magnetization in the longitudinal and transverse planes. This is in the rotating reference frame.
This excited state of the spin can then relax in two fashions. First, the net magnetization can return to the direction of B0, called longitudinal or T1 relaxation. The other relaxation occurs when the coherence of the net magnetization within the transverse plane is lost, called T2 relaxation. T1 41 relaxation will not be discussed further, as the investigation of transverse magnetization is the focus of this dissertation.
The loss of coherent net magnetization in the transverse plane occurs through the dephasing of spins. This is diagrammed in Figure 4.2 below.
Figure 4.2: Loss of conherent net transverse magnetization, T2.
This T2 is related to the distribution of spin phases. Many factors
(magnetic homogeneity, susceptibility, etc) influence the phase and are further explained in below.
But first, the property of magnetic susceptibility must be discussed.
Magnetic susceptibility is a characteristic of every object and describes the way that a magnetic field is altered by the presence of that object. It may be either negative (diamagnetic), positive (paramagnetic), or zero (nonmagnetic).
Paramagnetic objects draw magnetic field lines toward itself (strengthening the 42 magnetic field locally), diamagnetic objects divert the magnetic field away from itself (locally weaker magnetic field), and nonmagnetic would not affect the field.
In Figure 4.3 is a diagram of magnetic susceptibility. Iron and other metals are paramagnetic (defined by their unpaired electrons) while bodily tissues are diamagnetic because of the large water content.
Figure 4.3: Magnetic Susceptibility. The N and S blocks are north and south poles of the magnetic field, respectively. The sphere within the center is an object of labeled susceptibility. The magnetic field lines are drawn between the north and south poles.
43 These basic principles of MRI are used in discussions of dephasing mechanisms that will lead to changes in transverse relaxation.
DEPHASING MECHANISMS
The loss of coherent magnetization in the transverse plane can occur by many mechanisms. As the spin precess in the transverse plane, there will be external influences that will lead to dephasing. Acquired phase of a spin is related to the Lamor frequency, changes in magnetic field (ΔB), and time.
φ(t)=γΔBt Equation 4.3
Where φ is the time dependent phase of a spin, γ is the gyromagnetic constant,
ΔB is the change in magnetic field due to several factors (explained below).
Changes in the magnetic field can have several forms, each of which may contribute to the dephasing of the net magnetization. These mechanisms are summed into the ΔB value of Equation 4.3.
MAGNETIC HOMOGENEITY
Magnet design is vital to the quality of an MRI signal. Shim gradient hardware is included in each MRI system. The purpose of the shim gradient is to alter the magnetic field after the placement of a sample so that the
44 field is homogeneous. While this is not perfect, the imperfections are small and can be neglected over the size of a single voxel.
IMAGING GRADIENTS
Localization of a spin within a three-dimensional space was an engineering idea by Lauterbur that resulted in the concept of medical MR imaging. This is accomplished by the application of magnetic gradients in orthogonal planes. The spins along the gradient will precess at slightly different rates. The signal detected from each spin can be related to the location by the known gradient strength.
Magnetic Gradient
Figure 4.4: Magnetic gradients used in the localization of spins. The precession of the spin is altered by the amount indicated by each red arrow at that position along the gradient.
45 As a result, the location of each spin can be determined. However, the gradient also leads to increased dephasing of the net magnetization. Effects and compensation of gradient dephasing will be further discussed in a later section of this chapter.
MACROSCOPIC SUSCEPTIBILITY
Susceptibility effects that occur over the distance of several voxels are known as macroscopic. This is the result of materials with large magnetic susceptibility difference imaged while adjacent to one another. The most common demonstration of this effect is at air/tissue interfaces. Regions of the brain near the inner ear and the chest cavity are both plagued with the signal loss and distortion that can result. This is also the mechanism involved in the BOLD
(Blood Oxygenation Level Dependent) signal measured in Functional MRI
(Ogawa et al., 1990).
MESOSCOPIC SUSCEPTIBILITY
Intravoxel dephasing mechanisms are called mesoscopic and will be the focus of discussions in the dissertation.
Regardless of the crystalline or oxidative nature of the iron (even if it is not natural but a contrast agent) it is capable of imposing a mesoscopic susceptibilty effect on the MR signal. For example, a small iron particle can be approximated as a spherical object with a magnetic moment, µm, as a function of susceptibility (Haacke et al., 1999). 46 3 Equation 4.4 µm=4π/3a M(χ)z
Where µm is the calculated magnetic moment, a is the radius of the iron particle,
M is the susceptibility (χ) induced magnetization, and z is the unit vector in the z direction.
Magnetic dipole moments induce a change in the magnetic field
(ΔB) that is a function of the inverse cubed distance (r) from the particle (Haacke et al., 1999). µ0 is the constant of permitivity and r is a unit vector in the direction of r.
3 Equation 4.5 ΔB=µ0/4*[(3µm≅r)r-µm]/r
Therefore a particle of iron oxide is capable of influencing the magnetic field beyond its spatial size.
This is further diagrammed in Figure 4.5. In this example, derived from Equation 4.5, a 1 µm particle can provide a 22 mGauss change in the main magnetic field up to 100 µm away in the direction of the magnetic dipole.
Therefore, the 1 µm size particle could influence a volume up to 1 million times
(ten times R) its actual volume.
47 Figure 4.5: Showing the partial volume effects of a sphere of radius, r, influencing a volume that is 1 million times its actual volume (Haacke et al., 1999).
The mesoscopic susceptibility effects are further described by the motion narrowing and static regimes. Kennan (1994) and Hardy (1991) evaluated the effects of spherical perturbants and gradients respectively, and their influence on the diffusing and static spin. The motion narrowing regime is defined as when rate of diffusion is slow and dephasing is a result of inhomogeneities. When spins are free to diffuse, the susceptibility gradient effects are averaged out. The slowly varying gradient of the motion narrowing regime broadens the linewidth in the frequency domain. This has been simulated and resultant T2 calculated by Hardy (1991). Kennan (1994) and Weisskoff
(1994) both use computer simulation to calculate T2 from the dephasing of spins.
48 MICROSCOPIC SUSCEPTIBILITY
Microscopic susceptibility effects refer to those that are due to molecular interactions. These are known as inner and outer sphere effects and they influence the phase and thus T2 (Lauffer, 1987; Caravan et al., 1999). Inner sphere is the direct coordination of a water molecule to a paramagnetic element.
For example, Gadolinium-DTPA allows for the coordination of one water molecule to the gadolinium atom. There is a transfer of magnetization and increased dephasing due to this inner sphere interaction. Outer sphere interactions would be the influence of hydrogen bonding of water molecules to
DTPA (in this example), which is also referred to as second sphere. The translational diffusion of spins past the paramagnetic element, without coordination, gives rise to pure outer sphere interactions. These mechanisms influence the development of contrast agents in aqueous solutions.
BLOEMBERGEN-PURCELL-POUND THEORY
Finally the atomic level influence on dephasing. These effects are due to the intra-atomic quantum interactions that occur. Including the homo- and heteronuclear interactions, chemical shift and j-coupling (through bond interactions), and dipole-dipole (through space) interactions. Chemical shift is the frequency shift of the spectra of the nucleus. This may be due to electronic interactions or other nuclear interactions. J-coupling is the interaction between two nuclei that are within a few bonds of one another. This results in a splitting of 49 the NMR spectra where the distance of the splitting is the coupling constant, J.
The nuclear Overhauser effect (NOE) transitions are dipole interactions (nuclei do not have to be bonded to one another) and involves the transfer of polarization. This is often a heteronuclear experiment used to enhance the signal of the desired nucleus.
DEPHASING TO RELAXATION
The translation of dephased spins into transverse relaxation time calculations is not easily accomplished. There are studies that have used computer simulations to estimate these behaviors for relaxation time measurement schemes. This involves numerically solving the Bloch equations as already alluded to in the mesoscopic effects section (Kennan et al., 1994;
Weisskoff et al., 1994). Yablonskiy (1994) uses a statistical theory while Hardy
(1991) considers numerical simulations to describe how dephasing of spins will effect T2 and T2*. T2 and T2* will be discussed in greater detail in the following sections in the context of MRI acquisition methods (i.e. pulse sequences).
MOVING TO 8 TESLA
Returning to the Boltzmann distribution, we can realize the importance and significance of increasing field strengths. As the magnetic field strength is increased, the Lamor frequency increases, and so does the number of spins that is in the NMR active state increase. Therefore, there are more spins that are accessible for the MRI signal. From this, it is easy to see that an 50 advantage of higher field strengths is increased Signal to Noise Ratios (SNR).
Increased signal provides opportunity to increase the resolution of the acquired images (potentially 100x100 µm2 in-plane). Smaller anatomical regions could be resolved that are not observed at 1.5 T field strength. The 8 T MRI would therefore allow small anatomical features, such as the hippocampus for AD, to be resolved well enough to minimize CSF partial volume effects.
Relaxation rates are different at higher field strengths as well.
Generally this includes increased T1 and decreased T2 and T2*. Therefore, contrasts are very different and may aid in the development of certain diagnostic tools. Increased T1 values for soft tissues hinder in vivo study progress at high fields because of the required increase in relaxation time (TR) for pulse sequences. Proper setting of T1 lengthens acquisition times and increases the chances of patient motion.
Magnetic susceptibility effects are also field dependent. And so it follows that if iron is associated with the plaques of Alzheimer’s disease, the 8 T system would be particularly sensitive to the dephasing mechanisms discussed.
This results in both a benefit and disadvantage to high field. That is, the susceptibility induced contrast enables the visualization of small vascular structures and increased sensitivity to iron deposits. However, susceptibility induced artifacts and distortions are also enhanced.
Finally, the higher field strength results in shorter RF wavelengths.
At 8 T, these wavelengths are similar to the size of the human body resulting in unique constructive and destructive interference patterns within the excitation of 51 the human head. Coil engineering and design remains one area in which research is on going.
The 8 T system at the Ohio State University is a novel instrument having the highest field strength for a MRI with a bore size large enough for full human body imaging. It is a fully functional system controlled by a Bruker
Avance console running Paravision software. Using the largest MRI magnet in the world, it allows access to many of the concepts and effects discussed throughout this chapter. This puts the MRI research group in a unique position to explore all of these outcomes with higher resolution and clarity.
FREE INDUCTION DECAY
The free induction decay (FID) of the signal after excitation will be dependent on all of the dephasing mechanisms described above. Therefore, it is not feasible to separate the effects of the different mechanisms.
FID PULSE SEQUENCE
Thus, the most simple pulse sequence consists of an RF excitation pulse (blue in Figure 4.6) and acqusition of the FID signal for some time.
52 RF
Acquire exp(-t/T2*)
Signal
Figure 4.6: Diagram of a pulse sequence used in sampling the Free Induction Decay.
This method is typically used in spectroscopy measurements with chemical NMR systems.
T2*
As individual spins are exposed to locally different fields ΔB(x,y,z) they will dephase over time by
Δφ(x,y,z)=γΔB(x,y,z)t Equation 4.6
53 where the locally variable ΔB and thus Δφ is the sum of all above listed effects.
The net magnetization M(t) then decreases because the vector sum becomes smaller due to dephasing.
M(t)%ΣkΣlexp[-iΔφk(xl,yl,zl)t] Equation 4.7
In a simplified form, the dephasing is described by a decay constant, T2* such that
M=M0exp[-t/T2*] Equation 4.8
The T2* exponential signal decay. M is the signal at time, t. The pulse sequence for this measurement would include the RF pulse (angle set to maximize signal) and a readout gradient.
The measurement of T2* is based on the time dependent decay of the FID. It is also defined as the line-width at full width at half max (FWHM) in the frequency domain (or in the NMR spectrum), i.e. the Fourier transform of the
FID signal.
54 exp(-t/T2*)
Fourier ↑ Transform Signal Time → Frequency →
Figure 4.7: T2* definitions. The FID is sampled at various TEs by refocusing the dephased portion. After a Fourier Transform into Frequency domain, 1/T2*, R2*, is the FWHM (indicated by the red line) on the spectrum.
T2* may also be defined as the inverse of the inverse sum of T2
(irreversible, explained below) and T2’ (reversible) dephasing measurements.
R2*=R2+R2’ Equation 4.9
This explains again that T2* is a combination of the microscopic transverse relaxation of spins and the increased relaxation due to field inhomogeneities due
55 to the reasons listed above. T2’ is discussed in more detail below with the
GESFIDE (gradient echo sampling of FID and echo) sequence. The use of T2* on the analysis of AD brain tissues is discussed in Chapter 5.
GRADIENT ECHO SEQUENCE
A basic schematic of a typical gradient echo sequence is shown below in Figure 4.8. The slice and phase encode gradients have been eliminated from the diagram for simplicity.
The gradient echo sequence is in essence an imaging implementation of the simple FID sequence where the FID signal decays under the influence of the first gradient 1. If the second gradient, 2a, with equal length and amplitude as 1 but inverse polarity is given next, this gradient will undo the dephasing of the first gradient, 1, and an echo will form at time, TE, determined by the placement and length of the gradients. Leaving the so-called read out gradient on, 2b would dephase the signal again. Data are collected during gradient 2.
56 TE
RF
Readout 1 Gradient 2a 2b
T2*
Signal
Figure 4.8: Simplified schematic of the gradient echo sequence. After the RF excitation pulse, the redout gradient is used to sample the refocused portion of the FID. The green dashed decay represents T2* plus gradient dephasing.
T2
T2, also known as the transverse relaxation constant, is often measured using a spin echo sequence. Rather than measuring the FID, as in the gradient echo, T2 is a measure the dephasing of spins in a spin echo sequence. The equation is similar to 4.8, but the time constant is T2 rather than
T2*.
57 exp(-t/T2*) exp(-t/T2)
↑ Signal Time →
Figure 4.9: Differences in the measurement of T2 and T2*. The green dotted line traces the decay of the FID, which is measured by a gradient echo. The blue dotted line traces the spin echoes that are used in calculating T2.
There are two methods of measuring T2 that are of interest for discussions in this dissertation. First, the Hahn spin echo (Hahn, 1950) will be discussed. The Hahn spin echo removes the dephasing mechanisms associated with macro and mesoscopic susceptibilities, i.e. the reversible signal loss. This is accomplished by applying a 180° RF refocusing pulse (see below). However, the
Hahn spin echo is still very sensitive to the effects of diffusion. The basic schematic of the Hahn spin echo is below.
58 RF
Slice
Phase Encode Read
Signal
Figure 4.10: Simple schematic of the Hahn spin echo sequence. The time between the excitation and refocusing pulses (1/2 TE) is changed for each acquisition for measurement of T2.
While it is possible to measure T2 using the Hahn SE, it is mostly used for T2-weighted images.
There are two versions of the other spin echo sequence of interest for this dissertation. Carr and Purcell (Carr et al., 1954b) adjusted the Hahn spin echo sequence by the addition of multiple refocusing pulses (the CP spin echo).
These pulses are applied along the same direction as the initial 90° excitation RF pulse. The time between the excitation pulse and the first refocusing pulse is known as τ. Therefore, the first echo occurs at 2τ. In the circumstance where n number of refocusing pulses are used, there will also be n number of images acquired. The TE of each image will be n*2τ.
59 τ τ 2τ 2τ
RF
Slice
Phase Encode
Read
Signal n=1 n=2 n=3
Figure 4.11: Example CP spin echo. The refocusing pulses are applied along the same axis as the excitation pulse.
Meiboom and Gill (Meiboom et al., 1958) have adapted the CP sequence for the reduction of susceptibility effects in the presence of inhomogeneous B1 fields (the CPMG spin echo). The CP sequence was plagued with the significant loss of transverse magnetization due to non-180° refocusing pulses. This is diagrammed in the figure below:
60 z’
φ y’ 2φ
x’
Figure 4.12: Diagram of the refocusing of spins with a CP spin echo in the rotation plane. After the first imperfect 180°, the spins are not within the transverse plane by an angle φ, where φ is the angle of the refocusing applied. After the second imperfect 180°, there is angle of 2φ difference. Therefore, after n pulses the error would be nφ.
To correct this problem, Meiboom and Gill apply the refocusing RF pulse along the –y direction (rotating frame).
61 z’
y’
Figure 4.13: Diagram of the CPMG spins. The x’ plane is extending out of the surface of the page and is the direction of the spins after the excitation pulse. Just before the refocusing pulse, the spins have dephased in the x-y plane. After the first 180° pulse, the spins are within the plane designated by the red line. However, the spins will now rephase in the direction of the green arrows onto the x’-axis. The red line represents any arbitrary refocusing angle.
The primary difference between the CP and CPMG sequences is the direction of the magnetization after the refocusing pulses. In CP, if the initial magnetization is in the +y’-direction, then after the first refocusing pulse the magnetization will be in the –y’-direction. Finally, the magnetization would return to the +y’-direction after a second 180°. However, in the CPMG sequence, the magnetization would remain in the +y’-direction after each pulse.
The benefits of the CPMG (or CP) sequence over the Hahn spin echo is that the data set required to fit a mono-exponential decay for T2 calculation may be obtained in one pulse sequence/acquisition. This significantly decreases the scan time (Hahn requires an acquisition for each TE). However, the CP and CPMG sequences are not as sensitive to the effects of diffusion.
62 Another difficulty with these multiple refocusing pulse sequence is the amount of
RF power required for the train of 180°. Several authors have related T2 to tissue iron content (Schenck 2003; Drayer et al., 1986; Ye et al., 1996, Bartha et al., 2002) but this is discussed in greater detail in Chapter 5.
γ2G2D
As was noted above, Hahn spin echoes are dependent on spin diffusion in an inhomogeneous magnetic field. The CP or CPMG sequences reduce this dephasing. During the CPMG sequence, the constant refocusing of the spins does not allow for the accumulation of susceptibility and gradient effects. That is, τ is typically short and does not allow for the accumulation of phase differences. The Hahn spin echo, by contrast, allows the spin to experience these effects over a longer TE (dephasing period) without multiple refocusing steps. Hahn is therefore more sensitive to the influences of susceptibility gradients from iron.
In 1954, Carr and Purcell (Carr et al., 1954a) derived an equation to describe the signal for spin echo sequences dependence on a linear gradient in an imperfect magnetic field and spin diffusion.
2 2 3 2 S = S0 exp ((-t/T2)-γ G Dt ) / (12n )) Equation 4.10
63 Where γ is the gyromagnetic constant, G the linear gradient strength, D the diffusion term, and n the echo number. For Hahn SE, n=1.
Acquisition of a series of Hahn spin echoes at various TEs and a short τ-CPMG sequence allow for a combined spin echo analysis using this equation. This technique, while not time efficient, does allow for the isolation of the γ2G2D term, which could be correlated with total iron content and an intrinsic
T2 value for the tissue.
It has been observed that Hahn and CPMG spin echoes do not yield the same T2 values (Whitaker et al., 2001; Whitaker et al., 2002; Whitaker et al., 2003a). CPMG has specifically shown this dependence on the selection of
τ (Ye et al., 1996a; Ye et al., 1996b; Bartha et al., 2002).
Finally, the diffusion term and susceptibility gradient term cannot be separated in the combined fit of γ2G2D. It may be possible to acquire diffusion maps and use them to isolate the gradient susceptibility effects.
The motion narrowed and static regimes may be useful in understanding this term (Hardy et al., 1991; Weisskoff et al., 1994; Yablonskiy et al., 1994; Reichenbach et al., 1997). Further explanation is made in Chapter 7 with reference to that data.
64 FDRI
Field Dependent R2 Increase (FDRI) is a method that was developed by Bartzokis and is based on the dependence of the field strength on
T2 measurements.
FDRI=1/T2high field – 1/T2low field Equation 4.11
This method is dependent on field dependent dephasing mechanisms and has been correlated to non-haem iron, i.e. specifically ferritin.
Bartzokis (1993) concludes that regions such as white matter do not demonstrate large FDRI values as these regions have field independent dephasing mechanisms. For example, Bartzokis indicates that the dephasing effects of an iron solution phantom are field independent while a ferritin solution phantom effects are field dependent (total iron content was constant in both phantoms).
The protocol by Bartzokis includes a dual-echo CPMG with TR/TE=2500/20,90 ms at two field strengths (0.5 and 1.5 T). Several localizer sequences are acquired so that slice selection is consistent between field strengths.
Bartzokis has studied in vivo regions (correlation with Hallgren’s equations; Hallgren et al., 1958; Bartzokis et al., 1993), Alzheimer’s Disease
(Bartzokis et al., 1994a; Bartzokis et al., 2000b), Huntington’s Disease (Bartzokis et al., 2000a), and aging (Bartzokis et al., 1994b; Bartzokis et al., 1997). Parsey
65 (1997) has also used this technique for Alzheimer’s disease and also concluded that FDRI increased in the red nucleus, putamen, and left frontal lobe. Finally
Schenck (1995) used this method at 1.5 and 4 T with the Hahn spin echo to consider the red nucleus, substantia nigra, and midbrain in normal controls.
Rather than reporting his results as a difference in field strength, Schenck used a percent increase approach.
GESFIDE
GESFIDE (Gradient Echo Sampling of FID and Echo) is a sequence that allows for the simultaneous collection of R2, R2’, and R2* that was developed by Ma and Wehrli (1996). The sequence uses a train of gradient echoes to sample the descending and ascending parts of the Hahn echo (before and after the refocusing pulse).
