Washington University in St. Louis
Washington University Open Scholarship Electronic Theses and Dissertations
1-1-2011
Diffusion Magnetic Resonance Imaging of Central Nervous System Diseases: Structure, Function and Pathology Qing Wang Washington University in St. Louis,
[email protected]
Follow this and additional works at: http://openscholarship.wustl.edu/etd Recommended Citation Wang, Qing, "Diffusion Magnetic Resonance Imaging of Central Nervous System Diseases: Structure, Function and Pathology" (2011). Electronic Theses and Dissertations. Paper 365.
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WASHINGTON UNIVERSITY School of Engineering and Applied Science Department of Mechanical Engineering & Materials Science Dissertation Examination Committee: Philip V. Bayly, Chair Sheng-Kwei Song, Co-Chair Ramesh K. Agarwal Dennis L. Barbour Anne H. Cross Guy M. Genin
Diffusion Magnetic Resonance Imaging of Central Nervous System Diseases: Structure, Function and Pathology by Qing Wang A dissertation presented to the Graduate school of Arts and Sciences of Washington University in partial fulfillment of the requirements for the degree of Doctor of Philosophy
August, 2011 Saint Louis, Missouri
ABSTRACT OF THE DISSERTATION Diffusion Magnetic Resonance Imaging of Central Nervous System Diseases: Structure, Function, and Pathology by Qing Wang Doctor of Philosophy in Mechanical Engineering & Materials Science Washington University in St. Louis, 2011 Professor Philip V Bayly, Chair Professor Sheng-Kwei Song, Co-Chair
Diffusion magnetic resonance imaging (MRI) is a noninvasive imaging modality widely used to probe the microstructure of central nervous system (CNS). Studies have demonstrated that progression of the CNS pathology may increase or decrease apparent diffusion coefficient (ADC) measured by diffusion MRI, and the axial and radial diffusivities derived from diffusion tensor imaging (DTI) have shown promises to separate axonal injury from myelin damage. Recently, diffusion MRI was also used to predict functional outcome in various CNS diseases. In this dissertation, diffusion MRI was first employed to image the normal retinal cell structure. The retinal cell pathology of retina degeneration-1 (rd1) mice was then evaluated by diffusion MRI and found that the photoreceptor cell death induced retinal vascular leakage causes increased inner retinal ADC in living rd1 mice. To further investigate the unknown relationship between
ii
DTI-derived biomarkers of axonal injury and myelin damage and changes in functional behavior of white matter tracts, in vitro electrophysiological recordings of compound action potential (CAP) were conducted to evaluate the function of mouse optic nerves after transient retinal ischemia. The correlation between CAP measurements and DTI injury markers was established. In this dissertation, a novel diffusion MRI method, diffusion basis spectrum imaging (DBSI), was employed to detect and quantify neuroinflammation as well as coexisting axon/myelin damage in a single setting. Our findings suggest that diffusion MRI is a promising noninvasive tool in investigating the structure, function and pathology of CNS.
iii
Dedication
To my family For their love and support.
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Acknowledgement With a grateful heart, I am writing this acknowledgment. When I was looking back the past six years, I found that I had been blessed with the best luck of the world and received so much support and help from so many wonderful people.
I have the beginner’s luck to start my career under the guidance and mentoring of Dr. Philip V. Bayly and Sheng-Kwei (Victor) Song. They accepted me and guided me through years of adventure on exploring the MRI world. Without their enlightening guidance, consistent support, and unreserved trust, I would not have been able to overcome the obstacles I encountered, and would not have been able to comprehend and appreciate the beauty of science.
I am lucky that I joined the productive team and have wonderful colleagues. I want to give my special appreciation to Junjie Chen, who taught me the necessary experimental knowledge and skills for MRI and animal studies. With his help, I start to establish my confidence in research. I’d also like to acknowledge our group labmates: Joong Hee Kim, Mattew D. Buddee, Junqian Xu, Shu-Wei Sun, Peng Sun, Yong Wang, Tsang-Wei Tu, Mingqiang Xie and Chia-Wen Chiang. Their expert experience with MRI has been of tremendous help to the completion of the studies. And also many thanks for Bill Spees and John Engelbach for creating a very enjoyable, inspiring research environment. Also many thanks to Debra Brouk and Alpay Ozcan for their excellent assistance in various administrative and technical issues.