66 RF
Slice
Phase Encode Read
%exp[-(R2+R2’)t] %exp[-(R2-R2’)t] Signal
Figure 4.14: Schematic of the GESFIDE pulse sequence. The signal decay (red line) is used to measure R2* and is equivalent to sampling the gradient echo FID. The refocusing of the signal (green line) is also sampled and is equivalent to R2-. It is possible to derive R2’ from these measurements.
From the rephasing component, it is possible to measure R2- (≡R2-
R2’) and from the dephasing component R2* (≡R2+R2’). This sequence allows for the rapid measurement of R2* and derivation of R2’. R2’ is defined as the irreversible dephasing mechanisms and equivalent to R2-R2*. This has been shown to correlate very well with iron distributions. For example, Gelman (199) was used this sequence at 3.0T to relate both R2 and R2’ with iron content and found that R2’ correlated better with iron content in gray matter than white matter. 67 Also they suggest that R2’ is less sensitive to differentiating gray and white matter than R2. This is not surprising since the total iron content of gray and white matter is not significantly different.
Truong (2004) has implemented this sequence and another variant of this approach, i.e. the GESSE sequence at 8 T. The Gradient Echo Sampled
Spin echo (GESSE) sequence measures R2 only and is less sensitive to B0 and
B1 inhomogeneities. This latter feature makes the sequence very appealling for
8 T. For additional information about this work, please refer to the Truong (2004) doctoral dissertation.
PHASE IMAGING
Phase imaging has been useful in the visualization of small blood vessels by exploiting the blood oxygen level dependent (BOLD) effect and macroscopic susceptibility. Data acquisition for phase images is simple and uses a standard gradient echo. Typically a long TE (on the order of T2*) is used to allow dephasing of the spins for the region of interest. A magnitude image is constructed from the square root of the sum of squares of real and imaginary for each pixel. The phase image is the tangent of the quantity imaginary divided by the real. This method is discussed with respect to visualization of vascularity by
Reichenbach (2000). However, the macroscopic effects used for vessel phase imaging apply to the effects of iron deposits as well.
Specifically, phase imaging allows for differentiation of various structures based on their magnetic susceptibilities. Paramagnetic substances 68 imposing a stronger field on surrounding protons, and will induce a difference in phase compared to materials that are diamagnetic.
Phase contrast has been used in vascular imaging utilizing the paramagnetic nature of deoxyhemoglobin. The increased phase change around vessels allows for excellent contrast compared to surrounding tissues. At 8 T, this effect is dominant and Dr. Abduljalil has implemented this technique
(Abduljalil et al., 2002a, b; Chakeres et al., 2003).
Figure 4.15: A) Magnitude in situ image and B) Phase image corresponding to magnitude image in A. In the magnitude image the nuclei (putamen and substantia nigra) and some vessels are well defined. There is good gray and white matter contrast as well. In the phase image, the gray-white contrast is lost and has similar signal to the CSF of the ventricles. The putamen is still prominent and so are many of the large vessels. In addition, there are smaller vessels that are now visible. The hippocampus is also distinguishable against the CSF.
69 The soft tissues of the in situ brain have little to no contrast (these are both diamagnetic) while vessels and nuclei (paramagnetic) are very well defined. In situ phase images are difficult to visually inspect initially because there is a loss of much of the anatomical contrast while the in vivo images maintain gray and white matter contrasts (see example in Figure 4.12). This loss of contrast between in vivo and in situ is not yet understood. In situ vessels should contain
100% deoxy-hemoglobin, which is more paramagnetic than oxy-hemoglobin in vivo. This would seem to indicate that 1) there is a disproportional increase of deoxy-hemoglobin in gray matter than white matter in situ or 2) another mechanism is altering the phase contrast in gray and white matter in vivo.
Other examples at 8 T include studies of carcinoma vessels
(Christoforidis et al., 2002; Novak et al., 2003), general vascularity observations by for in vivo and in situ images by Dashner (2003), and other studies.
70 A B C
Figure 4.16: Figure 1 from Dashner (Dashner et al., 2003) where IP=intraparenchymal vessels, PC=perforating cortical vessels, and LM=leptomeningeal vessels. A) In vivo, B) In situ, C) In situ embalmed images.
This brief overview of the techniques used in the evaluation of total iron content using MRI demonstrates the dependence on finding a method that identifies the susceptibility effects of iron and does not include other tissue factors. The other factors could include increased fluid content due to edema or atrophy or alteration in the consistency or viscosity of the tissues. While there are various methods that have been used, our choice of the combined analysis was primarily based on the hypothesis that the γ2G2D term may be more strongly dependent on iron content, and less dependent on other factors (i.e. water content and viscosity) though these other factors influence the diffusion constant,
D.
71 CHAPTER 5
IMAGING ALZHEIMER’S DISEASE
One of the early targets of Alzheimer’s disease is the medial temporal lobe, specifically the entorhinal cortex and hippocampus. These regions are difficult to resolve at 1.5 T. For example, most volumetric studies consider the hippocampus but not the entorhinal cortex while relaxation studies look into regions not necessarily involved with AD.
MRI studies have evaluated spectroscopic measurements, volumetrics, magnetization transfer, and relaxation time measurements. The hope is to discover a method of tracking the progression of dementia so that the effectiveness of developing therapies can be tested for their ability in slowing the progression or reversal of disease.
VOLUMETRICS
While the high resolution attainable at high field would result in better differentiation of anatomical regions for volumetric studies, we did not focus on these measurements for our studies. Atrophy occurs after the accumulation of neuritic plaques and neurofibrillary tangles and the loss of
72 neurons. Therefore, volumetrics would not be the earliest predictor of dementia and we chose to focus on a potentially earlier feature of AD (that is if iron is always associated with plaques). Finally neurons cannot be recovered under current medicinal practice, but this is being tested (Sugaya, 2003). MRI volumetric studies are being performed on a regular basis at standard field strengths (≈8 T) and typically consider the entire hippocampal region (Detoledo-
Morrell et al., 1997; De Toledo-Morrell et al., 2000; Mizuno et al., 2000; Wolf et al., 2001). This is unfortunate as regions such as the CA1, which are not resolved at 1.5 T, have been shown to lose neurons more quickly than other regions (West et al., 1994; Bobinski et al., 1998). Some studies have not focused on the entorhinal cortex (Barber et al., 2002; Hampel et al., 2002) because of the small size of the region complicated by extreme atrophy.
Logistical issues with sectioning tissues (Luft et al., 1998; Wang et al., 1998b) need to be addressed where investigators are always trying to find methods of reducing bias in outlining anatomy, partial volume effects, and defining anatomy
(cellular layers are not resolved with MRI and are necessary to proper delineation of anatomy). The typical protocol for volumetric studies involves a collection of
T1-weighted gradient echo, multi-slice images that are aligned perpendicular to the long axis of the hippocampus, perpendicular to the anterior/posterior commissure plane, or parallel to the brain stem. The volume is calculated from the known pixel size of the images.
73 SPECTROSCOPY
Spectroscopy of many atomic nuclei is possible for the study of disease. For AD, hydrogen spectra offer an indirect method of measuring neuron viability. N-Acetyl Aspartate (NAA) is almost exclusively found in neurons.
Choline (Cho) is related to phospholipid structure (cellular membranes), creatine
(Cr) is found in neurons and glial cells, and myo-inositol (MI) is found within glial cells. All of these compounds are found within the hydrogen spectra of brain tissues and have been shown to differentiate AD from normal controls (De
Stefano et al., 1999; Schuff et al., 1999; Schuff et al., 2001). Typically a decreased NAA/Cr and increased MI/Cr ratio are observed. Creatine levels show little change between AD and normal controls and therefore present a way of standardizing the populations. There has also been correlation between volumetrics (Schuff et al., 1999) and genetic mutations (Klunk et al., 1998).
Klunk (1998) and Pettegrew (2001) examine 31P spectra in vitro using post mortem tissues. Phosphorus spectra include chemicals associated with neuronal membranes. Decreases in those used chemicals involved in the building of membranes and increases in membrane breakdown byproducts are correlated with neuron loss. The most typical use of 31P spectroscopy is ATP metabolism.
MAGNETIZATION TRANSFER
Magnetization Transfer (MT) is not as widely applied in studies of
AD compared to the other methods, especially volumetrics. In MT studies, an off 74 resonance excitation pulse is used to excite the protons of water located in or on large molecules. This may be accomplished with either a gradient echo or a spin echo sequence. Transfer of protons or water from the bound location to the free water pool will transfer the magnetization as well. In AD, there is a loss of neurons and therefore a loss of proteins and bound water. This results in the decrease of the MT ratio (Ms/M0). So far, AD has only been studied at 1.5 T using MT (Hanyu et al., 2001; Kabani et al., 2002). This would be an interesting study at high field. However, initial work at 8 T phantom studies with agarose and gelatin of both spin echo and gradient echo MT did not yield any differences in ratios.
RELAXATION TIME MEASUREMENTS
Disruption of iron homeostasis of AD has been documented in
Chapter 2. Briefly, pathological markers of AD accumulate in regions of the medial temporal lobe in early dementia. Iron has been shown to be associated with the amyloid plaque (Emmett, 1989; Beauchemin et al., 1998; Sayre et al.,
2000b). Connor (1992a) hypothesized that AD involved the overloading of ferritin’s capacity to store iron. Plus, Vymazal (1996a) has demonstrated linear relaxation rate dependence on the loading factor of ferritin. Therefore, several researchers have hypothesized that the accumulation of plaques will lead to a decrease of tissue relaxation rates.
75 MAGNETIC RESONANCE MICROSCOPY
Magnetic Resonance Microscopy (MRM) has been implemented on small tissue samples and transgenic mouse models for AD. These super high resolution images allow for an almost microscopic view of anatomy. The first to show direct imaging of amyloid plaques was Benveniste (1999) with a 7.1 T, small bore system. MRM results were correlated with pathological slides of the tissues. The hypothesis was that if amyloid plaques are laden with iron, then the mesoscopic susceptibility effects would allow visualization of large plaques with a gradient echo sequence. Others have observed that there is not a T2* effect associated with the amyloid plaque at 11.7 T (Dhenain et al., 2002). They argue that the mesoscopic partial volume effects are not observed at their high field strength. In either study, imaging protocols required from 4_ to over 18 hours.
This renders this technique useless for possible in vivo study of AD at this point in the state of technology.
GRADIENT ECHOES
There is relatively little data on T2*. This is partly due to the location of the hippocampus. That is, the medial temporal lobe is within close proximity to the inner ear and the mouth cavity, inducing macroscopic susceptibility related artifacts and distortions. Drayer (1988a, 1988b) makes note of hypointensities associated with iron in a series of review articles. Small (2000) also used gradient echoes at 1.5 T with an oblique coronal slice (similar to volumetric) with TR/TE of 300/45 ms. Finally, Rutledge (1987) measured T2* in 76 AD with TR/TE=1500-2500/23-120 ms at 1.5 T with coronal images. All of these studies indicated hyperintensities in AD images compared to normal controls.
SPIN ECHOES
Some researchers have used qualitative assessment of images to study Alzheimer’s disease (Rutledge et al., 1987; Imon et al., 1995; Fazekas et al., 1996; Parsey et al., 1998) or hypointensities (Milton et al., 1991). A more scientific approach has also been accomplished by evaluating T2 values.
However, this is not always performed in regions that are affected by AD. Kirsch
(1992) used a Hahn SE to evaluate AD at 0.04 T and found that T2 was increased in the hippocampus compared to normal controls. However, at 0.04 T, the hippocampus would not be fully resolved and the interpretation would be complicated by atrophy. Therefore Kirsch may actually be evaluating partial volume affects with CSF. Petrella offers a review of some relaxation studies
(2003). Laakso (1996) found prolonged T2 at 1.5 T in the hippocampus but the results did not allow for differentiation between AD and normal controls.
Pitkanen (1996) observed that there was some increase in T2 that did not correlate with atrophy of the hippocampus. Finally, at 7 T Huesgen (1993) used a CPMG sequence with TR/TE=2500/10-80 ms to calculate T2 for subfields of the hippocampus. They concluded that regional variability did not occur in the hippocampus and AD could not be differentiated from normal controls by T2.
In non-AD related papers, Schenck offers a review of the theory of the effects of iron on relaxation rates (2003). Thomas (1993), and Pujol (1992) 77 all suggest that their decreased T2 measurements are correlated with areas of increased iron, specifically many of the nuclei. Drayer (1986) indicated hyperintensities on T2-weighted images correlated with brain iron in the globus pallidum, substantia nigra, red nucleus, and dentate nucleus.
Finally, there are studies considering the inter-echo time (τ) on the
CPMG and how this correlates with brain iron. Ye (1996b) and Bartha (using a variation called CP-LASER; Bartha et al., 2002) have both shown that increased
τ resulted in increased susceptibility contrasts in areas of high iron content.
This review of the literary works of imaging AD provides a basis for understanding the need to develop quantitative measurements of dementia.
Many methods have been tested in MRI, but none have gained favor over standard clinical assessment of dementia.
It is for this reason that we believe that 8 T will provide new insight to the capabilities of MRI to diagnosing dementia. Many of these techniques that we have not attempted could be implemented with some effort.
78 CHAPTER 6
ACCURACY OF T2 MEASUREMENTS AT HIGH FIELD
The measurement of T2 relaxation for in vivo study is very useful in understanding and optimizing the contrasts of tissues, but also in the differentiation of normal versus disease states. This is especially true when a disease results in the accumulation of paramagnetic material (such as iron or calcium) or a change in the consistency of tissues (such as the demyelination associated with multiple sclerosis). Some studies have investigated the accuracy of calculated T2 from experimental data compared to computer simulation at low fields. The accuracy of these measurements is dependent on the system performance as well as the homogeneity of the RF magnetic field, or the B1 field, applied to the sample. While the B0 field is dependent on shimming gradient capabilities after the sample has been inserted into the magnet, the B1 field is dependent on the type of RF coil (birdcage, surface, transverse electromagnetic resonator, etc; Ibrahim et al., 2001a). And at high field, the propagation of RF waves within the sample cause complex patterns of constructive and destructive interferences (Christoforidis et al., 1999; Kangarlu et al., 1999).
79 Low field MR has excellent B0 and B1 field homogeneity. This is desirable for the clinical applications to obtain reliable results for diagnosis.
However, the novel high field MR systems (7 T and higher) have the unique problem of engineering RF coils that can provide a homogenous excitation of a non-regularly shaped sample (such as the human head). While several types of coils have been evaluated, the one most commonly used at 8 T for volume imaging is the transverse electromagnetic, TEM, coil. This has been shown to have the best field homogeneity (Ibrahim et al., 2001a; Ibrahim et al., 2001b;
Ibrahim, 2003), but the RF field amplitude is highly variable within the human head .
The most studied cases of spin echoes have been the multi-echo acquisition, specifically the Carr-Purcell (with or without Meiboom-Gill correction) spin echo. The primary differences between CP and CPMG are briefly reviewed here. More detail can be found in Chapter 4. Here there is a train of n 180° flip angles that yields n number of images (Figure 6.1 shows n=3). In the CP sequence, the 90 and 180° pulses are applied along the x-axis. However, due to non-perfect α pulses, there is a loss of transverse magnetization with each successive 2α, refocusing pulse. Meiboom-Gill (Meiboom et al., 1958) then proposed applying the α pulse along the y-axis. Making this adjustment to the initial RF pulse application reduces the loss of transverse magnetization. This series of images is then used for the calculation of a CPMG T2.
80 RF
Slice
Phase Encode
Read
Signal
Figure 6.1: Diagram (simplified) of CPMG sequence. Blue pulses are RF, Yellow is slice select, Red is phase, and green is readout. The last line indicates where echoes occur with decreasing amplitude. By acquiring multiple echoes, the sequence is only acquired once.
However, the original spin echo sequence used only one 180° refocusing pulse. The Hahn spin echo is typically not used for T2 calculation in clinical based research due to the increased time required for image acquisition.
This sequence is also more susceptible to the effects that cannot be refocused completely (i.e. susceptibility gradient and diffusion). Again, a more complete description of this sequence is found in Chapter 4.
81 A B
RF
Slice Phase Encode
Read
Signal
Figure 6.2: Diagram (simplified) of Hahn sequence. Again, Blue pulses are RF, Yellow is slice select, Red is phase, and green is readout. The last line indicates where echo occurs with decreasing amplitude. A) is a short TE and B) indicates a longer TE. These sequences must be repeated in order to obtain enough data for a T2 curve.
Ignoring the influences due to partial volume and diffusion and focusing on those that are due to an imperfect B1 excitation field, it has been shown that the result of non-perfect α and 2α pulses can quickly lead to the formation of spurious echoes. This is perhaps seen best through the use of a phase diagram. When starting with a 90° RF pulse, the spins will begin to lose phase coherence. The application of a 180° at _TE pulse reverses the phase so that as the spins rephase, the echo will occur at TE.
82 180°
90°
Figure 6.3: Phase diagram of a perfect Hahn spin echo. All magnetization is dephasing, refocused at time =_TE, and rephases into an echo at TE.
However, in the case of refocusing pulses that deviate from 180°, only a portion of the dephasing spins are inverted thus leaving a longitudinal component. With this situation, the phase diagrams become more complicated and there are more spin histories that may result in an echo. Magnetization pathways are indicated by subscripts of 0=stored phase, 0*=stored inversion phase, 1=dephasing, and –1=rephasing (Haacke et al., 1999).
83 180° 180° 180°
180° 180°
90°
Figure 6.4: Phase diagram of a CPMG spin echo where there is a perfect 90° and 180° RF pulses. Horizontal lines indicate a longitudinal magnetization component that may be refocused at any later application of a refocusing pulse. The pathway outlined in red is refocused by the first 180°, does not experience the second, and is not completely focused by the third 180°. This results in a spurious echo.
Non-perfect excitation or refocusing RF pulses lead to miscalculation of T2 values in two ways. First, spurious echoes may lead to ghosting artifacts that may overlap with the regions of interest. In some instances, the ghost is superimposed and alters the signal for that particular TE.
84 The other miscalculation occurs when not all magnetization is transferred into the transverse plane. Loss of magnetization leads to decreased signal for a TE and shorter T2s.
There are some methods of improving the CPMG sequence’s susceptibility to inherent RF field inhomogeneity. Most of the methods proposed by Majumdar (1986a; 1986b; 1987), Poon (1992), Crawley (1987), or Sled (2000) involve the use of composite RF pulses to improve the area of excitation or slice selection gradients applied before and after the refocusing pulses. Each correction is described below.
Majumdar (1986a; 1986b; 1987) examines B0, B1, and multi- slice/slice-thickness effects on T2 calculations. The B0 differences will not be discussed since this was not specifically investigated at 8 T with Hahn and
CPMG spin echoes. Multi-slice complications occur because of the non- rectangular slice profile and are described later.
Regarding T2 accuracy dependence on B1, Majumdar (1986a) provides a detailed explanation of the formation of stored, longitudinal magnetization by imperfect 180° refocusing pulses (for CPMG spin echo only).
Through solving the Bloch equations analytically, they have provided a means of eliminating the artifacts resulting from stored longitudinal magnetization that has been reflected into the transverse plane leading to spurious echoes. Majumdar describes the magnetization of the spin echo by the conventional equation with an additional function.
85 MnT(t)=M0exp[-2nτ/T2]fn(θ) Equation. 61
Function describing the magnetization at nth echo by Majumdar that has an additional sinusoidal term describing the loss due to imperfect refocusing pulses.
Values for fn(θ) are shown below in Table 6.1 for a simple RF pulse. τ=time between 90 and refocusing pulse, n=echo number, fn=function dependent on flip angle (θ).
N fn(θ) 1 [1-cos(θ)]/2 2 [1-cos(θ)]2/4 3 [1-cos(θ)]3/8 + [(1-cos(θ))(1+cos(θ))]2/8 + cosθsin2θ/2 4 [1-cos(θ)]4/16 + 3sin4θ/8 + cosθsin2θ(1-cosθ)/2
Table 6.1: fn(θ) for echoes number 1-4 of a CPMG sequence.
Simulating these equations resulted in a calculation of Gx and Gz values that suppressed the phase encode ghosting. Application of these balanced gradients in the slice selection direction around the odd reflection pulses only suppressed ghosting in a mineral oil phantom. Placement of these gradients are diagramed below. 86 RF
Slice
Phase Encode
Read
Signal
Figure 6.5: CPMG sequence diagram shown again, but with the Majumdar crusher gradients applied to the slice direction. Majumdar proposes equal area, balanced gradients before and after the odd refocusing pulses only.
Crawley (1987) also investigated the CPMG multi-slice sequence.
He also offers a different gradient scheme for eliminating spurious echoes. This includes not only calculating G values, but also calculating the most efficient direction in which to apply the gradient. They suggest G values of 0, 1, -1, 2, -
2… for refocusing echoes n=1, 2, 3, 4, 5…, which is an increasing gradient strength with longer CPMG echo trains.
Crawley also discusses the effects of slice thickness. In most cases, the borders of the slices do not receive the full 180° pulse and with successive refocusing pulses, the effectively excited slice becomes thinner
(Crawley et al., 1987). Therefore, Crawley suggests using a thicker slice profile 87 for the refocusing pulse than the excitation slice. He also observes that measurements are more accurate for thin slices when more echoes are acquired and the inverse for thick slices. While the vast majority of our data have been acquired using single slice techniques to reduce cross-talk, some data presented within this chapter includes multiple slices.
Poon (1992) begins with a different problem of typical T2 studies, that is the inadequate sampling of the exponential curve. It is important to acquire enough data to properly fit the exponential prior to developing schemes for the elimination of ghosting. Poon continues by reviewing the methods of
Majumdar and Crawley and added his own modification. This gradient scheme is applied to each refocusing pulse including n=1 and is progressively weaker for the later echoes. They also note that this gradient scheme for suppression of unwanted echoes leads to a decrease in the observed T2 for non-180° pulses.
Another method discussed was phase cycling. Here the phase of the RF pulses is changed for each repetition of the pulse sequence. With the multiple echo acquisition (such as CPMG) the number of sequence repetitions are greatly increased for each echo included. Acquisition time is therefore increased and may be render this method useless for in vivo application.
Rewinding gradients in phase encode causes any ghosting to overlap the imaged object. This provides clinically (qualitatively) useful images but is useless for T2 calculations due to T1 contamination of the MRI signal. Poon also suggests specialized RF pulses to clean up image data. However, adiabatic pulses have
88 the potential to cause increased heating and composite pulses are time consuming (especially true for high field strengths).
Finally, Sled (2000) uses acquired B0 and B1 maps with Equation
6.2, to correct the T2 maps and obtain accurate relaxation measurements.
-1 T2=[1/T2obs+lnf/τ] Equation 6.2
Where T2 is the true tissue T2, T2obs is the apparent T2, f is the attenuation factor based on B0 and B1 maps, and τ is the time between echoes.
The pulse sequence gradient scheme was similar to the one proposed by Poon. There is also a comparison of a rectangular and composite
RF pulse. While other methods were tested only on manganese chloride phantoms, Sled shows that these methods can be used for in vivo studies. The regions that are typically plagued with artifacts (near air/tissue interfaces) were shown to have very different T2 values before and after correction methods were used.
The studies described are all at lower field strengths (1.5 and 0.7 T) where relative B1 inhomogeneity compared to 8 T is minimal. The complicated
B1 and B0 maps reflect this (Figure 6.6 shows an example of an in vivo B1 map).
89 90
0
Figure 6.6: Sample in vivo B1 map. Only the region inferior to the ventricles received an approximately 90° flip angle.
However, T2 has been studied at 8 T despite these inhomogeneities. In order to aid in the development of techniques it is important to understand how T2 changes in those areas where a nominal flip angle deviates from 90°. The goal of this study was to evaluate the variation in T2 calculated from Hahn and CPMG spin echo sequence across the range of flip angles typically observed in the head and head-sized phantoms with the 8 T in vivo and in situ images.
METHODS AND RESULTS
In all cases, the images were acquired using the 8 T whole body system. Tuning of the coil was accomplished with a Network/Spectrum Analyzer
HP 4195A after reflection calibration. Reflection calibration is a technique of
90 calibrating the network analyzer to external standards. For details of this procedure refer to the owners manual.
Nominal flip angles were determined using a 5x5x5 mm3 voxel- selective spectroscopy stimulated echo sequence (Bruker: VSEL_STE_SPECT).
The STEAM (stimulated echo acquisition mode) is a sequence of three 90° slice selective pulses along each of the three orthogonal planes to define a cubic volume within a sample. Since signal is a sin3 function of the flip angle, the 90° flip was defined as the first maximum reached as RF power is increased. Other desired flip angles were set according to the function below:
α=90*10^[(Tx90-Txα)/20] Equation 6.3
where α is the desired flip angle, Tx90 is the power attenuation setting for a 90°
flip angle, and Txα, the transmit power level for the desired flip angle.
Flip angle maps are constructed from multiple Gradient Echo acquisitions (Bruker: GEFI_TOMO) with the flip angle varying for each acquisition. The images were then fit to:
M=M0sinα Equation 6.4
91 Many flip angles may be acquired; however, to conserve time some cases included only the 60 and 120°, but each study will designate as to how many
(and which) angles were included.
96WELL
A polypropylene 96 well container by FisherScientific (typically used for multi-sample preparation and assays) was used in the initial evaluation of the feasibility to use these holders for contrast agent studies. All wells were filled with 0.5 mM Gd-DTPA (Aldrich, to shorten T1) and 0.125 M NaCl (dielectric properties similar to in vivo study), then the holder was placed into a larger container of water in order to reduce the artifacts associated with susceptibility differences at air/sample interfaces. Imaging was performed in a dual strut/dual port coil.
T1 was measured using Inversion Recovery Spin echo (Bruker:
MSME_TOMO) with TR/TE/TI=4000/16.4/25,50,100,200,500,1000,3000 ms. T2
(Bruker: MSME_TOMO) was measured using both Hahn
(TR/TE=800/12.4,90,150,200,250,350 ms) and a 16-echo CPMG spin echo
(TR/τ=800/12.4 ms). The spin echo images for T2 measurement were acquired
1) as single 3 mm thick slice and 2) five 3 mm thick slice with no gap (to study the amount of cross talk between adjacent slices). All spin echoes were acquired with a FOV of 15 cm, a matrix of 256x256 (final resolution of 586x586x3000
µm3), a sinc3 RF pulse of 3 ms and 2168 Hz, readout of 50 kHz, and read and 92 phase offsets adjusted to center the phantom. Flip angle map images were collected with 18 α (ascending order; 8.0, 15.1, 30.2, 45.1, 45.1, 60.2, 74.9, 90.0,
120.1, 149.5, 165.8, 179.7, 194.7, 211.1, 226.2, 239.1°), TR/TE=2000/6.3 ms, single 5 mm thick slice with the same in plane resolution and RF pulse profile as the spin echoes.
Post processing of the images included reconstruction in IDL
(software for data analysis and visualization) and pixel-wise multiple linear regression for relaxation maps. B1 and receive sensitivity maps are also completed pixel-wise but with a gradient-expansion algorithm to compute a non- linear least squares fit (IDL: Curvefit). Regions of Interest (ROIs) were set within each sample area and did not include holder walls.
T1 has been shown to depend less on flip angle than T2 (Kingsley et al., 1998) and was calculated to be 380 ms for the Gd-DTPA solution. T2Hahn and T2CPMG were 210 and 280 ms, respectively. Flip angle variation did not exceed 130° within the regions of the 96wells. Sample data are shown below.
93 A
B C 300 300
0 0
D E 180° 100%
0° 0%
Figure 6.7: 96well sample images. A) Hahn spin echo image with TE of 12.4 ms, B) Hahn T2 map, C) CPMG T2 map, D) Flip Angle Map, and E) Receive Map. T2maps are scaled from 0 to 400 ms, Flip angle from 0 to 90°, and Receive from 0 to 100%
94 The sample image within Figure 6.7 demonstrates the signal loss common to low flip angle or receive sensitivity. The result is that the square phantom appears to be arrowhead shaped. Adjusting the contrast within the relaxation time maps allows a more complete view of the entire phantom. The images from the longest TE for the Hahn spin echo exhibited a unique artifact.
Each sample well became irregularly shaped, similar to the well shapes in the flip angle map. This artifact contributes to the contamination of Hahn T2s and may be due to system instabilities or eddy currents. The CPMG sequence may be effectively reducing these instabilities.
Figure 6.8 depicts the average calculated T2 values within each sample well. Flip angle varied between 50° and 120°, blue markers are Hahn spin echo, and pink markers are CPMG. As seen here, Hahn spin echo is more sensitive to the errors in flip angle. As discussed previously, the CPMG sequence will compound errors by applying multiple non-180° refocusing RF pulses. However, here it appears that the CPMG is acting to recover the loss of some transverse signal due to instabilities. The use of a TR that was significantly less than five times T1 also introduces T1 contamination.
95 350
300
250
200
T2 (ms) 150
100
50
0 40 50 60 70 80 90 100 110 120 Flip Angle (degrees)
Figure 6.8: T2 dependence on flip angle for 96well phantom. Each point is the mean T2 value for a well. ♦ are Hahn data points and are CPMG.
Histograms of the above data indicate that there are several T2 values calculated for Hahn data while there appears to be only one for the
CPMG. Figure 6.9 is another view of the data, but for each pixel individually.
There is an extreme amount of scatter within the Hahn data with no clear trend from low to high flip angles.
96 Figure 6.9: A) Scatter plot of Hahn T2 dependence on flip angle. This is a pixel-wise plot rather than the mean values in Figure 6.6. B) Histogram of the Hahn T2s. There are several peaks that stand apart. C) CPMG T2 scatter plot. There is less scatter within the data, and the mean value is approximately 300 ms over the range of flip angles. This is also observed in the histogram D) where only one peak is present. The FWHM (full width at half max) is very narrow reflecting the refocusing of some errors by the CPMG sequence for this data series.
Multi-slice data indicated that the variations in-slice due to RF inhomogeneity were greater than single-slice acquisitions. Therefore, all subsequent studies (Chapters 7-10) were acquired using single slice techniques to avoid these problems.
This phantom was abandoned for several reasons. First, there were truncation artifacts within the sample areas making it difficult to obtain ghost free images and maps. The presence of the grid-like pattern makes the data more difficult in defining ROIs which can be observed in a zoomed section of an image (see Figure 6.10). Also, the small size of the phantom and coil used prevented the ability to obtain a large range of flip angles. Image quality was general poor (noisy and many artifacts) and this resulted in the sample wells not
97 maintaining a square shape within the maps. This is especially true for the flip angle and receive maps.
Figure 6.10: Demonstration of the truncation artifacts in the grid-like pattern of the 96well phantom from the Hahn SE short TE image. These artifacts propagate into scatter within the T2 maps.
The general observations to conclude from this study were that T2 decreased with increased flip angle and CPMG appears to be more stable across flip angle variations. This may be influenced by the T1≈T2. Also, the CPMG appears to be refocusing some of the errors that are contaminating the Hahn spin echo.
BALL PHANTOM
Continuation of this study included a previously prepared phantom used for previous studies of TEM resonator modes. This 18 cm diameter ball filled with the same solution as the 96well and loaded into a shielded 16-strut, single port coil (Ibrahim et al., 2003). T2 was acquired again but with two TRs 98 each, 400 and 2500 ms, to study the effects of T1 contamination, i.e. the residual magnetization remaining from the previous sequence repetition from a short TR.
Hahn SE TEs included 19.4,100,200,250,300,350 ms and the 8-echo CPMG had a τ=50 ms. Spin echoes were of a single 5 mm thick slice with matrix of
256x128, a FOV of 20 cm (reconstructed final in-plane resolution of 781x781
µm2), 50 kHz bandwidth, and RF pulse profile of 8 ms sinc3 of 750Hz. Gradient echoes included 9 αs of 11.25, 22.5, 45, 60, 90, 120, 135, 150, and 180°, the same RF profile, but a matrix of 128x128 and TR/TE=2500/5.8 ms for computation of flip angle and receive maps.
Some variation in T1 was seen with values ranging from 300-350 ms (Mitchell, 2004) and was shorter than those seen in the 96well phantom.
Poor image quality for the spin echo images resulted in extreme fluctuations in the calculated T2 values. Approximate values of T2 in locations with a 90° flip angle for Hahn was 170ms and CPMG was 210 ms.
99 A
B C 350 350
0 0
D 180° E 100%
0° 0%
Figure 6.11: A) Sample Hahn SE image of the ball phantom (TR/TE=2500/19.4 ms), B) Hahn T2 map , C) CPMG T2 map, D) flip angle map, and E) receive sensitivity map for first data series. In all cases, an ROI was drawn to eliminate a large portion of the ghosting to reduce computational time on data that would not be meaningful.
100 While ROI selection was not an issue with this phantom, the images possessed extreme ghosting, which were carried into the fitting routines.
This is evident in the scatter plots of Figure 6.12. Also, nominal flip angles did not exceed 150° greatly decreasing the range of values for study. Repeat studies could solve the range of flip angles by adjusting the attenuation settings.
Figure 6.12: T2 dependence on flip angle scatter plots. A) Hahn sequence T2 Variation and B) the Hahn histogram sequence with a long TR. C) and D) are the same as A and B, respectively, but for the CPMG Spin echo. The amount of ghosting in the images in apparent in these graphs by the scattering present, even within the small area shown in T2 maps in Figure6.11.
However, even with this scatter, the histograms indicate that there are not multiple Hahn T2 values as compared to the 96well. Figure 6.13 shows that while there is a broad peak centered on the above-mentioned T2s, there is not the multiple peaks as seen previously.
101 Long and short TR did not appear to have a large effect on the distribution of T2s resulting from the images. For the short TRs, there is a narrowing of the histograms while the mean values are similar to the T2s of the long TRs.
Figure 6.13: Histograms from Hahn and CPMG spin echoes with long (2500 ms) and short (400 ms) TRs.
The enclosed coil behaves differently than the standard TEM head coil used for in situ studies. This, along with the consistently poor quality of images resulting from the ball study lead to the search for a new phantom.
102 4 L NALGENE BOTTLE
A large Nalgene bottle filled again with the same solution of Gd-
DTPA and saline was imaged with a 16 Strut/Single Port TEM coil. T2 protocols were acquired with two TR values of 450 and 2500 ms. Hahn SE TEs were
19.2,100,200,300,400 ms and the 8-echo CPMG τ was 50 ms. Spin echoes were of a single 3 mm thick slice, a 256x128 matrix, 20 cm field of view
(reconstructed final in-plane resolution of 781x781 µm 2), 50 kHz readout bandwidth, and RF profile of an 8 ms sinc3 pulse with 750 Hz. The data were also collected as a single 5 mm thick slice with nominal flip angle of 45°. Flip angle and receive sensitivity maps were computed from gradient echoes were acquired with 23 αs of 36.6, 42.5, 49.4, 57.4, 66.7, 70.1, 75.6, 81.4, 85.6, 90,
92.3, 94.6, 99.5, 102.0, 104.6, 109.9, 115.6, 118.5, 121.5, 124.6, 127.7, 134.3, and 137.7° with 256x128 matrix, and TR/TE of 2000/19.2 ms.
These images were again very ghosty as seen in Figure 6.14 below.
103 A B
C D 400 400
0 0
E F
Figure 6.14: Images from 1.5L Nalgene bottle experiments. A) Sample image TR/TE=2500/19.2 ms, B) same image from but from the repeat study. C) Hahn T2 map, D) CPMG T2 maps from the first experiments. E) and F) are the same respectively as C and D, but for the repeat study.
104 G 180° H 100%
0° 0%
I J 180° 100%
0° 0%
Figure 6.15: Continued from Figure 6.14. G) Flip Angle Map, and H) Receive Map from the first experiment. I) and J) are from the repeat study and correspond to G and H. The flip angle and receive sensitivity maps were masked to reduce computation time.
T1 and T2 were approximately equal and at values ranging from 300 to 350 ms.
However, as the flip angle varied from 90°, the T2s resulting from mono- exponential increased. This was more pronounced with the Hahn SE than the
CPMG as seen in Figure 6.9. Images and maps in Figures 6.14 and 6.15 show data from two experiments that were part of a repeated study.
105 Figure 6.16: T2 scatter plots for the two 1.5 L Nalgene Bottle data repeat studies. The amount of scatter in the second series (C & D, see Figure 6.14) as compared to the first (A & B) may represent instrument degradation. The CPMG sequence has a much broader range over which the T2 is fairly constant compared to the Hahn sequence.
In the case of the repeat study on a separate day, the scatter is severely increased and is a direct result of the increased ghosting. The B1 field dependence is shown to be greater in the repeat study (see Figure 6.16).
It is important to note; however, that the range of flip angles now covers the broad spectrum from below 45° up to nearly 180°. Using an
106 attenuation setting for a nominal flip angle of 90° does not yield any additional information. Instead, only part of the variation is observed (see Figure 6.17)
Figure 6.17: Hahn spin echo data for A) Attenuation setting for a nominal flip angle of 90° and B) nominal flip angle of 45°.
The expected mono-exponential behavior expected for spin echo signal decay with TE is not observed in many of the pixels. This applies to both the Hahn and
CPMG sequences. While this is expected with CPMG (due to oscillations mentioned above resulting from spurious echoes, see Table 6.1 and refer to
(Majumdar et al., 1986a)), this is not be expected for Hahn as only one time point is acquired at a time. Some sample signal decays are shown in Figures 6.18 and
107 6.19. In several of the CPMG cases, the first echo signal is lower than the second echo.
Figure 6.18: Hahn spin echo decay patterns for 0. 5mM Gd-DTPA phantom at various flip angles. A) α=157°, B) α=144°, C) α=92°, D) α=34°.
Figure 6.19: CPMG spin echo decay patterns for 0.5 mM Gd-DTPA phantom at various flip angles. Flip angles are the same as in Figure 6.16.
108 However, T1≈T2, as occurs in this experiment, is not the T1/T2 ratio observed in tissue. Tissue relaxation rates typically have T1 up to 30 times greater than T2. Because the purpose of this study is to evaluate how T2 is affected by flip angle during in situ study, a more appropriate phantom would have to be designed. Also, when evaluating this new phantom, it might be useful to assess the combined analysis fit.
1.5 L PLASTIC CONTAINER
Kraft (1987) provides, in a matrix format, T1 and T2 ratios for solutions of agarose and copper sulfate at various temperatures and field strengths. Using this information, a new phantom was designed in a 1.5 L plastic food container consisting of 4% Agarose (Aldrich), 2 mM CuSO4 (Jenneile), and
0.125 M NaCl. This phantom was also imaged using the 16 strut/Single Port
Coil, but in a coronal plane. This results in a different flip angle and receive sensitivity distribution. According to the data from Kraft, chemicals affecting solution viscosity alter T2 more than T1 while paramagnetic metallic salts alter
T1. Therefore, in order to obtain a high T1/T2 value, we wanted low salt (µM) and high agarose concentration (almost super concentrated).
Inversion recovery spin echo was used for T1 measurements with
TR/TE/TI=4000/16.4/25,50,100,200,500,1000 ms. T2 spin echoes had TRs of
600 and 3000 ms. These were chosen such that the short TR would be equal to
T1 while the long TR is five-times T1. Hahn TEs were 20,50,90,134.4 ms and
109 the 8-echo CPMG τ was 20 ms. All spin echoes were acquired 3 mm thick single slice, 256x128, FOV of 18 cm (final in-plane resolution of 703x703 µm2), 50 kHz bandwidth, and a RF profile of 8 ms sinc3 at 750 Hz. A two angle B1 map was constructed from gradient echoes with α=60 and 120°.
Fitting of the T1 data resulted in a relaxation time of approximately
630 ms. Spin echo images demonstrated less ghosting than previous phantom studies.
110 A
Figure 6.20: A) Spin echo sequence image. B) Hahn, C) CPMG, D) Flip angle, and E) Receive sensitivity maps
111 Chi square maps were constructed from the Hahn and CPMG fitting routines; however, IDL calculates a reduced Chi square (Χ2, Chi square divided by the degrees of freedom). A threshold was selected by visually examining goodness of fit and setting a maximum value for Χ2 accordingly. This elimination of pixels not fitting a mono-exponential model considerably reduced scatter within plots of T2 versus Flip Angle. Figure 6.21 demonstrates these scatter plots.
Hahn spin echo has more scatter at low flip angles while the CPMG show an increase in T2s as the flip angle deviates by more than 20° from a optimal value at 90°.
112 Figure 6.21: T2 dependence on flip angle scatter plots for A) Hahn and B) CPMG T2 results. C) 2 Hahn and D) CPMG T2 data again, but filtered using Χ to eliminate areas where the fitting does not fit the mono-exponential decay model. Unlike what has been seen previously, the Hahn data is more stable over the range of flip angles than the CPMG. This is apparently due to the T1/T2 ratio difference. Since this ratio is similar to what is seen at high field in human brain, then we can expect CPMG to be more sensitive during in situ or in vivo study.
Averaging a 10x10-pixel region to calculate the Hahn, CPMG, and
Combined analysis data reduced the amount scatter compared to a pixel-wise fit.
This also reduces the deviations in T2 due to variations within the receive sensitivities, which tends to be fairly constant over small regions of interest.
113 Figure 6.22: A) Hahn and B) CPMG T2 scatter plots with flip angle. Each points is representative of a 10x10 pixel region of interest averaging the T2 and flip angle in that area. Combined spin echo analysis results with a 10x10 pixel ROI averaging the flip angle and fit results. C) Intrinsic 2 2 T2 scatter with Flip Angle and D) γ G D scatter. While the Hahn data was fairly well behaved, the CPMG influence causes the large variation.
Even in image regions of ideal flip angle (90/180°), a mono- exponential decay model does not fit the spin echo signal well, and so the combined equation for T2 signal from Carr and Purcell was also used. This data were assessed in a similar manner with the scatter plots. As seen from Figure
6.22, the combined data T2 and γ2G2D demonstrate a behavior similar to that of the CPMG.
114 Comparison of long and short TR again did not show a large difference in Hahn, CPMG, intrinsic T2, nor γ2G2D as compared to in-plane variations.
IN SITU CADAVER
Application of these methods to human head imaging would provide more information on what data may be considered accurate in the study of dementias. The use of in situ cadavers allows for extended study of protocols for many hours. Without the concern of motion artifact, the same ROIs may be used throughout the analysis.
Imaging at 8 T utilized a 16-strut/Quad-port TEM (transverse electromagnetic resonator, where the struts were located at 180°) coil. Tuning of the coil was accomplished with a Network/Spectrum Analyzer HP 4195A after reflection calibration. Due to the variation of flip angle across a high field image, the attenuation required for a local α of 90° in the area of the hippocampus was found by using a voxel (5x5x5 mm3) selective stimulated echo spectroscopy sequence (VSEL_STE_SPEC).