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I am lucky that I have such a supportive thesis committee composing of top scientists on engineering and neurology: Dr. Ramesh K. Agarwal, Guy M. Genin, Dr. Anne H. Cross, and Dr. Dennis L. Barbour. I sincerely thank them for their enlightening comments, suggestions and help.
I am lucky that I have a happy family, where I can always find support, understanding, encouragement, and hope. I thank my parents and my in-laws for their love, support, and understanding. I don’t know how to express my appreciation to my husband, Yong Wang, for his love, help and sacrifice in the past six years. I can only say I’m really lucky to marry him. I sincerely appreciate what I have received from all these wonderful people.
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Table of Contents ABSTRACT OF THE DISSERTATION ........................................................................... ii Acknowledgement .............................................................................................................. v List of Tables ..................................................................................................................... ix List of Figures ..................................................................................................................... x List of Abbreviations ........................................................................................................ xii Chapter 1 1.1
Introduction ................................................................................................... 1
Physical Principles of the Phenomenon of Diffusion........................................... 1
1.1.1
Diffusion and Self-diffusion ......................................................................... 1
1.1.2
Fick’s Laws and The description of Self-diffusion ....................................... 1
1.1.3
How Does Diffusion Affect the MR Signal? ................................................ 4
1.2
ADC changes reflect the central nervous system injury ...................................... 5
1.3
Magnetic resonance measurements of ADC ........................................................ 8
1.3.1
Pulsed-Gradient Spin-Echo Sequence .......................................................... 9
1.3.2
Diffusion MR imaging ................................................................................ 14
1.3.3
Minimize or eliminate interference from non-diffusion gradients ............. 16
1.3.4
Diffusion tensor imaging (DTI) .................................................................. 18
1.3.5
The significance of DTI application ........................................................... 20
1.3.6
The limitations of DTI ................................................................................ 20
Chapter 2
Diffusion weighted MRI detects retina degeneration in rd1 mice .............. 23
2.1
Diffusion weighted MRI in the C57BL/6 wildtype mouse retina ...................... 23
2.1.1
Introduction ................................................................................................. 23
2.1.2
Methods....................................................................................................... 25
2.1.3
Results ......................................................................................................... 34
2.1.4
Discussion ................................................................................................... 40
2.2 Diffusion weight MRI detected photoreceptor degeneration and vasogenic edema in rd1 mice ......................................................................................................... 45 2.2.1
Introduction ................................................................................................. 45
2.2.2
Material and Methods ................................................................................. 47
2.2.3
Results ......................................................................................................... 51
2.2.4
Discussion ................................................................................................... 54
Chapter 3 Diffusion Tensor Imaging Detected Optic Nerve Injury Correlates to Decreased Compound Action Potentials following Murine Retinal................................. 61 3.1
Introduction ........................................................................................................ 61 vii
3.2
Methods: ............................................................................................................. 63
3.2.1
Animal Model ............................................................................................. 63
3.2.2
Electrophysiology ....................................................................................... 64
3.2.3
MRI ............................................................................................................. 65
3.2.4
Data Analysis: Electrophysiology and DTI ................................................ 66
3.2.5
Immunohistochemistry ............................................................................... 67
3.3
Results: ............................................................................................................... 71