Four Hahn (TE=21.7,50,90,134.4 ms, MSME_TOMO) and eight- echo CPMG SE (τ=21.7ms, MSME_TOMO) were acquired with a TR of 1500 ms.
These images were of a 3 mm thick coronal slice with a 512x384 matrix, NEX=2, and 16 cm field of view (FOV, final in-plane resolution of 312x417 µm2). The RF pulse profile was a sinc3 for 8000 ms and bandwidth of 750 Hz; readout
115 bandwidth was 50 kHz; frequency direction was in the head-to-foot direction; and read, phase, and slice offsets were adjusted to center the samples within the
FOV. Total scan times were approximately 20 minutes each.
The flip angle variation across the image is more than two-fold; therefore, in order to assess which areas of the image had unreliable T2s due to flip angle extremes, B1 maps were constructed. This included two gradient echo
(GEFI_TOMO) scans with TR/TE=5000/min of multiple 3 mm thick coronal slices to cover a majority of the brain, a 256x64 matrix, and attenuation set such that
α=120 and 60°. Remaining parameters remained the same as for the spin echo to ensure imaging of the similar locations. Total scan times were approximately
2.5 minutes each.
All images were reconstructed on IDL from the FID data. Hahn and
CPMG were initially fit to mono-exponential decays for their respective T2 values.
Combined analysis was also performed on the data with evaluation of all CPMG echoes, the even only CPMG echoes, and all CPMG echoes except n=1 prior to complete analysis. Mono-exponential decay of both Spin echo sequences was performed in a pixel-wise fashion using a multiple linear regression fit (IDL:
Regress). Combined fit of Hahn and CPMG using the Carr & Purcell equation used a gradient-expansion algorithm to compute non-linear least squares pixel- wise fit T2 and γ2G2D maps (IDL: Curvefit). Also, for combined analysis, pixels corresponding to invalid flip angles were eliminated evaluating the signal intensities for CPMG echoes. That is, if the 1st CPMG echo was lower than SNR or less than the 2nd echo signal or if signal of the 2nd echo was less than the 116 signal for the 3rd echo, then that voxel was not fit. Regions of interest were drawn on the TE=50 ms images using IDL.
There is a correlation between the areas of non-perfect 90/180°
RF pulses and regions with a decreased signal for the first echo of the CPMG sequence. This is shown in Figure 6.23 for an in situ data set.
Figure 6.23: A) Spin echo image where regions of flip angle <70° and >110° are shaded and B) regions where 1st CPMG echo signal is greater than the 2nd. These regions correspond and this limitation was used in the rejection of pixels during the fitting routine.
In some voxels, the first echo of the CPMG sequence is lower than the second echo. Still in other voxels, there is an oscillating pattern between even and odd echoes. This is due to the non-90° excitation and non-180° refocusing pulses in the sequence. We investigated how the distributions of intrinsic T2 and γ2G2D were altered by excluding the first CPMG echo, the odd 117 echoes, or inclusion of all echoes. The exclusion of the first echo did not change the distributions to a large degree while even echoes versus all echoes did.
Since there the oscillating behavior was not uniform (odd echoes consistently lower in signal than even echoes) and an adapted gradient scheme was not implemented to reduce the formation of spurious echoes, all echoes were used in the combined spin echo fitting routine.
118 2 2 Figure 6.24: Distributions from a gray and white matter ROI. A) T2 and B) γ G D distributions for Combined Spin echo fit with all CPMG echoes included. C through F are similar except C & D do not include the first CPMG echo and E & F use even only echoes.
Only one in situ study was examined with a nominal flip angle of
45°. The ROIs that were analyzed are the same as those described in Chapter
9. This includes cortical gray and white matter, hippocampal gray matter, and parahippocampal gyrus white matter.
119 Unfortunately, there does not appear to be a consistent trend through the Hahn and CPMG data. That is, unlike the phantom results, there is not a clear increase of the relaxation time at the lower flip angle. This is summarized in the table below:
ROI Name 90°-45° Hahn 90°-45° CPMG Cortical Gray Matter L - - White Matter L + + Cortical Gray Matter R - - White Matter R + + Superior Cortical Gray - + Matter L Superior White Matter L + - Superior Cortical Gray - + Matter R Superior White Matter R + + Hippocampal Gray - - Matter L Parahippocampal White - - Matter L Hippocampal Gray + - Matter R Parahippocampal White + + Matter R
Table 6:2: Relative T2s for Regions of Interest in situ at 90° and 45° nominal flip angle. Phantom data indicate that the decreased nominal flip angle would result in an increase in T2 values. Therefore, the values for Table 6.2 should be negative for all ROIs.
Without a clear trend, it is difficult to determine how the nominal flip angle affects gray and white matter. The combined analysis was not performed
120 on this data. This was due to previous observation that the T2 and γ2G2D term tend to follow the CPMG data.
DISCUSSION
T2 variations occur for many reasons during in vivo study. Soft tissues are not the same consistency throughout and this may or may not be observed in image data. Therefore, it is important to understand the stability of image contrast with respect to instrumental issues. The homogeneity of the B0 and B1 fields are two issues that can be addressed with hardware and pulse programming development; however, hardware is expensive for a novel instrument, engineering expertise must be on hand to properly install hardware to the current system, and pulse programming required an understanding of the language, which was not available to our researchers until fairly recently.
Therefore, this study was an effort to understand what were the limitations on the current system producing consistent T2 values for a uniform phantom. Using these criteria, we can then eliminate data for in situ and in vivo studies that are known to have invalid relaxation times.
Poon, Sled, and others have stated that T2 should decrease with a deviation from a nominal flip angle of 90°. These data demonstrate just the opposite trend, consistently. While the amount of scatter and ghosting is different for each phantom and scan, the trend remains the same.
Chi square as calculated by IDL is a reduced chi square value, which is divided by the number of degrees of freedom. However, the chi square 121 map does not correlate with the flip angle nor receive maps. Therefore, this may not be the only factor at play. By examining the agarose phantom data with the
10x10 voxel moving ROI, it was possible to evaluate T2 with the receive coil sensitivity fairly constant. Still, T2s increased outside of the 90° pulse.
The Hahn sequence did not exhibit constant behavior. In the original 96well series, it was the Hahn T2s that were less stable across flip angle variations. The exact opposite is true in the case of the agarose phantom.
Clearly, this is partly due to the T1/T2 ratio of the phantoms. In the 1.5 L
Nalgene bottle studies, both Hahn and CPMG T2s increased outside of a 90° flip angle, with CPMG being the more stable sequence. Again, this observation defies expectations. The CPMG sequences have several refocusing pulses and errors in the pulse profile would be expected to compound. The simpler Hahn sequence, with only one refocusing pulse, would then be expected to have less error propagation.
The gradient scheme used by Bruker for the MSME_TOMO sequence does not appear to follow any of the optimized corrections for B1 inhomogeneities offered by Majumdar, Poon, Crawley, nor Sled. Therefore, in other experiments, to limit the deviations in T2 that occur due to RF inhomogeneity, only those pixels that lie within a nominal angle of 90±20° were fit.
It would be of interest to investigate more closely, by analytically solving the Bloch equations, what the gradient scheme in the Bruker sequence is
122 doing. While at low field it may be a safe assumption that the B1 field is mostly homogeneous, clearly it is not at high field.
When continuing this study, it is important to remember the material in which the phantom is constructed. The importance of the T1/T2 ratio is demonstrated with the behavior of Hahn and CPMG T2 with the flip angle. While it is not clear at this time why Hahn is more variable when T1≈T2 and more stable when T2< 8 T than copper. Unless a better performing RF coil can be designed, it is necessary to examine the quality of the data produced by the 8 T system. Until this has occurred or the pulse sequences are adapted such that the residual longitudinal relaxation can be completely dephased so that spurious echoes do not result, then only that data that corresponds to regions of 70-110° flip angles can be used. This is beneficial in understanding the effects of flip angle on relaxation and to help build confidence in the data that are reported; however, there are several drawbacks to this. First, the criteria cause much of the data to be eliminated for larger fields of view. To overcome this, the power settings for a 90° pulse must be set in the region to be investigated. If there are many areas of the sample to be considered, this may require two acquisitions (increasing time and expense). In order to eliminate the data, gradient echoes must be collected 123 so that the flip angle maps may be constructed. This again adds time to the experiments and expense to the investigator. Elimination of some data by considering the signal intensity of the first CPMG spin echo may be a method of reducing this step. In many in vivo cases, if patients are physically unable to remain motionless or claustophobic, they may not be capable of tolerating such long scanning sessions. 124 CHAPTER 7 MACROMOLECULAR EFFECTS ON T2 Contrast agents have been developed for altering of relaxation times of in vivo system to enhance the contrast between tissues or to evaluate organ function and kinetics. The relaxation mechanisms of these paramagnetic agents have been studied for lower field strengths, but have not been investigated thoroughly at higher magnetic fields (≥7 T) (Weinmann et al., 1984; Unger et al., 1985; Lauffer, 1987; Martin et al., 1995; Aime et al., 1996; Bulte et al., 1998; Wang et al., 1998a; Caravan et al., 1999; Hines et al., 1999; Loubeyre et al., 1999; Stanisz et al., 2000; Schmalbrock et al., 2001). There is relatively little work done with T2 measurements as compared to T1. This in part is due to the fact that gadolinium, a very common contrast agent, is primarily a T1 agent and is very popular in clinical research. There are several factors that can affect the relaxivity of a sample, field strength (as alluded to above), pH, temperature, viscosity (Lauffer, 1987; Caravan et al., 1999), presence of macromolecules (Aime et al., 1996; Stanisz et al., 2000), and in cell cultures (Hines et al., 1999; Schmalbrock et al., 2001). The studies described here are an attempt to begin to understand how field strength 125 and macromolecular content affect the T2 relaxivity of a sample of gadolinium- DTPA in aqueous solution and solutions containing macromolecules. The relation between relaxation and contrast agent concentration is (Stanisz et al., 2000): 1/T2=1/T20+R2[X] Equation 7.1 where T20 is the relaxation time without the contribution of the paramagnetic relaxation agent, R2 is the calculated relaxivity of that particular agent, and [X] the concentration of the agent (recall also that R2=1/T2). This linear relationship has been used in describing behaviors in aqueous solution phantoms and then extrapolated to estimate agent concentrations within tissues. There is an equivalent equation for T1, which has been well studied, and T2 is expected to follow the same trends. This study is an attempt to measure and understand R2 values for gadolinium-DTPA in the presence of bovine serum albumin (BSA) at 0.7, 1.5, and 8 T. We hope to better understand the relaxation mechanisms for the development of contrast agents that are efficient across all the field strengths. This work has been presented at the International Society for Magnetic Resonance in Medicine. 126 METHODS Gadolinium is an element, which is paramagnetic in its ionic state with seven unpaired electrons (Gd3+). While being extremely toxic in vivo, chelation has been possible with several compounds leading to a molecular structure that is thermodynamically stable enough for in vivo study without the risk of toxicity (Weinmann et al., 1984; Martin et al., 1995; Aime et al., 1996; Caravan et al., 1999). One such chelator, DiethylTriaminePentaacetic Acid (DTPA, C14H23N3O10, structure shown below in Figure 7.1), meets the requirements for aiding in gadolinium’s ability to form a contrast agent by tightly binding gadolinium, allowing coordination of at least one water molecule to the metal, and intact excretion from the body. 127 COO- - -OOC-CH CH2-COO 2 CH2 N-CH2-CH2-N-CH2-CH2-N - - OOC-CH2 CH2-COO Gd3+ Figure 7.1: Structure of DTPA with coordination to Gadolinium. This structure allows a single water molecule to coordinate to the gadolinium. It is important to note, however, that when Gd-DTPA is injected into an in vivo system it is too large to move across the blood brain barrier (BBB) and remains within the vascular spaces provided that BBB is intact. Within vessels are compounds other than water, including proteins, hormones, small molecules, etc that also affect the transverse relaxation of water. Therefore, a true model must include compounds within the solution of a phantom used for study of contrast agents. Bovine Serum Albumin (BSA) is analogous to a human version of this protein originating within the blood, thus making it ideal to use for our study simulating the features/properties of Gd-DTPA in vivo. It is a well-studied protein of approximately 68 kDa, with a largely alpha-helical secondary structure (Carter et al., 1994), a heart-shaped tertiary structure (Bos et al., 1989; Carter et al., 128 1989), and a uniform distribution of negative, positive, and neutral surface residues at a neutral pH (Carter et al., 1994). Several protein assays include BSA for construction of the standard calibration curve, so confirmation of protein concentration within the samples would be facilitated. Magneviste Gd-DTPA compound was selected for its availability to the study. It also has the simple chemical structure (Figure 7.1) with a q value of 1 (the number of water molecules that may coordinate to gadolinium at a time). It is commonly used in clinical study. It is possible to chemically bind Gd-DTPA to BSA (Lauffer et al., 1985; Lauffer et al., 1986). This involves the mixture of anhydrous DTPA to solid protein, which is followed by the rapid solubilization in buffer. The reaction is completed by the addition of a metal. Both the Magneviste Gd-DTPA and BSA chemicals used in our studies were in solution (i.e. not anhydrous). Therefore, we assume that there was no coupling between BSA and DTPA in our solutions. MRI METHODS Samples of BSA (Immuncor Gamma; 0, 3, 12, 18%) with Gd-DTPA (Magneviste; 0.25, 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2 mM) were prepared with deionized water in 0.5 mL Eppendorf tubes. These samples were then placed, 16 at a time, into an Eppendorf holder (Fisher Scientific), which were then set into a small container of water prior to placing into an RF coil. The purpose of the additional container of water is two-fold: 1) this avoids air/sample interfaces around the sample tubes thus decreasing the macroscopic magnetic 129 susceptibility associated artifacts and 2) increases the load within the coil to obtain adequate signal for RF coil tuning and imaging. At 8 T, the RF coil was a double strut/dual port TEM coil (transverse electromagnetic resonator, where the struts were located at 180°). Tuning of the coil was accomplished with a Network/Spectrum Analyzer HP 4195A after reflection calibration (see Chapter 6). Lower field strength (0.7 and 1.5 T) data were collected with an extremity phased array coil on GE Signa systems. As described in Chapter 4, Hahn and CPMG spin echoes may be used in a combined analysis in order to obtain information regarding the diffusion and susceptibility effects associated with paramagnetic materials within a sample (Carr et al., 1954a). Therefore, both sequences were used for the acquisition of data at 8 T. Single Hahn SE (TE=25, 50, 100, 200, 400 ms, Bruker: MSME_TOMO) and 16-echo CPMG SE (τ=25ms, Bruker: MSME_TOMO) were acquired with a TR of 6000 ms (TR was greater than 5 times the T1 of the sample of lowest concentration of Gd-DTPA). These images were of a 5 mm thick coronal slice with a 256x128 matrix and 20 cm field of view (FOV) with a final resolution of 781x781x5000 µm. The RF pulse profile was a sinc3 for 3000 ms and bandwidth of 2168 Hz; readout bandwidth was 50 kHz; frequency direction was in the Head-to-Foot direction; and read, phase, and slice offsets were adjusted to center the samples within the FOV. Total scan times were approximately 12_ minutes for each spin echo. Particularly at high field, image quality is compromised by an 130 inhomogeneous B1 field (Ibrahim et al., 2001a). Therefore, acquisition of a flip angle map is necessary for the elimination of data in areas where the flip angle is outside the 90±20° criteria described in Chapter 6. After finding a nominal flip angle of 90° with a voxel selective stimulated echo sequence (VSEL_SPEC_STE), gradient echo sequences (Bruker: GEFI_TOMO) were acquired with attenuation set such that α was 60° and 120° (see Chapter 6 for details of finding a 90° flip angle). These allow for the analysis of the regional flip angle and receive sensitivity of the coil for T2 data collected by Equation 7.2 (Mitchell et al., 2003; Whitaker et al., 2003b). Sα/S2α=cos2α Equation 7.2 At 0.7 and 1.5 T only the Hahn sequence data were collected because a true CPMG sequence is not standard on these GE systems. The spin echo sequence had TR/TE=2500/30, 50, 100, 250, 500 ms, 5 mm thick coronal slice with a 256x192 matrix and 20x15 cm field of view (FOV; final resolution of 781x781x5000 µm), 15.6 kHz readout bandwidth, and read, phase, and slice offsets were adjusted to center the samples within the FOV. Total scan times were approximately 6_ minutes each. Because the low field strengths do not have significant issues with flip angle homogeneity relative to the high field images, data for flip angle maps were not collected. 131 Data analysis was performed on Interactive Data Language (IDL), Version 5.5, by Research Systems Inc (RSI). Low field Hahn mono-exponential data were fit pixel-wise by multiple linear regression (IDL: Regress). Individual mono-exponential and combined fit of the Hahn and CPMG of 8 T data were accomplished pixel-wise by multiple linear regression (IDL: Regress) and a gradient-expansion algorithm to compute a non-linear least squares fit (IDL: Curvefit) using n=1 for Hahn, and n=echo number for the CPMG, respectively. Voxels associated with a nominal flip angle outside 90±20° were eliminated by the criteria of CPMG signal for n=1 less than signal for n=2. Regions of Interest (ROIs) were drawn on magnified T2 maps. Viewing histograms eliminated stray points that were due to partial volume effects along the border of the sample tube. The same voxels were used within an ROI for both T2 and γ2G2D analysis. Regressions for relaxivities were weighted by the standard deviation of each pixel of the relaxation maps. Quadratic fit of the γ2G2D term with gadolinium concentration was also weighted by the standard deviations, forced to have an x- intercept of 0, and was performed using a Levenberg-Marquardt least-squares minimization (IDL: MPFit). RESULTS LOW FIELD HAHN ANALYSIS Low field data results indicate that Hahn T2s cannot be correlated to concentration of Gadolinium within the sample once a macromolecule is added. That is, the decay is dominated by the presence of a macromolecule with 132 very little effect from the gadolinium. 133 A 0.7T Gd-DTPA with 0% BSA R2 0.012 0.01 0.008 ) -1 0.006 R2 (ms 0.004 y = 0.0039x + 0.0014 R2 = 0.8594 0.002 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Gd-DTPA (mM) B 0.7T Gd-DTPA with 18% BSA R2 0.012 0.01 0.008 ) -1 0.006 R2 (ms 0.004 y = -0.0004x + 0.0071 R2 = 0.0381 0.002 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Gd-DTPA (mM) Figure 7.2: 0.7 T R2 versus Gd-DTPA concentration. Pixel-wise data in blue diamonds and yellow lines are mono-exponential decay results (A) 0% BSA showing a linear increase with concentration. (B) 18% BSA showing that there is no change in R2 with increased Gd-DTPA. The relaxation time is dominated by the presence of BSA. The contribution from gadolinium on R2 is least pronounced in the highest concentrations of BSA. Table 7.1 (below) lists the results for all low field Hahn 134 Spin echo data. 0.7T R2 1.5 T R2 [BSA] (x10-3 ms-1mM-1) (x10-3 ms-1mM-1) 0% BSA 3.87 ± 0.26 3.98 ± 0.27 3% BSA 3.18 ± 0.26 3.09 ± 0.28 12% BSA 1.62 ± 0.26 2.24 ± 0.28 18% BSA -0.36 ± 0.26 -0.15 ± 0.30 (x10-3 ms-1mmolal-1) (x10-3 ms-1mmolal-1) 0% BSA 3.87 ± 0.261 3.98 ± 0.27 3% BSA 3.10 ± 0.25 3.00 ± 0.28 12% BSA 1.43 ± 0.23 1.98 ± 0.24 18% BSA -0.29 ± 0.21 -0.12 ± 0.25 Table 7.1: All 0.7 and 8 T Hahn Spin data for relaxativity (R2). Both Molar and Molal results are shown. The linear regression for the 18% concentration BSA results in a negative slope, but the R2 value is also extremely small. It is important to note that even at low field, the mono-exponential decay model is not sufficient to describe T2 for samples containing macromolecules, and that the decay pattern is dominated by the presence of macromolecules. However, since the CPMG sequence was not available on the GE Signa system, the combined analysis was not possible. Conversion of the gadolinium concentration from molarity to molality yielded slightly slower relaxivities. The reason for performing this conversion was that molal concentrations reflect the increased gadolinium concentration due to reduction of the water pool. This results in higher 135 gadolinium concentrations than initially calculated by molarity. The significance of this data may be a factor to examine in the future and may reflect the free water exchange giving insight into the inner sphere contributions to relaxation. HIGH FIELD ANALYSIS High field data included both the Hahn and CPMG spin echo sequences. Neither sequence yielded a sufficient mono-exponential fit of the signal data. This is demonstrated below in Figure 7.3 with a linear regression through the ln(S) data for Hahn (A) and CPMG (B). The data are then shown with the combined analysis fit in C. In the case of mono-exponential fit, the results from least-squares fits did not fit the shortest echoes (T2 too long) or the longest echoes (T2 that is too short). For the Hahn data it is evident that the mono-exponential fit attempts to fit the last echo while the shorter echoes indicate a much shorter T2 and may be a truer reflection of the data. CPMG data were not mono-exponential and demonstrated some oscillating behavior with time due to non-90/180 α/2α flip angles. Only the combined fit resulted in a satisfactory fit of these data. Other groups have shown that data from complex systems with multiple components cannot be described by a mono-exponential decay for T2 data (Whittall et al., 1999). 136 A B Hahn Mono-Exponential CPMG Mono-Exponential 3 3 2 2 1 1 ln(Signal) 0 ln(Signal) 0 -1 -1 -2 -2 0 50 100 150 200 250 300 350 400 0 50 100 150 200 250 300 350 400 TE (ms) TE (ms) C Combined Analysis 3 2 1 ln(Signal) 0 -1 -2 0 50 100 150 200 250 300 350 400 TE (ms) Hahn CPMG Figure 7.3: Demonstration of the inability of a mono-exponential decay to fit high field Hahn (A) and CPMG (B) data. (C) is the same data as (A) and (B) but shown with the combined fit proposed by Carr & Purcell. Another indication that the combined fit yielded results that are a better representation of the data is that the high field R2 determined by mono- exponential decay were not linear with concentration. This is especially true for 137 the highest concentrations of BSA. There appeared to be no systematic behavior in these plots. With the combined fit, it is possible to derive an intrinsic T2 and the γ2G2D term representing diffusion and susceptibility/gradient effects. This intrinsic T2 was linear with concentration of gadolinium for a given macromolecule concentration. The γ2G2D term was not linear, but rather appears to have a quadratic dependence with Gd concentration. With these observations, it is possible obtain a relaxivity value (R) for T2 at varying concentrations of BSA, but there is no equivalent linear relationship for γ2G2D. Below in Figure 7.4, we can see how the intrinsic T2 derived from the combined equations allows for a linear fit of the data with concentration. Also there are examples of the linear and quadratic fits to the γ2G2D term. While there are some stray points in these plots, they were not eliminated because when those voxels were included in the R2 fits they did not demonstrate significant stray. 138 A 8T Gd-DTPA with 18% BSA R2 B 8T Gd-DTPA with 18% BSA γ2G2D 0.04 1.00E-04 0.035 0.03 ) 8.00E-05 -3 ) -1 0.025 6.00E-05 0.02 D (ms 0.015 2 4.00E-05 G R2 (ms 0.01 2 γ 2.00E-05 0.005 0 0.00E+00 0 0.5 1 1.5 2 0 0.5 1 1.5 2 Gd-DTPA (mM) Gd-DTPA (mM) 2 2 Figure 7.4: (A) Relaxivity plot of R2 versus Gd-DTPA for 18% BSA. (B) γ G D for the same voxels as in A. Both the best-fit linear and quadratic results are shown with the pixel-wise data. 2 2 The linear plot is unable to include the highest Gd-DTPA data and also has a negative γ G D value for a pure BSA solution. Quadratic fit was forced to have a minimum at x=0. The results for all 8 T data are shown in Table 7.2 and do not show a trend with BSA concentration. 139 8 T γ2G2D R2 R2 [BSA] 0 Ax2+Bx (x10-3 Ax2: (x10-7 Bx: (x10-7 (%) (x10-3ms-1) ms-1mM-1) ms-3 mM-2) ms-3mM-1) 0 1.53 4.66 ± 0.05 2.23 ± 0.04 -0.29 ± 2.23 3 4.93 3.46 ± 0.26 1.85 ± 0.21 0.87 ± 0.16 12 11.19 5.71 ± 0.42 47.41 ± 1.10 -10.84 ± 1.12 18 15.94 1.72 ± 0.70 136.42 ± 2.96 -1.16 ± 3.03 (x10-3 (x10-7 (x10-7 (x10-3ms-1) ms-1mmolal-1) ms-3 mmolal-2) ms-3mmolal-1) 0 1.56 4.66 ± 0.05 2.23 ± 0.04 -0.29 ± 2.23 3 4.90 3.39 ± 0.25 1.89 ±0.20 0.74 ± 0.16 12 11.19 5.03 ± 0.38 36.82 ± 0.85 -9.62 ± 0.99 18 15.96 1.40 ± 0.58 91.68 ± 2.04 -0.51 ± 2.55 Table 7.2: High field data resulting from combined spin echo fit of Hahn and CPMG data. Both Molar and Molal results are shown with the most difference occurring with the C coefficient of the 2 2 γ G D term. DISCUSSION Our 8 T data shows that Hahn and CPMG signal decay with TE cannot be fit well with mono-exponential curves. Further, especially with large concentrations of BSA, the relaxation rate, R2, from such mono-exponential fits did not show the expected linear increase with Gd-DTPA concentration. Better results were achieved with combined fit using Carr’s equation. Specifically, the resultant intrinsic R2 follows a linear relation with Gd. However, the resultant relaxivity R2 does not have a simple relaxtion with [BSA]. Furthermore, the γ2G2D 140 term as proposed by Carr and Purcell does not change linearly with increased concentration of macromolecule, but has a quadratic relation with concentrations of Gd-DTPA. That is, γ2G2D is more sensitive to slight changes of gadolinium at high concentrations of protein. Iron oxide samples in Chapter 9 are also linear in R2 and quadratic for γ2G2D with iron content. To further evaluate the differences between γ2G2D for a range of BSA concentrations, it is useful to consider what is occurring in diffusion. Spins are not stationary objects, but rather are dynamic throughout the course of an experiment. The degree to which spins are free to move is a function of the medium in which they exist. As the concentration of BSA is increased, the viscosity of the sample also increases (linearly according to Wetzel (1980)) meaning that the diffusion term, D, (distance traveled per unit time) decreases. The higher concentrations of BSA exhibit the largest quadratic increase over gadolinium concentrations for γ2G2D while having the lowest values of diffusion (D) over a constant G (susceptibility). The works of Hardy (1991), Kennan (1994), and Yablonskiy (1994; 1998) provide insight to a possible mechanism for understanding these observations. Diffusion of spins can be described as discrete step-wise jumps, and the motion may be described by 2 τD=R /D Equation 7.3 141 where τD is the diffusion time, R is some distance covered during this time, and D is the diffusion constant. To understand how relaxation is influenced by spin motion in the presence of magnetic field inhomogeneities from paramagnetic particles, one can diagram a situation as shown in Figure 7.5 (for simplicity only one dimension of motion is shown). The magnetic field perturbations from the paramagnetic particles extend over some radius, R, causing frequency fluctuations δω=γΔB(R) Equation 7.4 Proton spins traversing past the magnetic field inhomogeneities will experience variable phase shifts and signal loss over t (t=TE in Hahn spin echo or τ in CPMG sequences) as described by the relaxation rate 2 2 2 R2=R2intrinsic+γ G Dt /12 Equation 7.5 where the local field gradient described the local magnetic field inhomogeneities ΔB(R) as discussed in Chapter 4. Three different cases may therefore occur (see Figure 7.5). In the case of path C, the spins travel more quickly past the magnetic perturbers than in 142 path B. In A, though the situation is not realistic, for all practical purposes it is realized when the diffusion is negligible during the MR observation time, TE. This case, A, is known as the static rephasing regime, and is the case when the spins are most strongly influence by the paramagnetic particles and the frequency fluctuations δω>>1/τD. In this case, the relaxation rate is dominated by the γ2G2D term (Hardy et al., 1991; Kennan et al., 1994; Majumdar et al., 1987). If on the other hand, spins move very rapidly past the magnetic field perturbers (path C in Figure 7.5), magnetic field fluctuations are effectively averaged and the apparent variability is small, i.e. δω<<1/τD. The relaxation rate is then dominated by the intrinsic term. Path B, depicted in Figure 7.5 is an intermediate situation known as the motion narrowing regime, where both the intrinsic and the γ2G2D terms influence the relaxation rate, R2, which reaches a maximum (Kennan et al., 1994; Weisskoff et al., 1994). The results of the Kennan and Weisskoff simulations are summarized in Figure 7.6. 143 A B C R Figure 7.5: Diagram of speed of diffusion past field perturber (contrast agent). In path A, the spin travels slowly (static regime). By contrast, path C the spin moves quickly and experiences little effect by the local magnetic field. Finally, path B is an intermediate situation where the relaxation 2 2 time is influenced by both intrinsic T2 and γ G D. Figure 7.6: Summarized Weisskoff and Kennan simulation results 144 At 8 T, where susceptibility effects are relatively stronger compared to lower field strengths, relaxation of Gd-DTPA in BSA solutions seems to be best described by this intermediate path, B. The dependence of R2intrinsic and γ2G2D on BSA for different Gd concentrations, and on Gd for different BSA concentrations are shown in Figure 7.7 and 7.8. 3.00E-05 4.00E-05 A B 3.50E-05 2.50E-05 3.00E-05 2.00E-05 2.50E-05 ) ) -3 -3 1.50E-05 2.00E-05 D (ms D (ms 2 2 G G 2 2 γ γ 1.50E-05 1.00E-05 1.00E-05 5.00E-06 5.00E-06 0.00E+00 0.00E+00 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 2 4 6 8 10 12 14 16 18 Gd-DTPA (mM) [BSA] (%) 0% BSA 3% BSA 12% BSA 18% BSA 0.5mM 0.75mM 1.0mM 1.25mM 1.5mM 1.75mM 2.0mM 2 2 Figure 7.7: A) γ G D dependence on Gd-DTPA concentration using the fit parameters tabulated in Table 7.2. Experimental measurements are not shown to emphasize the overall trend within 2 2 the data. B) γ G D dependence on BSA concentration. Figure 7.7B does not show wether γ2G2D behaves similar to R2* in Weisskoff (1994) where R2* reaches an asymptote at a particular particle size. Or perhaps if concentration will continue to increase (within the saturation limits of a BSA). This figure also demonstrates how quickly the γ2G2D term increases in high concentrations of protein in the motion narrowing regime. Figure 7.7B data was fit to a quadratic and the results are below in Table 7.3 145 [Gd-DTPA] Ax2 Bx R2 (mM) (x10-7 ms-3) (x10-7 ms-3) 0.50 0.23 1.99 0.9698 0.75 0.40 2.84 0.9849 1.00 0.64 4.04 0.9898 1.25 0.94 5.58 0.9919 1.50 1.31 7.47 0.9930 1.75 1.74 9.69 0.9936 2.00 2.24 12.30 0.9940 2 2 Table 7.3: Least squares results for γ G D as a function of BSA concentration for various Gd- DTPA concentrations. R2 does not appear to follow Equation 7.1. This may be due to a lack of data. Rather, it may be viewed in light of the conclusions by Weisskoff (1994) or Kennan (1994). Figure 7.8A below shows that 12 and 18% R2s intersect at approximately 1.2 mM Gd-DTPA. This is point of intersection becomes a maximum R2 is dependent on BSA concentration. 146 A 2.50E-02 B 2.50E-02 2.00E-02 2.00E-02 1.50E-02 1.50E-02 ) -1 ) -1 R2 (ms R2 (ms 1.00E-02 1.00E-02 5.00E-03 5.00E-03 0.00E+00 0.00E+00 0 2 4 6 8 10 12 14 16 18 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 [BSA] (%) Gd-DTPA (mM) 0% BSA 3% BSA 12% BSA 18% BSA 0.5mM 0.75mM 1.0mM 1.25mM 1.5mM 1.75mM 2.0mM Figure 7.8: A) Intrinsic R2 (1/T2) versus Gd-DTPA where slope is equal to R2. R2 does not have a specific trend across BSA concentrations. B) Intrinsic R2 as a function of BSA concentration for various Gd-DTPA concentration. The length of the red line corresponds to the slope in A for that BSA concentration. The data presented here are a beginning to understanding the effects of macromolecules on transverse relaxivity. There is a limitation in the data that was complicated by further loss as a result of technical difficulties. Further engineering may aid in eliminating these problems at high field. The γ2G2D term obviously has its weaknesses by assuming that gradient effects from susceptibility and diffusion of spins are independent. This also does not consider the effects from the motion-narrowing regime. However, it provides a basis for explaining the contrast changes at 8 T as compared to clinical field strengths. 147 CHAPTER 8 IN VITRO STUDY OF HIPPOCAMPUS Nearly all of the techniques discussed in Chapter 5 have been used at some time to investigate AD. With the capacity to have image resolutions of 200x200 µm2 in-plane and increased sensitivity to mesoscopic susceptibility effects, the differentiation of AD from normal controls should be facilitated by 8 T MRI. To investigate whether T2 or T2* at 8 T would be sensitive enough to detect increases in iron (and neuritic plaque) accumulations in Alzheimer’s tissues, we examined formalin fixed hippocampal specimens. METHODS Specimens (7 AD and 3 Normal Control; ages 57-87; age, sex, and hemisphere matched) were collected from tissues (in 10% buffered formalin) prepared for autopsy according to the standard protocol. Diagnosis of AD was confirmed by pathological diagnosis according to the NINCDS criteria. 148 Age Sex Dementia Identification 73 M - A 73 M - B 75 F - C 64 M AD D 68 F AD E 79 F AD F 80 M AD G 80 F AD H 85 F AD I 87 F AD J Table 8.1: Details of the suject cohort. Sections of the medial temporal lobe collected included a coronal slice of the hippocampus and parahippocampal gyrus. These specimens were stored in 3.8 cm diameter by 1.9 cm height plastic containers with formalin and imaged in a Teflon container (6.35 outer diameter by 5.08 cm height). A small disk of plastic was used to prevent the tissue from floating during imaging. MR images were acquired with the 8 T using a 7.6 cm diameter single strut coil. Two protocols were evaluated. The first included four, 2 mm thick slices through the 1 cm thick specimen using a Hahn spin echo series with TR/TE=750/20,50 ms and a 2D gradient echo series of TR/TE=100/5.4,7,10,15 ms, α=20°, and NEX=6. In addition we attempted to manipulate iron levels on the amyloid plaque by submersion of tissue specimens into a solution of 0.01 mM Ferric Chloride and 0.01 mM Ferric Citrate. Two specimens (one normal control and 149 one AD) were scanned prior to addition of iron and at days 2, 4, and 6 after addition of iron. The same MR protocol was followed. Data for FDRI were collected at 1.5 T for comparison with the 8 T images. Low field images were collected on a General Electric Signa scanner. These included five 2 mm thick slices with no gap of a Hahn spin echo, 256x128 matrix, FOV of 6 cm (final reconstructed in-plane resolution of 234x234 µm2), TR/TE=800/20,50 ms, and a NEX of 8. The FDRI calculations were performed according to Bartzokis (1993). The second protocol included a single, 2 mm thick slice Hahn spin echo series of TR/TE=800/20,50,90,134.4 ms and an eight-echo CPMG spin echo with TR/τ=800/16.8 ms. All images had a 5.39 cm FOV, 256x256 matrix, with final resolution of 210x210x2000 µm3. All images were reconstructed off-line using IDL (Research Systesms Incorporated, RSI). Mono-exponential decay of gradient echo and both spin echo sequences were fit with a multiple linear regression for T2* and T2, respectively. Combined fit of Hahn and CPMG data was pixel-wise with a gradient-expansion algorithm to compute a non-linear least squares in a pixel- wise manner to calculated intrinsic T2 and γ2G2D maps. Phase images were constructed with the help of Dr. Abduljalil. After completion of imaging, each specimen was split in-half coronally and prepared for histological staining. This included Perls’ Stain for Iron, Bielschowsky’s for plaques and tangles, and Hematoxylin and Eosin for general pathological analysis. 150 Regions of interest for the hippocampus were based on the subregions of the cornu Ammonis (CA) and drawn on the TE=50 ms images using MRIcro (Rorden et al., 2001). Although the exact locations of the regions of the CA and entorhinal cortex are best defined by histology, their locations were approximated by consideration of several histological samples of hippocampi and the anatomy described in Devernoy (1998). Figure 8.1: Demonstration of the ROI selection. A mixed effects model (Ramon et al., 1996; Pinheiro, 2000) is one in which the variables are random rather than fixed. That is, there is a different error associated with each observation, and this allows the conclusions to be applied to an entire population and not just the sampled cohort. A mixed effects model was applied to these data using the freeware program, R (Hornik, 1998). The difference between AD and normal controls was evaluated. Each ROI was compared between normal controls and a threshold value for the 151 confidence level was set. If the difference between AD and a normal control ROI exceeded the threhold, it was denoted significantly different. We used the bootstrap technique with the Kolmogorov-Smirnov test for finding approximate confidence intervals. The Kolmogorov-Smirnov test returns the Kolmogorov- Smirnov statistic (D) and significance level, which states the probability for the null hypothesis that two given data sets are drawn from the same distribution (Press et al., 1992). Such a technique is described on pg. 271 of Rice (1995). This analysis was performed on IDL. RESULTS The goal of this study was to develop a protocol that would be suitable for examination of relaxation time measurements in AD. These results reflect the refinement of a protocol that was applied to in situ and in vivo studies. OBSERVATION OF ANATOMY The quality of the images was excellent with visualization of several of the anatomical features of the parahippocampal gyrus and hippocampus (even in AD tissues with atrophy). 152 Figure 8.2: Sample Hahn Spin echo image (TE=50 ms) demonstrating typical contrast and SNR (120). Gray matter is hyperintense to white matter and isointense to formalin. Basic anatomic features of the hippocampus were visible, including the fimbria, dentate gyrus, cornu Ammonis, subiculum, and parahippocampal gyrus. Detailed layering was observed and will be discussed below. Signal to Noise Ratio (SNR) for the first Hahn spin echo was approximately 120. Formalin was typically dark on short TEs and became bright by TE of 90 ms. Gray matter had lower signal than white matter regions. The vestigial parahippocampal sulcus was extremely low in signal and this is the result of a high density of vessels in this region. Cellular layers that were resolved included the alveus, hippocampal polymorphic layer, pyramidal layer, cornu Ammonis (CA 1-4), hippocampal molecular layer, vestigial hippocampal sulcus, dentate molecular layer, granule layer, and dentate polymorphic layer. 153 CYTOARCHITECTURE AND CELLULAR LAYERS Besides the anatomy described above, we may be detecting other microscopic anatomy. Brain anatomy can be divided categorically by the cytoarchitecture. This is the organization of brain structures based on their microscopic organization of neurons (Mesulam, 2000). The complexity of this cytoarchitecture increases with evolutionary advancement. The limbic system, which shows the earliest involvement of AD, is composed of the simplest cytoarchitecture structure microscopically. This includes the corticoid and allocortex. The corticoid regions demonstrate very little organization with dendrites projecting into several different directions. The slightly more organized allocortex possesses two distinct layers of neurons with dendrites in ordered directions. The allocortex is further divided into archicortex and paleocortex, of which the hippocampal complex and piriform, respectively, are examples. There are three types of cortex outside of the limbic system. The mesocortex is a transition area between allocortex and isocortex and demonstrates six layers that are not well defined. The neocortex contains six layers that are well defined and is further divided into unimodal and heteromodal zones. The primary sensory-motor areas are examples of the idiotypic cortex. 154 Figure 8.3: A) Cresyl violet staining of the hippocampus and entorhinal cortex. While the hippocampus is part of the limbic system, which has few layers, starting with the subiculum into the entorhinal cortex there is further division into 6 layers. Layers 1-3 form a superficial band and 4-6 another band. These two sections are visible in B) a spin echo image. While the six layers of the entorhinal cortex are not differentiated in our images as they were in the images in Fatterpekar (2002), it seems that in the cresyl violet stained image from Mesulam that layers 1-3 are grouped into one layer and 4-6 form a deeper layer (Mesulam, 2000). This is demonstrated in the 8 T image of a medial temporal lobe section in Figure 8.9. The hippocampus has only three layers. However, starting with the subiculum, there is a hyperintense band superficially, a medium band, and the transition into white matter. While this provides unique information for study of gray matter, it now becomes a question as to how to define ROIs. The banding of this region causes scatter within calculated maps and complicates the analysis and results. 155 Histological Evaluation Pathological staining with Biel’s stains for plaques and tangles allowed assessment of the amyloid plaque burden for each specimen. The Alzheimer’s tissues had significant amyloid burden while normal tissues were virtually devoid of any plaques. A B Figure 8.4: Sample histological stainging results. A) Perl’s stain and B) Biel’s stain for hippocampus and entorhinal cortex. C) is a normal control for Perl’s stain of a liver section Unfortunately, the Perl’s staining for iron did not yield a positive result. DAB enhancement performed by the Perry laboratory at Case Western Reserve University indicated very slight staining of some plaques, but the levels of staining were not equivalent to their usual results. We attributed this to loss of iron due to formalin fixative solutions. From Chapter 3, we remember that formalin solutions may become acidic over time, which causes the loss of metals such as iron. The use of a alcohol based fixation solution, such as methacarn 156 (Puchtler et al., 1970) was then proposed for future in situ studies. Alcohol based fixative solutions may not be used prior to MR imaging because of the chemical shift between the protons of alcohol and water. Therefore, imaging of cadavers prior to autopsy and placement of tissues into methanol would maintain tissue iron levels but reduce chemical shift complications. INITIAL EXPLORATION OF METHODS DESCRIBED IN THE LITERATURE This initial attempt to measure a difference in T2 or T2* included only two normal control and four confirmed AD samples. T2 and T2* T2 relaxation times were based on a dual Hahn spin echo acquisition. AD (n=4) Normal Control T2 Values p-value ms (n=2) ms Entorhinal Cortex 28.86±2.27 38.10±10.76 NS Subiculum 29.97±5.03 25.96±0.10 NS CA1 30.75±4.38 34.10±0.42 NS CA2 34.48±3.27 50.42±10.00 0.03 CA3 29.07±3.32 33.11±1.87 NS CA4 29.31±3.83 32.50±1.84 NS Table 8.2: Mean T2 Values for AD and Normal Controls for all regions of the hippocampal complex. 157 The conclusion was that there was a significant difference (Student T-test) between the AD and normal controls in the CA2 region only. However, the normal control T2 for this region was long compared to the other regions and may involve some partial volume effects from nearby cerebral spinal fluid (CSF). This would be influenced by the imaged slice placement with respect to the tissue thickness. Large variations and standard deviations can also be observed with partial volume effects. The large standard deviations for the entorhinal cortex and CA2 reflect that one subject had large T2s as compared with the other normal controls. T2* measurements could not be obtained using a 2D-gradient echo sequence due degradation of the images at air/tissue interfaces. Also, there was contamination of the tissue signal where tissue and formalin were adjacent. The original six samples were scanned using a 2D gradient echo sequence in a large, custom-built Teflon container in order to reduce air/sample susceptibilty differences that would lead to artifacts. 