Chapter 4 Diffusion basis spectrum imaging: Seeing white matter pathology not seen by diffusion tensor imaging .............................................................................................. 82 4.1
Introduction ........................................................................................................ 82
4.2
Materials and Methods ....................................................................................... 84
4.2.1
Diffusion Basis Spectrum Imaging (DBSI) ................................................ 84
4.2.2
Fixed Trigeminal Nerve Phantom............................................................... 88
4.2.3
Diffusion Weighted Spectroscopy of Fixed Trigeminal Nerve Phantom ... 89
4.2.4
Cuprizone Treatment .................................................................................. 89
4.2.5
In Vivo MRI................................................................................................ 90
4.2.6
Immunohistochemistry ............................................................................... 91
4.2.7
Data Analysis .............................................................................................. 92
4.3
Results ................................................................................................................ 94
4.4
Discussion ........................................................................................................ 103
Chapter 5
Conclusion and future work ...................................................................... 113
5.1
Establishing ADC as a biomarker of retina cell damage ................................. 113
5.2
Application of diffusion tensor imaging in white matter injury....................... 115
5.3
Diffusion basis spectrum imaging of heterogeneous white matter injury ........ 116
Reference ........................................................................................................................ 117 Curriculum Vita .............................................................................................................. 132
viii
List of Tables Table 1 (Table 2-1): MRI and histology determined thickness of the retinal cell layers, overall retina, and choroid. ............................................................................................... 35 Table 2 (Table 3-1): Summary of Axon and Myelin Integrity Measured by DTI and Histology ........................................................................................................................... 68 Table 3 (Table 3-2): DTI and Electrophysiology Measures of White Matter Integrity... 71 Table 4 (Table 4-1): DTI DBSI, Histological counting results. (count in #/mm2; directional diffusivity in m2/ms) .................................................................................. 112
ix
List of Figures Figure 1 (Figure 1-1): A schematic representation of the PGSE sequence. ..................... 10 Figure 2 (Figure 1-2): Skejskal-Tanner spin echo diffusion imaging sequence. .............. 15 Figure 3 (Figure 2-1): The sketches of mouse holder for retina imaging. ........................ 26 Figure 4 (Figure 2-2): Animal holder for in vivo MRI measurements. ............................ 27 Figure 5 (Figure 2-3): Diagram of the capacitive coupling coil. ...................................... 28 Figure 6 (Figure 2-4): MRI images of the mouse eye and the H&E stained slice of the retina. ................................................................................................................................ 33 Figure 7 (Figure 2-5): Calculated T1 and T2 maps and quantified T1 and T2 relaxation time constants in the mouse eye........................................................................................ 36 Figure 8 (Figure 2-6): Directional ADC maps of a mouse eye......................................... 37 Figure 9 (Figure 2-7): Quantified directional ADCs in the vitreous, MR-detected choroid and MR-detected retina layers. ......................................................................................... 38 Figure 10 (Figure 2-8): Calculated ADC map and quantified ADC of a mouse eye. .... 39 Figure 11 (Figure 2-9): H&E staining and picrosirius slices of the mouse retina. ........... 40 Figure 12 (Figure 2-10): Representative diffusion-weighted images and H&E stained slices of the eyes of 1- and 3-month old rd1 mice and age-matched wildtype mice. ....... 52 Figure 13 (Figure 2-11): Quantified directional ADC in each retinal layers of wildtype and rd1 mice...................................................................................................................... 53 Figure 14 (Figure 2-12): DWI determined mean ADC in the remaining retina of rd1 mice and in all MR-detected layers of wildtype mouse at of 1- and 3-month of age. ............... 55 Figure 15 (Figure 2-13): Gd-DTPA enchanced MR images of a 3-month old WT, 1- and 3-monthold rd1 mice at baseline and T1W signal enhancement in the same eyes and quantitative analysis. ......................................................................................................... 56 Figure 16 (Figure 3-1): DTI derived RA, Axial diffusivity and Radial diffusivity of control (top row), 3DPI (middle row) and 7DPI (bottom row) experiment groups. ........ 70 Figure 17 (Figure 3-2): Immunohistochemistry of injured and control optic nerves. ..... 72 Figure 18 (Figure 3-3): Electrophysiological measurements setup and typical input/output curves of CAP amplitude. ................................................................................................. 74 Figure 19 (Figure 3-4): The correlation between RA and 50% amplitude of CAP. ........ 77 Figure 20 (Figure 3-5): Scatter plot of SMI-312 count and the sum of SMI-31 and SMI32count.............................................................................................................................. 78 Figure 21 (Figure 4-1): MR image plan and fractional anisotropy maps of mouse corpus callosum. ........................................................................................................................... 95 Figure 22 (Figure 4-2): DAPI and SMI-31 stained slices and the quantified values of a fixed mouse trigeminal nerve............................................................................................ 96 Figure 23 (Figure 4-3): Spectral analysis of isotropic diffusion tensor compartments. ... 98 Figure 24 (Figure 4-4): Resolution of crossing fibers by DSI and DBSI. ...................... 100 Figure 25 (Figure 4-5): MBP and SMI-31 staining slices and quantified values, and radial and axial diffusivities of the middle CC of control and 4 weeks cuprizone treated mice. ......................................................................................................................................... 102 Figure 26 (Figure 4-6): Cell densities quantified based on DAPI-positive nucleus counts linearly correlated with the restricted diffusion (assigned to cells) intensity fraction derived by DBSI. ............................................................................................................ 106 x
Figure 27 (Figure 4-7): White matter pathologies seen by DTI and DBSI. .................. 107
xi
List of Abbreviations ADC
Apparent Diffusion Coefficient
MRI
Magnetic Resonance Imaging
DTI
Diffusion Tensor Imaging
CNS
Central Nervous System
MS
Multiple Sclerosis
PGSE
Pulsed-gradient Spin-echo
RF
Radio Frequency
TR
Repetition Time
TE
Echo Time
MD
Mean Diffusivity
RA
Relative Anisotropy
ODF
Orientation Distribution Function
DBSI
Diffusion Basis Spectrum Imaging
NFL
Nerve Fiber Layer
GCL
Ganglion Cell Layer
IPL
Inner Plexiform Layer
INL
Inner Nuclear Layer
OPL
Outer Plexiform Layer
ONL
Outer Nuclear Layer
IS
Inner Segments
OS
Outer Segments
RPE
Retinal Pigment Epithelium
OCT
Optical Coherence Tomography xii
FOV
Field of View
DWI
Diffusion Weighted MRI
T1
Longitudinal Relaxation Time Constant
T2
Transverse Relaxation Time Constant
Gd-DTPA
Gadolinum- diethylenetriaminepentaacetic acid
RP
Retinitis Pigmentosa
BRB
Blood-Retinal Barrier
WT
Wildtype
rd1
Retina-Degeneration-1
CAP
Compound Action Potential
VEP
Visual Evoked Potential
CSF
Cerebrospinal fluid
DPI
Days Post Injury
EAE
Experimental Autoimmune Encephalomyelitis
CC
Corpus Callosum
IHC
Immunohistochemistry
SMI-31
Phosphorylated neurofilament
SMI-32
Nonphosphorylated epitope
SMI-312
Pan-axonal neurofilament
MBP
Myelin Basic Protein
DAPI
4', 6'-diamidino-2-phenylindole
xiii
Chapter 1 Introduction 1.1 Physical Principles of the Phenomenon of Diffusion 1.1.1 Diffusion and Self-diffusion Diffusion refers to the transport of gas or liquid through thermal agitation in a random way. Each molecule within the sample behaves independently from the others. The collision between molecules provokes a random displacement of each one, without a preferred direction, tracing the path known as “random walk”. The measurement of random walk is the calculation of a statistical measure of diffusion distance, averaged over an equilibrium ensemble of molecules (the so-called root mean square distance) in a determined period (2). When describing the mixing of two different liquids or gases, the underlying driving mechanism of this diffusion process is usually described in terms of the concentration gradient of the diffusing substance. In biological tissues, however, gradient concentration is not the driving forces, and the process of interest is the motion of tissue water, driven by thermal agitation, and referred to as self-diffusion.
1.1.2 Fick’s Laws and The description of Self-diffusion Fick's law states that local differences in solute concentration will give rise to a net flux of solute molecules from high concentration regions to low concentration regions. Fick’s Law provides the way to quantitatively describe the diffusion process. In mathematical terms, if F, the flux density, is the transfer rate of the diffusion substance through the unit
1
area of each section of the sample studied, c(r,t) is the concentration of the diffusion substance, then Fick’s first law of diffusion can be formulated as:
F Dc(r , t )
(1.1)
In this expression, the minus sign means that the material is transported in the direction of decreasing concentration. D is the diffusion coefficient and is expressed in units of m²/sec. The diffusion coefficient is a physical constant characterizing the movement of molecules.