158 Figure 8.5: Sample Gradient Echo image (TE=15 ms)of the hippocampal specimen. Striations within the image lead to miscalculations of T2*. Some regions adjacent to areas of CSF demonstrated bright spots that also lead to abnormally high T2* values. The large areas of signal void are due to air bubbles in the formalin. As in the Spin echoes, gray matter is hyperintense compared to white matter. AD (n=4) Normal Control T2* Values p-value ms (n=2) ms Entorhinal Cortex 11.90±2.28 7.44±4.17 NS Subiculum 14.43±4.88 12.82±6.20 NS CA1 21.24±5.60 23.30±9.62 NS CA2 23.94±20.25 25.78±5.53 NS CA3 -5.74±41.97 18.70±1.07 NS CA4 29.92±17.84 24.17±6.22 NS Table 8.3: Mean T2* Values for AD and Normal Controls for all regions of the hippocampal complex. This early protocol was deficient, in that the T2 curve was not sufficiently sampled. CPMG was not considered despite the majority of clinical field strength T2 measurements for AD use this sequence. Elimination of 159 artifacts in the gradient echo images is required for accurate T2* measurements, and therefore should not be pursued until another sequence could be tested thoroughly. Iron Manipulation Results Similar to the study by Sayre, (2000b) we attempted to manipulate the iron levels on the plaque by addition of ferric chloride and ferric citrate. With the addition of iron, T2 and T2* should decrease. However, after the samples were in iron solutions for two days, both the T2 and T2* values increased dramatically. These values reached a plateau with additional days in the iron solutions. This increase in T2 values would indicate that there was an increase in the water content of the tissue. Field Dependent R2 Increase (FDRI) FDRI is the comparison of R2 (T2-1) values of a sample at different field strengths. As mentioned above, some studies attempted to correlate a decrease in T2 with field strength to the concentration of iron in that sample. Previous studies were conducted at 0.5 and 1.5 T and resulted in a significant shift in T2 (Bartzokis et al., 1993; Bartzokis et al., 1994a; Parsey et al., 1997; Bartzokis et al., 2000b). If changes could be seen between 0.5 and 1.5 T, then the more sensitive nature of 8 T to iron deposits would be considerably more effective in differentiating AD from Normal Controls. 160 In this portion of the study, one normal control and one AD specimen was scanned at 1.5 T to provide preliminary results. As seen earlier, there was no change in the T2 values between AD and normal control at 8 T. However, T2 measurements at 1.5 T indicated a significant increase in T2 for the AD compared to normal controls (this would be expected with increased water). The result is that AD FDRI was increased compared to normal control, but this may be due to increased water and not iron. Normal Alzheimer FDRI Control x10-2(ms-1) Values x10-2(ms- 1) Entorhin 1.8 2.3 al Cortex Subiculu 2.0 2.4 m CA1 1.4 2.0 CA2 1.5 2.2 CA3 1.6 2.1 CA4 1.5 2.0 Table 8.4: FDRI results comparing 8 T to 8 T. These data include an n of 1 for both AD and Normal Control. Therefore, statistical methods are not possible for comparision. 161 While these numbers appear promising, the results are based on an n=1 for each category. With an n of 1, it is impossible to draw conclusions on whether FDRI is a good measure of AD tissues. Further study of T2 changes at 1.5 T in Alzheimer’s tissues must be examined as well. Phase Imaging Data acquisition for phase images uses a gradient echo with a long TE to gain added contrast based on the phase differences between the spins. When a FID is recorded from an experiment, both a real (magnitude) and imaginary (phase) portion of the signal is detected and recorded. A magnitude image is constructed from the square root of the sum of squares of real and imaginary for each pixel. The phase image is the tangent of the quantity imaginary divided by the real. Figure 8.6: A) Gradient echo magnitude image corresponding to B) phase image. Gray and white matter contrast is lost with the vestigial sulcus remaining prominent in the phase image. 162 The sample images of Figure 8.6 demonstrate the best in phase contrast of all the specimens with a gradient echo. There is a darkening of the vestigial parahippocampal sulcus, which is the location of several vessels, which is seen as a dark structure due to deoxyhemoglobin. In no regions of the cornu Ammonis is there any sign of phase contrast. Gray and white matter are isointense with formalin indicating they have similar magnetic susceptibilities. The only other anatomical features that may be depicted in the phase image include the fibria and a white matter tract superior to the temporal horn of the ventricle. We expected that application of this procedure to in situ tissues would yield a different result and should be considered in developing a protocol (see Chapter 9). Studies have already shown phase imaging to be effective in tumor recognition and the associated vascular change (Christoforidis et al., 1999; Dashner et al., 2003; Novak et al., 2003). COMBINED HAHN AND CPMG The second study was initially intended to compare the Hahn and CPMG spin echo sequences. We then hypothesized that a combined analysis of the sequences should yield a more sensitive measure of total iron content through the γ2G2D term (Carr et al., 1954) 163 Mixed Effects Analysis The distribution of T2 and γ2G2D within a region of interest does not have a normal (gaussian) shape. Typically the distributions are skewed toward higher T2 values while γ2G2D is very broad. With this observation, it becomes more difficult to properly describe the data and to determine whether two groups are significantly different, i.e. simple assessment of mean and standard deviations is insufficient. Example distributions for the CA1 regions are shown for all specimens for T2 and γ2G2D in Figure 8.7 and 8.8, respectively. The bottom three distributions are the normal controls with AD subjects above. 164 A B 2 2 Figure 8:7: Combined analysis distributions, A) Intrinsic T2 and B) γ G D, for CA1 regions of the hippocampal specimens. Hahn and CPMG data are below in Figure 8.8. The bottom three are normal controls. 165 Figure 8.8: Distributions for A) Hahn and B) CPMG hippocampal data. The bottom three are normal controls. Not surprisingly the distributions for the intrinsic T2 and γ2G2D for the hippocampal data have less scatter than what will be seen for the in situ distributions (see Chapter 9). This is due to the smaller coil that is capable of producing better signal to noise ratios and a more homogeneous B1 field. γ2G2D terms are again broader than the intrinsic T2 data. Again, there is no trend 166 toward shorter T2s or higher γ2G2D values that would indicate increased iron content. Pavlicova (Whitaker et al., 2003a) investigated the combined analysis using a mixed effects model (Ramon et al., 1996; Pinheiro, 2000). This model accounts for fixed and random affects of the data. The distributions of each region of interest were considered and the results is shown in Figure 8.9. There is no differentiation between the distributions of the AD cases (white) and normal control (gray). This figure also demonstrates the skewed distributions that lead to significant p-values. Combined R2 Figure 8.9: Sample data from mixed effects model for the CA1. Gray is normal control data and white is AD data. The boxes delineate 1 standard deviation and the circles are outliers. 167 There were some areas that had significant differences between AD and normal control. A summary of these results is listed below with the p- values. An ROI was drawn within the formalin to serve as an internal standard. The values within Tables 8.5 and 8.6 are averages over all AD and all normal control tissues. R2 (s) AD Normal Control p-value CA1 9.09 10.63 0.0026 CA2 9.88 9.90 NS CA3 10.31 10.41 NS CA4 8.84 10.34 0.0006 Entorhinal Cortex 9.26 9.14 NS Subiculum 10.60 10.71 NS Parahippocampal Gyrus 11.83 11.86 NS Formalin 3.50 3.90 0.0232 Table 8.5: Mixed Effects Model results for R2 averaged for all subjects. Only the CA1, entorhinal cortex, and formalin were statistically signficant. 168 γ2G2D AD Normal Control p-value (s-3) (x10-4) (x10-4) CA1 1.12 1.07 NS CA2 1.07 1.15 NS CA3 1.00 1.17 0.0205 CA4 1.08 1.13 NS Entorhinal Cortex 0.98 1.33 0.0004 Subiculum 1.19 1.44 0.0029 Parahippocampal Gyrus 1.13 1.32 0.0006 Formalin 0.51 0.58 0.0001 2 2 Table 8.6: Mixed Effects Model results for γ G D averaged for all subjects. Only the CA1, CA2, and CA4 regions were considered to be not significantly different. The most common observation was that if the distributions had a p- value making them statistically significant, it was due to a tail within a data set. Formalin was set as a control and should not have been significant. However, in both R2 and γ2G2D it was found to be significantly different between AD and normal controls. This may have been true if iron leached out of the tissues into solution, but the solutions were replaced during each imaging session. Bootstrap Analysis Another statistical comparison of distributions is called the Bootstrap method. This is a type of Monte Carlo analysis applied to observed data. Again, this analysis allows for the comparison of non-gaussian distributions. Two distributions are compared using the Kolmogorov–Smirnov test. The upper and lower bounds of the 90% confidence level of the difference 169 term, D, were measured between groups. A threshold was set for each of the regions of interest from the normal controls. This threshold then determined whether a comparison was significantly different from normal control comparisons. Thresholding was accomplished using pair-wise comparisons between the normal controls for all regions of interest. However, the histograms for γ 2G2D for one normal control had extremely poor quality. Even with eliminating this normal from the comparison, the lower bound of the 90% confidence levels for nearly all ROIs were over 0.50. This indicates that the normal controls were not similar enough for comparison of disease and normal control tissues. T2 γ2G2D Region Threshold Threshold CA1 0.78 0.90 CA2 0.96 0.55 CA3 0.97 0.40 CA4 0.86 0.67 Entorhinal Cortex 0.42 0.73 Formalin 0.67 0.29 Parahippocampal 0.77 0.80 Gyrus Subiculum 0.99 0.98 Table 8.7: Threshold levels for the normal controls. For these values, the greatest lower bound of the 90% confidence level is the threshold. 170 CONCLUSIONS As a preliminary study to consider protocol procedures, such as pulse sequences, parameter selection, pathological evaluation, etc, in vitro study may provide a starting point. However, in vitro study of anatomical tissues is not ideal for any investigation on relaxation (Vymazal et al., 1996b). If there are differences between the relaxation rates of Alzheimer’s diseased tissues and Normal Controls, it was not detected in these studies. If this was due to fomalin solutions, then an in situ or in vivo study will eliminate this problem. 8 T should be sensitive enough to detect such differences if studies at 1.5 T have a positive result. However, Dhenain’s (2002) results, which appear to be consistent with our own, makes a valid point on the lack of susceptibility effects (increased partial volume effects of paramagnetic substances). But, these studies were also on in vitro tissues. It is possible to obtain high-resolution images of brain tissue with great image quality and anatomical differentiation at high field with short scan times. The multiple hour studies with MR Microscopy do not lend themselves to in vivo applications and their results should be considered with caution. The anatomy visible in high field MR make analysis difficult. Banding in the gray matter may be associated with cytoarchitecture and can potentially provide vital and new information if it can be obtained in vivo (Fatterpekar et al., 2002). However, it now sets forth the question of how to define regions of interest. No longer is it sufficient to study only gray versus white matter. Will this appearance of the layering be found throughout the brain? 171 If so, cortical layering knowledge will be required in order to analyze high field data. T2 measurements can be made at 8 T with either Hahn or CPMG sequences, or the data can be considered simultaneously in a combined spin echo analysis. This may provide a measurement that may correlate with total iron content. T2* measurements could not be made using the current gradient echo sequence. Field Dependent R2 Increase may also provide another measurement that can correlate with total iron content, but a greater understanding of the relaxation rates of our specimens at 1.5 T must be acquired before FDRI can be made practical. Finally, phase imaging has already shown promise at 8 T. The lack of contrast in this study may not be indicative of possible results with in situ study. While this has not been useful in the sense of a positive result, it did provide the opportunity to evaluate the types of data acquired from 8 T tissue studies and how to correctly evaluate the results with powerful statistical tools when normal distributions are not present. 172 CHAPTER 9 IN SITU STUDY OF ALZHEIMER’S DISEASE As discussed in Chapters 3, iron is distributed inhomogeneously throughout the brain with the most concentrated areas being the nuclei. This normal distribution is disrupted in many neurodegenerative diseases, such as Parkinson’s and Alzheimer’s diseases. This may play a part in the etiologies of these diseases because of the importance of iron in cellular metabolism, the toxicity of high levels of metal, and the catalysis of oxidative stress pathways (Choi, 1995; Sayre et al., 1999; Perry et al., 2000; Rottkamp et al., 2000; Smith et al., 2000b). This presence of iron leads to susceptibility effects that alter the magnetic field locally. These local magnetic fields lead to measurable signal change with a variety of MRI methods (see Chapter 5). Following the investigation of small tissue samples of the medial temporal lobe regions (Chapter 8), the next step in developing a protocol for the study of Alzheimer’s disease was to involve postmortem in situ human brain studies. While the hippocampal specimens did not give a positive result (Whitaker et al., 2001; Whitaker et al., 2002; Whitaker et al., 2003a), we believed that this was due to the formalin fixation of the tissues. The use of cadavers 173 would allow for a study with less alteration of the tissues, development of a protocol in whole brain, and the extended study of different parameters, confirmation of dementia by pathology, and independent tissue iron measurements. The objective of this study was to evaluate Alzheimer’s disease using the combined spin echo analysis and to correlate these data with mass spectroscopy and histology. METHODS SUBJECT POPULATION AD patients with their legal guardians are usually given the option to have an autopsy done to definitively confirm AD. As part of the concent for the autopsy, relatives/legal guardians of AD patients that were under the care of Drs Scharre or Beversdorf, were also asked if they agreed to the 8 T MRI study. Other subjects without known dementia were made available through the body donation program of the Department of Anatomy and Medical Education. However, autopsy could not be performed on these subjects and so the absence of dementia could not be confirmed. The summary of the patients included in the study is shown in Table 9.1. 174 Mass- Other Age Sex Diagnosis Identification Spectroscopy Information 79 M Alcohol Dementia A 72 F Alzheimer’s Yes B 73 M Alzheimer’s Yes C 76 F Alzheimer’s Yes In vitro only D 81 F Alzheimer’s Yes E 85 F Alzheimer’s F 86 F Alzheimer’s G 91 F Alzheimer’s H Hahn & CPMG power 93 F Alzheimer’s I setting not constant 58 M Cardiomyopathy J 68 M Colon Cancer K 85 M Colon Cancer L 69 M Fronto-Temporal CPMG only M Fronto-Temporal & 70 F Yes In situ only N Alzheimer’s 88 F Hemorrhage O 57 F Huntington’s Yes P 79 F Multi-system Atrophy Yes In situ only Q 84 M Vascular & Alzheimer’s Yes R Table 9.1: Summary of subject population. The letter identification will be used in future charts and graphs. designates those subjects that were donated to science from the Department of Anatomy and Medical Education. Cases D, I, M were not considered in subsequent analysis. For cases labled as in situ only, the excised slice was not reimaged. Confirmation of dementia was accomplished with pathological evaluation and comparison to the clinical observations. At OSU, the histological method used for assessment of Alzheimer’s disease is Bielschowsky’s silver stain for plaques. All tissues are also stained with Haematoxylin and Eosin (HNE) for general pathological assessment. MRI DATA COLLECTION Imaging at 8 T utilized a 16-strut/4-port TEM (transverse 175 electromagnetic resonator) coil. Tuning of the coil was accomplished with a Network/Spectrum Analyzer HP 4195A after reflection calibration. Due to the variation of flip angle across a high field image, the power level for RF amplitude required for a local α of 90° was found by using a voxel (5x5x5 mm3) selective stimulated echo spectroscopy sequence (VSEL_STE_SPEC), (Ibrahim et al., 2001a). Voxel placement was in the area of the hippocampus (involved in early dementia) so that these regions would receive a 90° flip angle. Four Hahn (TE=21.7,50,90,134.4 ms, MSME_TOMO) and eight- echo CPMG SE (τ=21.7 ms, MSME_TOMO) were acquired with a TR of 1500 ms. These images were of a 3 mm thick coronal slice with a 512x384 matrix, NEX=2, and a 16 cm field of view (final in-plane resolution of 312x417 µm2). The RF pulse shape was a sinc3 for 8000 ms and bandwidth of 750 Hz; readout bandwidth was 50 kHz; frequency direction was in the head-to-foot direction; and read, phase, and slice offsets were adjusted to center the samples within the FOV. Total scan times were approximately 20 minutes for each sequence. The flip angle variation across the image is more than two-fold; therefore, in order to assess which areas of the image had unreliable T2s, data were eliminated by the criteria defined in Chapter 6. B1 maps were constructed from two gradient echo (GEFI_TOMO) scans with TR/TE=5000/min of multiple 3 mm thick coronal slices to cover a majority of the brain, a 256x64 matrix, and RF power level were set such that nominal flip angles were α=120 and 60°. Other parameters remained the same as for the spin echo to ensure imaging of the 176 identical locations. Total scan times were approximately 2.5 minutes for each flip angle. MRI IMAGE ANALYSIS All images were reconstructed on IDL from the raw k-space data. Hahn and CPMG were initially fit to mono-exponential decays for their respective T2 values. Combined analysis and CPMG were examined using all echoes, even echoes only, and all but the first CPMG echoes (Chapter 6). All fitting was accomplished pixel-wise using a multiple linear regression fit (IDL: Regress). Combined fit of Hahn and CPMG using the Carr & Purcell equation used a gradient-expansion algorithm to compute a non-linear least squares pixel-wise fit T2 and γ2G2D maps (IDL: Curvefit). 177 Figure 9.1: Demonstration of Hahn (yellow dashed line), CPMG (green dashed line), and Combined Fit (both terms, solid line) for white matter, gray matter, and the globus pallidus (subject C). Also, for the combined analysis, pixels corresponding to flip angles outside the previously defined accuracy range were eliminated (See Chapter 6). Regions of interest were drawn on the TE=50 ms images using IDL and included the superior frontal gyri (gray and white matter superior to the cingulate gyrus), the gray matter of the hippocampus, and white matter of the parahippocampal gyrus. These regions were selected for the following reasons. First, these regions are 178 consistently within the accuracy flip angle criteria of Chapter 6. Second, the medial temporal lobe demonstrates inverted contrast compared to the cortical areas, and this selection would allow comparison of T2s. Finally, the protocol parameters are not optimized for study of the nuclei, i.e. the TEs must be shorter. Figure 9.2: Selected ROIs used for assement of T2 distributions in AD and normal controls, demonstrated on one in situ case, C. Statistical methods for differences in distributions included the 179 bootstrap method described in Chapter 8. Thresholding was performed through comparisons of the two hemispheres of a single normal control. The other normal control subjects had no known previous clinical history for dementia but were found to have abnormal brain anatomy during the 8 T investigation. Unfortunately, further investigation was not possible because autopsies could not be performed on these subjects. BRAIN TISSUE PREPARATION Following the in situ MR imaging, the bodies (for those not participating in the body donation program) were returned to the OSU Morgue for autopsy. The whole brain was removed and evaluated by a pathologist for weight and overall physical appearance. The hemispheres were separated and one of the hemispheres prepared for brain banking by Tissue Procurement. The remaining hemisphere was cut to obtain a coronal slice approximately corresponding to the imaged slice. The remainder of the hemisphere was set into formalin for standard pathological evaluation at a later time. The brain slice was placed into a container of saline and vacuum-pumped with a sink fit aspirator to remove air bubbles from within the sulci that would lead to image artifacts. However, this step was sometimes not included as air bubbles could be mostly avoided if placed carefully within the saline. The slice was then imaged again with the same protocol as the in situ case (with FOV adjusted appropriately) using a double strut-double port small TEM coil, tuned with a Network/Spectrum Analyzer HP 4195A after reflection calibration of the unit (see Chapter 6). 