From equation (1.1), it is possible to derive Fick’s second law of diffusion, valid for constant D values and three-dimensional diffusion processes, which describe the mechanism only in terms of the temporal and spatial partial derivatives of the concentration c,
c(r , t ) F D 2c(r , t ) t
(1.2)
Fick’s second law satisfies the initial conditions of a delta function at the location r0 for the initial concentration profile, i.e., c(r , 0) (r r0 ) , and the boundary condition of unrestricted self-diffusion in an isotropic and homogeneous environment, i.e., c(r, t ) 0 as r0 .The solution of Fick’s second law becomes:
(r r0 ) 2 c(r , t ) (4 Dt ) exp[ ] 4 Dt
3 2
(1.3)
We can see that c(r,t) only depends on the net displacement, defined as R = r – r0 and not the intital positon r0. Hence, Eq.(1.3) could be written as,
3
c(r , t ) (4 Dt ) 2 exp[
R2 ] 4 Dt
(1.4)
2
Nuclear magnetic resonance measures the bulk magnetization of nuclear spin containing molecules, which represents an ensemble average of different spins. This requires a statistical re-interpretation of the above classical description of particle diffusion when a chemical/physical concentration gradient is not present. Thus, the flux density (F) can be interpreted as conditional probability flux, the concentration c(R,t) can also be described as a self-correlation function Ps(R,t)(3), and mass balance becomes conservation of total conditional probability. In analogy with Fick’s second law of diffusion, the self-diffusion mechanism can be described by
Ps ( R, t ) D 2 Ps ( R, t ) t
(1.5)
For the simple case of isotropic unrestricted self-diffusion in three dimensions, the solution to Eq. (1.5) takes the form of a Gaussian function, and the mean square dynamic displacement R2, can be calculated in terms of D:
R 2 R 2 Ps ( R, t )dR 6 Dt
(1.6a)
The process of self-diffusion can also be modeled (4) by assuming that the particles of any liquid take a random walk consisting of a succession of n random displacements of constant length , at constant time intervals, , over a time t = n. After each displacement there is a collision and then a new random orientation for the next displacement. Taking into account the random nature of this process, it is possible to determine the R2 as a function of :
t R 2 n 2 2
(1.6b)
3
This has the same form as Eq. (1.6a). Thus the diffusion coefficient arising from random walk is described by the Einstein equation:
D
2 6
(1.7)
1.1.3 How Does Diffusion Affect the MR Signal? In the presence of a constant magnetic field gradient (G), the precessing spins experience a change of magnetic field due to molecular diffusion (a change in position due to incoherent displacement), resulting in a change of Larmor frequency. After a given diffusion time, different spins accumulate different phase shifts, a result of their changing frequencies due to diffusion in the presence of the gradient. Because free unrestricted diffusion is a random process, the accumulating phase shifts are incoherent among the many spins. This leads to an attenuation of MR signal. This phenomenon was first observed by Erwin Hahn (5). The effect of diffusion on MR signal in the presence of magnetic field gradient can be introduced by a phase probability function,
P(, t ) (
2 4 D G 2t 3 12 ) exp 2 3 3 4 D G t / 3
(1.8)
Where, is the gyromagnetic ratio. Not surprisingly, the phase probability function also has the Gaussian functional form of Ps (R,t).
From a macroscopic view, Torrey (6) added diffusion terms to the phenomenological Bloch equation (7) so that self-diffusion is represented as a “transport of magnetization”. The Bloch-Torrey equation provides a convenient means to follow the bulk
4
magnetization evolving under the influence of both relaxation and molecular selfdiffusion.
The diffusion measurement of water in biological tissue is characterized by apparent diffusion coefficient (ADC). Water in a free environment, such as CSF, can diffuse easily in all directions. In other biological tissue there are barriers (such as proteins, membranes, nerve fibers and other biological molecules), which reduce the ability of water to diffuse. Measuring the ADC in biological tissue water gives insight into these biological barriers.
1.2 ADC changes reflect the central nervous system injury The ADC may decrease or increase depending on the underlying pathology in central nervous system (CNS) tissues examined.
The reduction in the ADC has been observed after the onset of cerebral ischemia(8), reflecting the undelying cytotoxic edema (cellular swelling). Cytotoxic edema accompanies cell membrane depolarization results in a reduction in the net displacement of diffusing water molecules. Cerebral regions of reduced ADC appear hyperintense in DWI. Since the conventional MRI parameters such as T1, T2 and proton density change relatively little at the acute phase of stroke, the ADC provides a unique method to detect the acute stroke(8). In the sub-acute phase, the ADC increases and results in the well recognized "pseudonormalization" reflecting the development of vasogenic edema. Chronic infarctions have shown much higher ADC values than unaffected areas(9). The return of ADC to above normal values after prolonged ischemia may reflect tissue 5
necrosis(10). Renormalization and subsequent increase of the ADC is thought to be due to the loss of cell member integrity that follows the recruitment of neutrophils in the ischemic region(11). Or the increased ADC is probably resulting from the degradation of restrictive barriers permitting a larger net displacement of water molecules for the same diffusion time(12).
The various stages of multiple sclerosis (MS) are characterized by de- and remyelination as well as the inflammation. ADC has been used as a sensitive maker to evaluate the pathological process of MS for a long time. The increased ADC in MS lesion was described previously(13). The greater ADC values have been observed in acute than in chronic MS lesions, which were both higher than the ADC in normal-appearing white matter (14). In addition, ADC was elevated in the NAWM of MS patients as compared with control subjects (14,15). The variability of ADC may represent the presence of vasogenic edema, in which the blood brain barrier is disrupted, superimposed with cytotoxic edema of oligodendroglia (16). Chronic MS lesions are characterized pathologically by the loss of myelin with axonal preservation, reactive astrogliosis, and absence of acute inflammation. Extracellular water is increased relative to uninvolved white matter, resulting in the intermediate increasing of ADC(16).
Epilepsy is a common chronic neurological disorder that is characterized by recurrent unprovoked seizures(17). There is a wealth of data confirming the usefulness of DWI in identifying and monitoring histopathological changes in epileptic seizures. The ADC decrease approximately 15% after the onset of status epliepticus(18). ADC was reported
6
to return to normal value after seizures. However, if permanent tissue damage has occurred, diffusion will continue to increase beyond normal values(19). The change of ADCs in epilepsy undergoes the similar process as that in cerebral ischemia, suggesting a common mechanism. However, the changes occur under very different circumstances. In ischemia, the blood supply to the tissue is seriously compromised, resulting in energy failure, membrane dysfunction, and cell death. Conversely, ongoing seizure activity leads to increased metabolic rate and cerebral blood flow so that cellular energy levels are upheld close to normal values. However, enhanced membrane ion permeability results in measurable cellular swelling (20). Therefore, it appears that loss of ionic homeostasis, whether from ischemia or from enhanced neuronal activity, is a common factor in both biophysical conditions (19).
Biophysics mechanisms underlying the alterations of ADC are still under intensive investigations. The most commonly cited mechanism that explains the reduction of ADC following injury is that tissue ATP reserves are depleted shortly after the onset of ischemia, and the subsequent failure of the Na+, K+-ATPase pump causes water molecules and Na+ to migrate from the fast-diffusing extracellular compartment into the more slowly diffusing intracellular space resulting in cell swelling(21). Duong TQ et al. hold the different opinion on the above mechanism and considered that the decreased ADC may be due to an impairment of the energy-dependent circulation associated with the energy metabolism failure in intracellular space and/or an increase in cytoplasmic viscosity(22). Other mechanisms underlying ADC changes include the effect of membrane permeability(23), macroscopic bulk motion(24), cytoplasmic motion(25,26),
7
restricted diffusion (27,28), alteration in extracellular volumes (29) and the effects of extracellular tortuosity (30). A complete biophysical explanation of the water diffusion in CNS tissue requires a solid framework to assess the contributions of the different mechanisms underlying the ADC changes. Monte Carlo simulations have been employed to investigate the impact of changes of several parameters characterizing tissue at cellular level (31). Combinations of the restricted intracellular diffusion approach, extra/intracellular water exchange across permeable boundaries, and the effect of extracellular tortuosity have all been examined to simulate the non-monoexponential water diffusion characteristics observed in various states of brain tissue(32).