180 Following 8 T imaging of the brain slice sample, gray matter and white matter samples of 50-100 mg wet weight were dissected. These samples were placed into 0.5 mL Eppendorf tubes and further prepared for mass spectroscopy. This included weighing the samples (to 0.1 mg accuracy), placing them into 4 mL HDPE (high density polyethylene) bottles, and dissolving the tissue with 2 mL concentrated nitric acid (HNO3). Samples were stored in HNO3 until microwave digestion. At that time, the bottled samples were placed into a 65°C sonicator bath for one hour. Finally, the partially liquefied sample was placed into the SH3 micro sample vial, and the HDPE bottle was rinsed two- times with 0.5 mL concentrated HNO3. The digestion protocol was performed on a Milestone Ethos Microwave Labstation with the power of 1000W, and QP=50 (ventilation safety limit). There was a 10 minute rise time and 10 minute digestion at 180°C with a 3 minute cool down. Samples were diluted to a final weight of 30 g with deionized water. Spectra were gathered on a ThermoFinnigan Element 2 Inductively Coupled Plasma Mass Spectrometer for 56Fe and 57Fe isotopes. Results are reported as part per billion (ppb). Dilution calculations convert this data to parts per million (ppm or µg/g). After collection for mass spectroscopy, the remainder of the imaged slice was placed into 60% Methanol for fixation. Methanol is the main ingredient found in methacarn (an tissue fixation alternative to formalin) used for iron staining (Nguyen-Legros et al., 1980). Methacarn was not used due to safety concerns surrounding hazardous chemicals such as chloroform (a key ingredient in methacarn). The slice was not fixed prior to placement into methanol due to 181 chemical shift effects between the alcohol and water protons. The general autopsy protocol involves placement of the tissue into formalin for fixative purposes. However, this solution has been shown to become acidic over time, which leads to the leaching of metals from the tissues. Unfortunately the pathology department has not been able to reproduce the intensity of staining with methanol for the H&E. This may be due to their low demand for non-standard methods. Further investigation is necessary to understand how staining protocols will need to be changed to obtain adequate staining. RESULTS IMAGE APPEARANCE Visualization of the hippocampus and medial temporal lobe was consistently achievable by setting the transmit power level so that the nominal flip angle was 90° in the area of the hippocampus. However, this typically resulted in flip angles of greater than 180° and regions of signal loss near the ventricles. Neocortical regions of the frontal lobe were typically within the optimal 90±20° flip angle range. On the shortest TE images, the gray matter is slightly hyperintense to white matter and the nuclei are prominent. The CSF is slightly brighter than soft tissues. By the TE of 50 ms, the gray matter is much lower in signal than the white matter and CSF is bright. 182 Figure 9.3: Example Hahn spin echo images for all TEs, subject G. Gray and white matter contrast is not consistent throughout the brain. Neocortical areas show gray matter with lower signal intensity than white matter. Conversely, there is a gray/white matter contrast inversion found consistently in the medial temporal lobe (Figure 9.4). This may be partly due to the anatomical cytoarchitecture (Mesulam, 2000) and/or the age-dependent iron distribution described by Hallgren (1958). 183 A B Figure 9.4: Typical image with TE=50 ms, subject G. A) Complete image and B) enlarged region of the medial temporal lobe. Note the gray and white matter signal inversion in B compared to the cortical areas near the top of the brain (green arrows for white matter and blue arrows for gray matter) Another demonstration of this contrast inversion was often observed in the region of the cingulate cortex. While partially visible in Figure 9.4, it is more clearly visible in Figure 9.5 where the cingulate gray matter is hyperintense to white matter, but the opposite is true for the gyrus immediately superior to the cingulate. Fatterpekar (2002) makes note specifically of the layering changes in the cingulate (which is of the mesocortical cytoarchitecture type) versus frontal cortex (which is of the neocortex cytoarchitecture type). Also included in Figure 184 9.5 are plots of the signal or calculated value across gray matt, white matter, and CSF. This also demonstrates the shift in gray and white matter contrast between the cingulate and cortical gyrus just superior to the cingulate. 185 Figure 9.5: A) TE of 50 ms spin echo image of the neocortical area, subject N. The gray and white matter contrast inverts between the cingulate cortex and the gyrus just superior to the 2 2 cingulate. This inversion continues through to the B) combined T2 and C) γ G D maps and may be due in part to the cytoarchitural layering and regional differences of iron within gray matter. The red arrow indicates an area with gray matter hypointense to white matter. The yellow arrow indicates the cingulate gyrus where white matter is hypointense to gray matter. These regions are enlarged at the right of each of A, B, and C. The green line on the enlarged section indicates the data that is included in a plot at the left of each of A, B, and C. In the plot, A) white matter of the cingulate cortex, B) gray matter of the cingulate cortex, C) CSF, D) gray matter of the gray matter gyrus superior to the cingulate, and E) white matter of the gyrus superior to the cingulate. In the 1.5 T in vivo image in Figure 9.6, the dependence on brain region is not 186 observed for the same imaged plane. Gray matter is consisently hyperintense to white matter. Figure 9.6: Example 1.5T image (in vivo normal control) showing that gray and white matter contrast are consistent through the brain, unlike at 8 T. (TR/TE=5325/79.93ms) Inversion of contrast is not a new finding. Zhou (2001) has described inverted contrast at 1.5 T in the occipital lobe (this region is not shown in images here). Inverted contrast may be due to the naturally occurring distribution of iron throughout the brain and/or the cytocarchitecture of the gray matter. As discussed in Chapter 8, this increases the difficulty in doing quantitative work. Therefore, selection of ROIs must consider layering effects. Ideally, high field ROIs should be selected such that different cortical layers are 187 differentiated; however, the spatial resolution on our study is too low to do this effectively and consistently. With the short T2s at high field, the signal values at long TEs are typically at or below the noise level (refer to Figure 9.7). Much of the data used for the combined analysis were within the noise levels and again increase the scatter. Figure 9.7: Signal to noise comparison for the various Hahn TEs. Dark solid lines are signal in cortical gray matter and dotted line is the noise level outside of the cadaver head. For TE of 134.4 ms, the signal is within the noise level and increases the scatter in fitting routines. Better selection of TEs is necessary for the proper calculation of γ2G2D. 188 TRANSVERSE RELAXATION AND COMBINED ANALYSIS MAPS Example calculated maps are shown in Figure 9.8. The color scheme was set identically for Hahn, CPMG, and intrinsic T2 to allow for visual comparison of T2s for the different techniques. However, this fails to depict the gray and white matter contrast in the Hahn spin echo maps. 189 Figure 9.8: Sample data from 8 T from subject N. A) Hahn, B) CPMG, C) Intrinsic T2, and D) 2 2 2 2 γ G D maps. The γ G D map demonstrates high gray/white contrast. The regions of the lateral temporal lobes are unreliable due to non-90° flip angles. The color scheme was set identical on Hahn, CPMG, and intrinsic T2 to allow for visual comparison of T2s with the different techniques. Note that this choice fails to depict GM/WM contrast in Hahn T2s. The nuclei were always prominent on the relaxation time maps. These areas possess the greatest iron concentrations within the brain. Therefore, it would be expected that the T2s of these regions would be extremely short to reflect this iron deposition. 190 While it is not obvious in Figure 9.8, there is contrast between gray and white matter in the Hahn and CPMG spin echo images. T2 values were lowest for the Hahn sequence and highest for the intrinsic T2. This follows from the nature of the Hahn sequence, which is more susceptible to the effects of diffusion and localized gradients leading to increased phase dispersion. Cortical regions (superior portion of the brain) typically demonstrate T2s lower for gray matter than white matter. This was often, but not always, observed to be inverted for the cingulate cortex (Refer to Figures 9.2 and 9.5). Also, this inverted T2 was seen in the medial temporal lobe. The lateral temporal lobes could not be evaluated in most cases as these areas are subject to low flip angle and coil receive sensitivity. On the magnitude images, these areas have a very low signal-to-noise ratio (SNR). This is observed in Figure 9.8 as inaccurate T2 values in the Hahn and CPMG maps and increased scatter in the Combined Analysis maps. In Figure 9.4, there is a loss of T2 values in the central region around the ventricles. This is explained by these regions experiencing flip angles greater than 90/180°. Inaccurate T2 values and/or increased scatter, similar to the lateral temporal lobe, is also observed as a result. The contrasts of the γ2G2D map is very similar to the signal contrast of the TE=50 ms image. This indicates that 8 T images are heavily-weighted by susceptibility effects (localized iron, hemoglobin, etc). Therefore, we would expect that the γ2G2D map that describes susceptibility effects should reflect the variability in tissue iron content. The nuclei are rich in iron and demonstrate high 191 γ2G2D and low T2. The inversion of gray and white matter (cortical versus cingulate and medial temporal lobe) is also observed in most cases when local flip angle and receive sensitivies did not result in a loss of signal. A summary of the averaged results are shown in Table 9.2. The subjects are divided into Alzheimer’s disease (not mixed dementias), other disease, and normal controls. T2 values for one cortical gray and white matter ROI and the hippocampal and parahippocampal ROIs are shown in the table. 192 Inferior 2 2 Hahn T2 CPMG T2 Intrinsic T2 γ G D Cortical (ms) (ms) (ms) (x10-5ms-3) Gray Matter AD 37.7±3.5 56.8±3.6 53.2±6.2 2.1±0.6 Other 41.2±3.7 64.3±5.0 37.5±4.2 1.2±0.3 Normal 42.6±4.1 70.4±4.9 42.1±12.3 0.3±0.7 All 40.6±2.1 63.9±2.9 48.2±4.5 1.6±0.3 Hahn T2 CPMG T2 Intrinsic T2 γ2G2D White Matter (ms) (ms) (ms) (x10-5ms-3) AD 37.7±3.0 61.9±4.0 56.1±5.8 2.1±0.6 Other 37.1±2.8 54.8±2.8 47.7±7.2 2.1±0.8 Normal 42.3±2.3 66.6±3.2 27.4±2.1 0.7±0.2 All 39.0±1.5 60.0±1.8 53.2±3.6 1.8±0.4 Hippocampal Hahn T2 CPMG T2 Intrinsic T2 γ2G2D Gray (ms) (ms) (ms) (x10-5ms-3) AD 48.6±5.9 77.7±7.7 40.6±6.9 0.6±0.4 Other 44.0±3.0 64.5±3.6 43.5±9.3 0.8±0.4 Normal 55.2±5.2 82.5±8.2 61.9±3.6 0.8±0.4 All 49.3±2.4 75.0±3.5 43.6±6.8 0.7±0.2 Parahippo- Hahn T2 CPMG T2 Intrinsic T2 γ2G2D White Matter (ms) (ms) (ms) (x10-5ms-3) AD 36.1±3.2 48.6±3.5 35.6±4.4 2.8±0.8 Other 36.9±2.6 51.5±2.8 58.3±7.1 2.6±0.8 Normal 41.2±2.0 60.0±1.8 58.9±1.7 1.8±0.4 All 38.4±1.5 54.3±1.7 52.3±3.6 2.6±0.4 2 2 Table 9:2: Summary of In situ Hahn, CPMG, Intrinsic T2, and γ G D data for cortical and medial temporal lobe gray and white matters. Averages for AD include cases B-I, other dementia A, M- R, and normal controls J-L. These data are mean values from non-normal distributions. However, this does provide some information on the typical gray and white matter values and contrasts at 8 T. Also, a Student’s t-test was performed on 193 each ROI with these means and some were found to have statistically significant p-values (listed below). AD versus Other Dementias • Inferior cortical gray matter, left – Hahn (p=0.039) • Hippocampus gray matter, left – CPMG (p=0.046) AD versus Normal Controls • Inferior cortical gray matter, left – CPMG (p=0.011) • Inferior cortical gray matter, right – Hahn (p=0.005) • Inferior cortical gray matter, right – CPMG (p=0.027) • Superior cortical gray matter, right – Hahn (p=0.031) • Superior cortical gray matter, right – CPMG (p=0.041) Normal Controls versus Other Dementias • Superior cortical white matter, left – Hahn (p=0.043) • Superior cortical white matter, left – CPMG (p=0.017) • Superior cortical gray matter, right – CPMG (p=0.002) • Superior cortical white matter, right – CPMG (p=0.045) • Hippocampus gray matter, left – Hahn (p=0.044) • Hippocampus gray matter, left – CPMG (p=0.050) • Hippocampus white matter, left – Hahn (p=0.050) • Hippocampus white matter, left – CPMG (p=0.046) The most noticable results from this statistical comparison is that only the Hahn and CPMG T2 were able to differentiate groups while the 194 combined analysis does not provide additional information that aids in the discrimination. CORRELATION TO IRON CONTENT The following data are independent measurements of iron content and the correlation to the combined fit analysis of Hahn and CPMG is discussed. This includes validation of the techniques using iron oxide samples, in situ cadaver to in vitro slice comparison, and finally in situ comparison to mass spectroscopy. ASSESSMENT OF COMBINED SPIN ECHO FIT ANALYSIS & IRON CONTENT MEASUREMENTS Iron oxide samples provided a means for evaluation of the combined analysis technique. By using an imaging protocol comparable to the in situ studies for these samples (TEs, FOV, matrix adjust accordingly) and plotting the results against known iron content, we obtained a description of the correlation of each term with concentration of total iron (similar behavior with concentration of paramagnetic gadolinium content was seen in Chapter 7). The result was that R2 was linear and γ2G2D was quadratic with concentration of total iron. A B 195 0.05 16 14 0.04 12 ) -3 10 0.03 ms -5 8 D (x10 2 R2 (1/ms) 0.02 G 6 2 γ 4 0.01 2 0 0 0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 [Fe] (ppm) [Fe] (ppm) Figure 9.9: Iron oxide data for combined analysis. A) Intrinsic R2 showing a linear dependence 2 2 on total iron concentration. B) γ G D showing quadratic behaviour. The same linear and quadratic trends were observed for iron oxide as was seen in Chapter 7 for Gd-DTPA for intrinsic T2 and γ2G2D, respectively. COMPARISON OF IN SITU CADAVER AND IN VITRO BRAIN SLICE COMBINED FIT RESULTS Because of the soft consistency of fresh brain tissues, it is nearly impossible to exactly match brain regions of interest with actual tissue specimens cut at autopsy. Yet previous studies demonstrated that image contrast and relxation properties of formalin fixed and fresh tissues (Dashner et al., 2003; Vymazal et al., 1996b) are very different for in situ, in vivo, and in vitro brain. In order to assess if a cut section of brain has similar relaxation parameters as in situ, we reimaged brain sections that were cut to approximately the imaged in situ slice. 196 Figure 9.10 demonstrates well the actual anatomy of the slice tissue. Also included is a photographic image of the same slice. These MR images of the slice typically had high SNR, except for regions on either side of the slice that are associated with areas of low receive sensitivity for the dual strut coil. 197 A B C Figure 9.10: Example data for Mass-Spectroscopy for subject C. A) In situ image with ROIs marked in light gray, B) In vitro image with ROIs, and C) photographic image of the brain slice. 198 Pixel-wise, mono-exponential fit to the Hahn, CPMG, and the combined fit for the brain slice in saline are showin in Figure 9.11. Visual comparison between in vitro T2 and γ2G2D maps of the brain section and the in situ data (Figure 9.8) suggest similar T2s for Hahn, CPMG, and combined T2 in the frontal lobe. However, the flip angle was very close to our 70° limit for the slice while typically near 90° in situ. In the brain slice temporal lobe, T2 values are higher with decreased contrast between gray and white matter. Comparison of this region to in situ data is difficult to do low flip angles in that region. Finally, brain slice γ2G2D are visually lower in the brain slice (Figure 9.11E) compared to in situ (Figure 9.8D) 199 0 Figure 9.11: Example slice image and maps, subject P. The loss of signal along the left side of the container is due to low receive sensitivity. The other loss of signal in the lower, central portion of the image is due to high flip angles. A) Hahn SE TE=50 ms, B) Hahn T2 map, C) CPMG T2 2 2 map, D) Intrinsic T2 map, E) γ G D map, and F) B1 field map. 200 Comparison of the combined spin echo results between in situ and in vitro is important for understanding how tissue parameters may change once removed from the body. Therefore, we evaluated combined fit results for similar regions of interest (limitation due to imprecise cutting of tissues by the pathologist) in situ and in vitro. These are shown in Figure 9.12. A Intrinsic T2 B γ2G2D 100 7.E-05 6.E-05 80 5.E-05 60 4.E-05 y = 0.3233x Slice Slice 3.E-05 40 y = 1.0226x 2.E-05 20 1.E-05 0 0.E+00 0 10 20 30 40 50 60 70 80 90 100 0.E+00 1.E-05 2.E-05 3.E-05 4.E-05 5.E-05 6.E-05 7.E-05 Cadaver Cadaver 2 2 Figure 9.12: Comparison of the intrinsic A) T2 and B) γ G D in In situ cadaver and In vitro brain slice. The solid pink line represents the 45° line. The solid blue link represents the least-squares fit of the data (equation shown on graph). A majority of the slice data is similar to the in situ data for T2 (points lie along the 45° line). This can be attributed to the immediate scanning of the slice upon removal at autopsy. T2 appears to follow the 45° line on Figure 9.12. Conversely, a large portion of the γ2G2D points lie below the 45° line. This would indicate that the In vitro data are lower than in situ (as concluded above from visual inspection). There are several reasons for this observation. First, there may be a bad fit of the signal data. The SNR is low on TEs of 90 ms and greater and this would reduce the accuracy of the combined analysis. The in vitro slice is not uniform in thickness; 201 therefore, partial voluming with saline is common in the slice images. Also, as stated prevoiusly, Vymazal (1996b) observed that relaxation rates change over time for fresh tissue. The saline may be altering the tissues by hydrating tissues or diluting the paramagnetic materials. Finally, there is a loss of blood in the brain tissues upon autopsy. This decreases the paramagnetic deoxyhemoglobin content. COMPARISON OF IN SITU R2 AND γ2G2D WITH MASS SPECTROSCOPY The in situ T2 data were compared to mass spectroscopy measurements of total iron within the tissues of different brain regions (see Figure 9.13). While the range of iron content is similar to what was observed in the iron oxide samples (Figure 9.9), the calculated values for γ2G2D are relatively similar across the iron content range causing a small non-linear term. An explanation for this observation may result from the motion narrowing regime conclusions of Chapter 7. In situ images demonstrate profound atrophy, which is accompanied with increased water content within tissues. Therefore, the trend in Figure 9.13 may be more similar to the 0 or 3% BSA concentration data of Figure 7.7 where the diffusion constant is much lower than for higher BSA concentrations. 202 A Nuclei 80 14 70 12 60 10 50 ) -3 ) -1 8 40 D (ms 2 6 G T2 (ms 2 30 γ 4 20 10 2 0 0 20 40 60 80 100 120 140 160 180 Total Iron (µg/g) Hahn CPMG Intrinsic g2G2D B Gray Matter 80 8 70 7 60 6 50 5 ) -3 ) -1 40 4 D (ms 2 G T2 (ms 2 30 3 γ 20 2 10 1 0 0 20 30 40 50 60 70 80 90 Total Iron (µg/g) Hahn CPMG Intrinsic g2G2D C White Matter 80 8 70 7 60 6 50 5 ) -3 ) -1 40 4 D (ms 2 G T2 (ms 2 30 3 γ 20 2 10 1 0 0 20 30 40 50 60 70 80 90 Total Iron (µg/g) Hahn CPMG Intrinsic g2G2D Figure 9.13: In situ data for combined analysis. A) nuclei, B) gray, and C) white matter results for Hahn, CPMG, and Combined Fit correlation to mass spectroscopy results. The information on the correlation is found in Table 9.3. 203 Figure 9.13 demonstrates MRI results correlated with total iron content using a linear relationship for T2 and quadratic relationship for γ2G2D. The results from this correlation are shown in Table 9.3 with the R2 value for each. For gray matter, the quadratic fit result was negative. All R2 values are low, reflecting the scatter within this data. Nuclei Ax B R2 Hahn -0.02 39.36 0.12 CPMG -0.16 70.78 0.55 Intrinsic T2 0.06 42.11 0.12 Ax2 Bx R2 γ2G2D 3e-5 0.07 0.67 Gray Matter Ax B R2 Hahn -0.09 37.94 0.02 CPMG -0.13 59.36 0.07 Intrinsic T2 -0.06 59.17 0.02 Ax2 Bx R2 γ2G2D -2e-4 0.09 0.15 White Matter Ax B R2 Hahn -0.10 47.08 0.10 CPMG -0.25 71.96 0.25 Intrinsic T2 -0.09 65.07 0.03 Ax2 Bx R2 γ2G2D 6e-4 0.02 0.50 2 2 Table 9.3: Correlation fit results from T2 and γ G D with total iron content measured by mass spectroscopy. T2 results have very little correlation with total iron content. This is evident by the extremely shallow slope for these plots. 204 While the majority of these subjects had a history of dementia or disease, the mass spectroscopy results were compared to the age-dependent Hallgren (1958) calculations for total brain iron. These results are shown in Figure 9.14 below. The Huntington disease patient iron content was much higher than what was to be expected for a normal control of the same age. If this case is ignored, the best-fit line through the remaining subjects has a slope of approximately 0.8. Several of the cases in which mass spectroscopy was performed were AD subjects. This slope indicates that the total iron content as measured by mass spectroscopy was higher than what would be expected for a normal control subject of the same age. 205 200 180 g/g) µ 160 140 120 N P B 100 R y = 0.8784x C 2 R = 0.8149 Q 80 E 60 40 Iron Content via Hallgren Calculations ( 20 0 0 20 40 60 80 100 120 140 160 180 200 Iron Content via Mass Spectroscopy (ppm) Figure 9.14: Hallgren equations compared to the mass spectroscopy results. When the Huntington’s disease is removed, the least-squares fit through the data results in the solid, red line (equation included on graph). APPLICATION TO AD Hahn T2s are shorter than CPMG T2s due to greater susceptibility and diffusion effects because of the longer effect TE. The combined fit distributions were broader than the Hahn and CPMG (see below). CPMG distributions were also broader than the Hahn equivalents. There were no trends in AD or other dementias compared to the normal control. In Table 9.2, the Hahn 206 spin echo data were beginning to indicate a difference between AD, other dementias, and the normal controls. However, these are mean values and as discussed previously, our data are not gaussian. The skewed distributions on some subjects may be leading to a false impression that Hahn T2 results are different. 207 Figure 9.15: Hahn Distributions for gray matter In situ subjects. 208 Figure 9.16: Hahn distributions for white matter In situ subjects 209 Figure 9:17: CPMG T2 distributions for gray matter In situ subjects. 210 Figure 9.18: CPMG distributions for white matter In situ subjects 211 Figure 9.19: Intrinsic T2 distributions for gray matter In situ subjects 212 Figure 9.20: Intrinsic distributions for white matter In situ subjects 213 2 2 Figure 9.21: γ G D distributions for gray matter In situ subjects 214 2 2 Figure 9.22: γ G D distributions for white matter In situ subjects A threshold for statistical bootstrap analysis was selected using the differences between hemispheres for the one normal control. The value for the threshold was set equal to the greatest lower bound of the 90% confidence levels for similar regions of interest in the right and left hemispheres. 