1.3 Magnetic resonance measurements of ADC Diffusion magnetic resonance imaging (MRI) has been widely used to measure the ADC in biological tissue.
The contrast of Diffusion MRI originates from the random
microscopic motion of water protons (i.e., water diffusion). It is widely used as a noninvasive and sensitive imaging modality for early detection and evaluation of the CNS injury.
Diffusion MRI is the only means we have to observe diffusion non-
invasively. It provides access to both superficial and deep organs with high resolution without interfering with the diffusion process itself: diffusion is an intrinsic physical process that is totally independent of the MRI effect or the magnetic field. In spite of the poor understanding of the biophysical mechanisms underlying the changes of water diffusivities, Diffusion MRI may be a means to better understand different pathological processes. Pathological processes tend to alter the magnitude of structural organization either by destruction or regeneration of membranous elements or by a change in 8
cellularity (e.g. scarring, inflammatory or neoplastic infiltration) (24). Water shifts between tissue compartments due to the changes in permeability, osmolarity, or active transportats may also occur. All these processes will have an impact on the extent of water diffusivity measured by diffusion MRI. Therefore, probing changes of water diffusion in neural tissue opens a totally new approach to describe related morphologic damages associated with focal or diffuse lesions seen on conventional MRI(33,34).
1.3.1 Pulsed-Gradient Spin-Echo Sequence The pulsed-gradient spin-echo (PGSE) sequence introduced by Stejskal and Tanner in 1965(35), a bipolar diffusion weighted pulse-gradient spin echo sequence, is widely used to sensitize MRI signals to molecular diffusion in tissues. In this sequence, two gradients with the same polarity are placed on each side of the 180° refocusing pulse. No field gradients were present during the radio frequency (RF) pulse transmission or signal acquisition period. The effect of the first gradient pulse is to spatially encode the spin isochromats with different precession frequencies (dephase), where precession frequency is linearly related to the position along the gradient axis. The effect of the second gradient, with effectively opposite gradient polarity of the same gradient strength, refocuses the dephased spin isochromats along the gradient directions to the same phase. This results in complete phase coherence among the spin isochromats and thus the spin echo amplitude experiences no attenuation in the absence of relaxation. In the presence of diffusion, the dephased spin isochromats can no longer be exactly refocused due to the scrambling of the spin phase-vs.-position relationship due to incoherent displacement of the diffusing molecules, resulting in a residual distribution of phases after the second gradient pulse. 9
The ensemble average of spins with different phases leads to an attenuation of the spin echo amplitude.
Figure 1 (Figure 1-1): A schematic representation of the PGSE sequence. A pair of diffusion gradients G is applied after 90° pulse and on both sides of the 180° pulse. The gradients shown in the figure are rectangular diffusion gradients, where is the time between the onset of a diffusion gradient pulse and the end of it, and is the time between the onset of the first and second gradient pulses. The signal is read out at the echo time, TE.
After the application of the magnetic field gradient G in static B0 field, the accumulative spatially dependent phase shift (t ) of a spin over time t will be:
10
t
(t ) B0t G (t ' ) r (t ' )dt ' ,
(1.9)
0
where is the gyromagnetic ratio, and r is the location of the nuclear spin. The first term
represents the phase accrual due to the static B0-field, and the second term is due to the effect of a magnetic field gradient. The phase term of the second part is proportional to the strength of the field gradient, the duration of the gradient, and the spatial location of the spin. It is obvious that the magnetic field gradient can be used to locate a spin by means of the differences in the Larmor frequency.