215 2 2 A Gray Matter Benchmark 2Gγ 2GD D 0.287642 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 95% Confidence Levels 2 2 2 2 B White Matter Benchmark Gγ GD D 0.213495 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 95% Confidence Level Figure 9.23: Example of the thresholding technique for A) gray matter and B) white matter. The colored lines represent pair-wise comparisons for regions of interest between two hemispheres. The dark, verticle lines are the threshold level. This threshold level was used against AD cases in a pair-wise fashion. However, there were no trends distinguishable to say that one region 216 was consistently and statistically different in AD from the normal controls. It again must be noted that there was an n=1 for the normal controls. Therefore, thresholding was based on intra-cadaver differences. The combined bootstrap results are shown below in Figure 9.24 for the inferior cortical gray and white matter ROIs. The red indicates significantly different from inter-hemisphere difference of one normal control (subject J). Gray indicates not significantly different. T2 H G F B E C Gray White γ2G2D H G F B E C Gray White Figure 9.24: Sample bootstrap results for cortical gray and white matter ROIs. This analysis was not fully evaluated, as there was only one normal control. It would ideal if more normal control data could be added in future studies. 217 UTILITY OF TECHNIQUES By now it is obvious that there is not additional information obtained from the combined spin echo analysis that aids in the differentiation of AD and other study groups. The simplest (though not most accurate) evaluation of the data using a Student’s t-test indicated that only the Hahn and CPMG spin echo sequences were capable of finding statistical differences. The bootstrap method was inadequate in that the thresholds were based on the inter-hemisphere comparisons of one normal control. The distributions do not offer any additional information in the differences between groups. From the correlation of mass spectroscopy iron measurements to MRI data, the high R2 values indicate that the γ2G2D correlates to iron content better than any of the T2 measurements. This was only for the nuclei and white matter as γ2G2D for gray matter had a negative quadratic relationship with total iron content. Therefore, if artifact free data are collected with signal well above the noise level, then the combined analysis appears promising in the evaluation of paramagnetic content. Meanwhile, the Hahn and CPMG data posses less scatter and appear to correlate just as well with total iron content as the intrinsic T2. In the evaluation of tissue contrasts and differences, there does not appear to be a benefit to the additional computations. 218 CONCLUSIONS The images acquired from in situ cadaver show good gray and white matter contrast and have high resolution. However, there was a wide variation in the B1 field (low signal in the lateral temporal lobes and inferior portion of the ventricles). This renders these regions useless for quantitative T2 studies as the flip angles are outside the 90±20° criteria. The two layers of gray matter (microscopic anatomical features) that are observed in the hippocampus specimens are not seen consistently seen in the in situ data. There is inverted contrast in many areas of these data (i.e. cingulate and medial temporal lobe). As a result, specific details on the location of regions of interest must accompany gray and white matter comparisons. We are unsure whether these changes are due to iron content and/or cytoarchitecture. The combined analysis data did not appear to offer different trends or additional information compared to the Hahn and CPMG mono-exponential decay. If the mean value for each ROI is examined, then some ROIs are found to be statistically different between AD, normal controls, and other dementias. However, this analysis is not accurate in that the distributions that were tested are not gaussian. The combined analysis appears to provide fairly reliable measurements of the intrinsic T2 while the γ2G2D possess broad distributions. These are acceptable if the broad distributions are due to the diffusion and susceptibility effect and not bad fits of the data. 219 For in vivo study with the combined analysis, it will be necessary to shorten the acquisition time. The γ2G2D term has its greatest effect on the fitting routine for longer interpulse times and so the Hahn pulse sequence is more sensitive to the gradient and diffusion effects (leading to the shorter T2s). Shortening the acquisition time by acquiring two CPMG sequences with varying τ would not be ideal. If a long τ is used, then the number of echoes acquired would be decreased, which would then decrease the number of observations for the fitting routine). We hypothesized that the combined analysis offers a means of estimating iron in a non-invasive manner. We are not observing this to be true. The more complicated fit of the data appears to add scatter to the data making analysis more difficult. Better selection of TEs in the future may increase the power of this analysis. From the iron oxide data presented here (and the Gd- DTPA data of Chapter 7), we are concluding that the relationship between γ2G2D and iron content is quadratic while the relationship with T2 is linear. Another consequence of the poor selection of TEs is the inability to properly assess relaxation time measurements in the nuclei. The shortest TEs do not adequately sample the decay curve in the nuclei. This is unfortunate because the nuclei provide interesting iron measurements compared to gray and white matter. In vitro comparison of the brain slice to calculated T2s for the in situ specimen show good correlation, especially for the T2 terms, but not γ2G2D. This 220 is encouraging as the ability to cut fresh brain tissue is difficult and exact one-to- one correlation with gyri and sulci did not occur in this study. Using in vitro data would also allow precise total iron calculations with laser ablation mass- spectroscopy. 221 CHAPTER 10 IN VIVO STUDY OF NORMAL CONTROLS The final series of experiments within this dissertation research focus on investigating the feasibility of implementing the combined spin echo analysis to in vivo study. However, correlation of MR results with total iron content presents a new problem for this study. Sampling of tissue for direct measurement by independent means (such as mass spectroscopy) would be an invasive procedure. Therefore, the equations derived by Hallgren (1958) must be used. Finally, the acquisition of images in vivo in the coronal plane at 8 T leads to additional problems not addressed thus far in this dissertation. The extended acquisition time used for in situ studies would not be tolerated by in vivo subjects. Also, motion and flow artifact suppression methods should be included within the pulse sequence; however, this study did not include these precautionary steps. METHODS The description of the subjects is as follows: 222 Age Sex Identification Comments 20 F A Only 2 Hahn SE 26 M B 29 F C Low SNR & 34 M D Flow Artifacts 49 F E 51 F F Flow Artifacts Table 10.1: Summary of in vivo subject population The protocol for in vivo image acquisition was only slightly different from the in situ protocol. The changes included a 512x256 matrix, a NEX=1 for a final in-plane resolution of 312x625 µm2. Total scan times were approximately 7 minutes for each spin echo. The gradient echoes for the construction of B1 maps were also slightly different. Repetition time was 4000 ms with a matrix of 256x64 matrix. Total scan times were 4 minutes per each gradient echo. Post processing of the images was similar to the methods described in Chapter 9. Images were filtered with a Hanning function (Fourier transform filtering, IDL) prior to fitting the combined analysis equation. The ROIs were the same as though sampled for the in situ study. Total iron content was calculated from the Hallgren (1958) age dependent equations. 223 RESULTS IMAGE APPEARANCE The first observation for in vivo images is the decreased SNR compared to in situ images. The protocol for in vivo includes only a NEX (number of excitations) of 1. There is also the slight loss of in-plane resolution in the phase encode (625 versus 417 µm) direction and flow artifacts associated with the cerebral spinal fluid around the brainstem and the ventricles. The general description of the images was similar to the observations within Chapter 9. The nuclei are prominent in all TEs and gray matter was lower in signal than white matter. CSF appears bright in signal for even the TE of 50 ms. There is very little gray and white matter contrast until TEs of 50 and 90 ms. Due to the lower signal to noise and spatial resolution, it was not possible to observe the inversion of the image contrast in the cingulate (as in in situ cases). The inversion of contrast in the medial temporal lobe was difficult to assess due to increased flow artifacts in that region (a result of the CSF within the brainstem). 224 A B C D Figure 10.1: Sample In vivo spin echo images with TR=1500 and TE of A) 21, B) 50, C) 90, and D) 134.4 ms. There is increased noise throughout the image as compared to the in situ version with similar TR and TE. The lateral temporal lobes are lost due to low flip angle and receive sensitivity. The signal to noise comparison shows that the shortest TE is very close to the noise level in the phase encode direction predominently due to ghosting from CSF motion in the ventricle. Using the noise in artifact free regions 225 in the frequency encode direction leads to a more favorable result (SNR≈17 for TE=20 ms, SNR≈2.5 for TE=134.4 ms). Nevertheless, TE will have to be optimized in future studies. Another pulse sequence may be used to acquire similar data that does not require five lengthy scans nor the B1 field dependence. Truong (2004) has investigated these with the GESSE sequence. The findings include GESSE resultant T2s that are similar to the Hahn T2s of the studies included in this dissertation. A GESSE/CPMG combination may substitute the current protocol as the use of two CPMG spin echoes will most likely not adequately describe γ2G2D. 226 Figure 10.2: Signal to noise comparison of In vivo data for A) the shortest Hahn TE and B) the longest TE. Thick line is the signal of a gray matter ROI, thin line is the noise in the read direction, and the dotted line is the noise in the phase direction near a flow artifact. RELAXATION TIME AND COMBINED ANALYSIS MAPS The increased noise and ghosting is carried through to the calculated maps and results in increased scatter. Even the γ2G2D maps do not exhibit the distinct gray and white matter differentiation as seen in the in situ image and is likely due to the low SNR and inadequate fit of this term in the combined analysis. With this increased noise, ghosting, and decreased spatial resolution, it is not clearly observed whether there is the relation time inversion or the layering of gray matter discussed in both Chapters 8 and 9. The cortical gray and white matter areas demonstrate a large amount of signal loss and scatter throughout. 227 This is most likely due to two reasons. First, voxels that were eliminated by the flip angle criteria of Chapter 6. Second, there were also low flip angles and receive sensitivities on images in the area of the temporal lobes. A 150 B 150 mono-exponential Hahn mono-exponential CPMG 0 0 C D 150 6e-5 Combined T2 2G2D 0 γ 0 Figure 10.3: Sample calculated maps from in vivo data. A) Hahn, B) CPMG, C) Intrinsic T2, and 2 2 D) γ G D maps. 228 Mean values for cortical gray and white matter are shown below in Table 10.2 for all in vivo studies. The images from two subjects had hippocampal regions that were not clearly visible for the delineation of ROIs due to flow artifacts. T2 values are slightly longer than for the in situ studies. Hahn T2 CPMG T2 Intrinsic T2 γ2G2D Region -1 -1 -1 (ms ) (ms ) (ms ) (x10-6ms-3) Cortical 58.9±3.0 62.3±1.8 61.3±1.4 11.6±1.9 Gray Matter White Matter 58.2±1.8 64.6±2.5 61.4±4.8 11.2±1.2 Table 10.2: Hahn, CPMG, and Combined Spin echo Analysis values for gray and white matter in vivo averaged over the age range. Distributions corresponding to the data in Table 10.2 are shown below for each subject. The distributions are stratified according to age (youngest at the bottom). 229 Figure 10.4: Hahn distributions for gray and white matter. The subjects are stratified by increasing age. Note that the T2 for subject A was calculated from only 2 TE values, since the subject could not sustain the long scan. 230 Figure 10.5: CPMG distributions for gray and white matter. The subjects are stratified by increasing age. 231 Figure 10.6: Intrinsic T2 distributions for gray and white matter. The subjects are stratified by increasing age. Note that for subject A Hahn echoes were acquired with only two TEs. Subject D may have moved between scans leading to errors in the calculation of the combined fit. 232 2 2 Figure 10.7: γ G D distributions for gray and white matter. The subjects are stratified by increasing age. With a few exceptions, in vivo T2 and γ2G2D distributions appear to be narrower and somewhat more consistent than in situ data. Reasons for this are not entirely clear. There is a slight trend toward shorter T2s with age in the Hahn spin echo (the youngest subject was acquired with only two CPMG SE). 233 CORRELATION TO THE HALLGREN EQUATIONS FOR TOTAL BRAIN IRON Without the ability to directly measure the total iron content for in vivo volunteers, the use of the Hallgren equations is necessary for correlation to fit results. The age dependent functions were used to estimate the total iron within the parietal gray matter (cortical gray matter ROI above cingulate), frontal white matter (white matter ROI in gyri above cingulate) and correlated with the respective MRI ROI data. The results of the age-dependent functions are shown in Table 10.3 Hallgreen Iron Calculations A B C D E F (µg/g) Parietal GM 20.9 25.6 27.9 28.5 29.1 29.7 Frontal WM 37.3 39.7 40.4 41.3 42.3 42.4 Table 10.3: Hallgren age-dependent total iron calculation results for in vivo subjects. Plots of this calculated data with relaxation time results are shown below for gray and white matter with the Hahn, CPMG, intrinsic T2, and γ2G2D results. 234 Gray Matter 0.03 4.E-05 3.E-05 0.025 3.E-05 0.02 ) -3 ) -1 2.E-05 0.015 D (ms 2 2.E-05 G R2 (ms 2 γ 0.01 1.E-05 0.005 5.E-06 0 0.E+00 20 25 30 35 40 45 50 Total Iron (µg/g) Hahn CPMG Intrinsic gamma term White Matter 0.03 4.E-05 3.E-05 0.025 3.E-05 0.02 ) -3 ) -1 2.E-05 0.015 D (ms 2 2.E-05 G R2 (ms 2 γ 0.01 1.E-05 0.005 5.E-06 0 0.E+00 20 25 30 35 40 45 50 Total Iron (µg/g) Hahn CPMG Intrinsic gamma term Figure 10.8: Correlations between the Hallgren total iron content and fit routine results. T2s 2 2 were correlated using a linear relationship while γ G D used a quadratic The least-square regression reults and R2 for the correlations in Figure 10.8 are below in Table 10.4. 235 Ax B 2 Gray Matter -4 -1 -1 -2 -1 R (x10 g*ms µg ) (x10 ms ) Hahn R2 -12.5 5.3 0.45 CPMG R2 -2.4 2.3 0.20 Intrinsic R2 -1.6 2.0 0.06 2 Ax Bx 2 -7 2 -1 -2 -7 -1 -1 R (x10 g *ms µg ) (x10 g*ms µg ) γ2G2D 0.29 -2.62 0.25 Ax B 2 White Matter -4 -1 -1 -2 -1 R (x10 g*ms µg ) (x10 ms ) Hahn R2 -8.3 5.6 0.09 CPMG R2 -4.8 3.6 0.16 Intrinsic R2 -1.8 2.5 0.02 2 Ax Bx 2 -7 2 -1 -2 -7 -1 -1 R (x10 g *ms µg ) (x10 g*ms µg ) γ2G2D -0.14 9.39 0.002 Table 10.4: Least-squares results from the correlation of total brain iron to Hahn, CPMG, 2 2 intrinsic T2, and γ G D. There is not a good correlation between the calculated results from the MRI data and estimated iron content. R2 should increase with iron content, but these results indicate the opposite trend. It is unclear whether this is a result of inadequate estimation of total brain iron by the Hallgren equations or if the combined analysis results do not actually reflect total iron. CONCLUSIONS Quantitative in vivo study at 8 T is extremely difficult. For qualitative evaluation, the images produced can show excellent vascularity and depending on the selection of TE, good gray and white matter differentiation. 236 However, the combined spin echo did not yield clean images nor relaxation maps. Another method must be pursued that will increase the signal-to-noise ratio of these images. Better selection of TEs would benefit this data significantly. Also, if flow artifacts are not suppressed, imaging in another plane (away from CSF) would decrease the amount of artifacts within the images. Despite these problems, the distributions shown in Figure 10.4-10.7 appear narrower than the in situ data. With some exceptions, there are not large deviations in T2s or γ2G2D values across subjects, regardless of age. This would seem to indicate that the wide range of values observed in situ may be due to the disease state. Further sutides are needed to evaluate these open questions. 237 CHAPTER 11 CONCLUSIONS Magnetic resonance imaging studies at low field strengths (mostly at 1.5 T) have indicated that the signal relaxation is sensitive to total iron content. These measurements were made in many regions of the brain and have been correlated with age-dependent increases in iron and with several diseases (specifically considered for this dissertation research was Alzheimer’s disease). We therefore hypothesized that the 8 T, whole body MRI system would be even more sensitive to the increased iron content in Alzheimer’s disease because of the increased sensitivity to susceptibility effects. More specifically, we expected to find an increased sensitivity to total iron content in the γ2G2D term of the combined spin echo analysis due to a quadratic dependence to susceptibility gradients. Gadolinium-DTPA in the presence of macromolecules (BSA) does not exhibit the expected relaxivity trends with monoexponential decay fitting routines for T2. The combined spin echo analysis was found to adequately fit the data where R2 was linear and γ2G2D was quadratic with Gd-DTPA concentration. When evaluated with BSA concentration, γ2G2D was found to increase 238 quadratically while R2 had a maximum value. This was also observed in iron oxide particle solutions. In situ cadaver images presented interesting contrast inversions in the cingulate cortex and medial temporal lobe compared to cortical areas. T2 values and γ2G2D were all correlated with independent total iron concentration by mass spectroscopy. There was very little decrease in T2 with total iron with extremely small correlation coefficients. γ2G2D had better correlation with iron using a quadratic fit. The combined fit did not appear to offer any additional information than the Hahn and CPMG individually. In vivo images had severe flow motion artifacts and fit results had very little gray and white matter contrast. Hallgren estimations of total brain iron were used for correlation with MR data. These correlations show the opposite trend from what was expected, i.e. decreasing R2 with total iron. Alzheimer’s disease tissues have a 10-20 ppm increase in total iron content according to mass spectroscopy and chemical studies (Emmett, 1989; Crapper et al., 1978, Cornett, 1998). In vitro studies were unable to differentiate AD from normal controls, but we believed this to be due to formalin fixation. There was also no differentiation between in AD and normal control in situ. There are two factors to consider: 1) the current method does not appear to be sensitive enough to detect a 20 ppm increase in iron and 2) only one normal control was available for comparisons. There are several reasons for the deviations between our findings and our hypothesis. First, there is the suboptimal data. This is a result of a poor 239 choice of TEs, extremely low SNR for long TEs, in vivo patient and flow motion artifacts, and degradation of T2 fits by CSF signal overwhelming parenchyma contrast by partial volume effects. Another reason for the discrepancy is that the model selected may not apply to our data. Carr and Purcell originally derived the combine spin echo equation for diffusion in linear gradients. Mujumdar (1988) 2 2 extended this equation to non-linear gradients by replacing G with Fitting to that equation implies that spins are either in the motion narrowed regimen where R2intrinsic (outer sphere) dominates or in the static rephasing 2 2 2 2 regimen where γ However, increased atrophy leads to increased water content and moves these spins back into the motion-narrowing regime. FUTURE WORK Continuation of this dissertation work should continue after re- evaluation of the current protocol. Better T2 measurement techniques should be investigated. Optimization of TE selection with the known T2 values from this dissertation 240 (where nuclei T2<20 ms). Most importantly, it would be necessary to confirm adequate SNR for every TE. In addition, TRs of 1500 ms are much too short for in vivo or in situ studies. Therefore, it will be necessary to evaluate the level of T1 contamination in these data by numerical solution of iterative Bloch equations. Other pulse sequence techniques and optimization should also be included in re-evaluating the protocol. The current Hahn and (especially) CPMG are highly sensitive to local flip angle variability leading to a loss of nearly 75% of data. Local techniques such as Bartha’s (2002) LASER sequence or optimization of crusher gradients (methods proposed by Majumdar, Poon, and Crawley in Chapter 6) are also items to be further investigated. Suppression of CSF signal through the use of a FLAIR (flow attenuated inversion recovery) sequence would reduce improper fits cause by partial volume effects with CSF. Motion compensation would reduce the number of artifacts of the in vivo images and allow analysis of regions that were excluded in this study (temporal lobe). The one factor that must change if in vivo correlation to iron content studies are continued is the length of acquisition time. The in situ studies required a minimum of two hours. The in vivo protocol was nearly an hour. Most subjects will not tolerate these long acquisitions and this increases the chance of motion and/or shift of the head placement within the ROI. A faster approach that continues to use the Carr and Purcell equation could include a GESSE sequence (which is less sensitive to B1 inhomogeneities) and a CPMG sequence (sensitive to B1 inhomogeneities). The Carr and Purcell equations become 241 2 2 R2GESSE=R2intrinsic + 1/12γ 2 2 R2CPMG=R2intrinsic + 1/12γ where TE is the central spin echo in the GESSE sequence and τ interpulse delay in CPMG. These two equations can be used to solve for the two unknowns 2 2 2 2 γ 2 2 2 2 R2intrinsic=τ R2GESSE+TE R2CPMG / [τ +TE ] Equation 11.4 Assessment of this method can be accomplished with data in this dissertation and from Truong (2004). Optimization of TEs will be required along with propagation of error to determine whether this will be sensitive enough to detect iron concentration. If evaluation of this method appears to be inadequate, then it may be necessary to exchange the Carr and Purcell equation with one that can be applied to a motion narrowed or intermediate regime. The Anderson-Weiss 242 model (Kennan et al, 1994; Anderson et al, 1953; Callaghan, 1991) may be appropriate for this sort of analysis. Phase imaging is still a promising prospect for correlation to total iron. In vivo phase images have excellent gray and white matter contrasts. These contrasts are lost in vitro and in situ and so we did not pursue that option in this dissertation. Finally, R2’ has been consistently shown to be sensitive to tissue iron. However, this is extremely difficult to measure at 8 T as the GESFIDE sequence. 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