The ADC can be measured using the PGSE sequence. After the first diffusion weighting gradient and before 180 RF pulse, the accumulative phase shift (t1 ) :
(t1 ) B0 TE / 2
1
G (t ' ) r (t ' ) dt '
(1.10)
1
After 180 RF pulse, the second diffusion weighted gradient G will induce an inverse phase shift (t2 ) of the proton spin:
(t2 ) [ B0 TE / 2
1
G (t ' ) r (t ' )dt ' ]
(1.11)
1
At the time when the spin echo is formed, the total phase shift resulting from the diffusion gradient pair is,
(TE )
1
1
1 ' ' ' G (t ) r (t )dt ) G (t ' ) r (t ' )dt ' ) 1
11
(1.12)
If there is no diffusion, all spins will be refocused resulting in no net phase shift. Thus, the spin echo amplitude is the same as without diffusion gradients for the stationary spin.
However, in the presence of diffusion, the displacement function r (t ) is random and the phase shifts accumulated by individual spins differ causing the signal attenuation.
The signal attenuation can be derived macroscopically using the Block-Torrey equations as presented in Stejskal and Tanner’s paper (35). A diffusion term can be added to the description of the transverse magnetization (Mxy) time evolution after a 90 pulse as follows:
M xy t
i0 M xy
M xy T2
i (G r ) M xy D 2 M xy
(1.13)
On the right hand side of Eq. (1.13), the first and third terms represent mutation in the
applied static field (B0) and in the extra field ( G r ) produced by the field gradient term. The second term is the T2 decay, and the fourth (extra) term describes the diffusion mechanism.
The behavior of the magnetization Mxy as a function of time can be derived by solving Eq. (1.13)(35). In particular, it is possible to separate the terms and study the effect of the diffusion mechanisms on the magnetization evolution. In the case of a magnetic field gradient along a single direction, Mxy assumes the following form:
M xy A0ebD eir k (t ) ei0 t et / T2
(1.14)
12
Where A0 is the magnetization at time t = 0 and we have defined the b factor from the
gradient first moment k : 2
t '' '' ' b G (t )dt dt 00
(1.15)
t k G (t ' )dt '
(1.16)
'
t
2
0
The b-value is determined by the strength and duration of the magnetic field gradient. In the narrow pulse limit (i.e., > 2 >> 3. The eigenvalues and associated eigenvectors that relate the orientation of the eigenvalues in the reference frame, and the derived parameters, mean diffusivity (MD), relative anisotropy (41), axial and radial diffusivities, are independent of the diffusion encoding direction.
The average of the three eigenvalues is referred to as MD or loosely defined as apparent diffusion coefficient (ADC):
MD
Trace( D) ( Dxx Dyy Dzz ) 1 2 3 3 3 3
(1.27)
The MD can be simply calculated by averaging the diagonal elements of the tensor, Trace(D), or be directly measured from the measurements by applying diffusionsensitizing gradients in three orthogonal directions. In the majority of DTI studies, the MD is calculated by averaging the eigenvalues after tensor diagonalization.
Relative anisotropy (41) is the measurement of the degree of diffusion anisotropy. Diffusion anisotropy reflects how much the diffusion ellipsoid deviates from a pure sphere. The definition of RA is the following:
19
RA
(1 ) 2 (2 ) 2 (3 ) 2
where
(1.28)
3
1 2 3 3
. The RA represents the ratio of the anisotropic and isotropic part
of D(42). The range of RA is from 0 (isotropic diffusion) to
2 (infinite anisotropy).
1.3.5 The significance of DTI application Since its inception (43), diffusion tensor imaging (DTI) has been widely used to study the underlying pathology of CNS white matter injury (44-47). White matter fibers, consisting mostly myelinated axons, have high anisotropy. DTI derived relative anisotropy (RA) and mean diffusivity (MD) have also been shown as sensitive markers of white matter injury (48,49).However, RA and MD lack of pathological specificity as many other MRI parameters. The inflammation, edema, axonal injury and demyelination may all cause the decrease in RA. In contrast, DTI derived directional diffusivities have been proposed to serve as biomarkers of both axonal and myelin injury (46). In CNS white matter, diffusion parallel to white matter fibers refers to axial diffusivity (|| = 1), and diffusion perpendicular to white matter tracts refers to radial diffusivity (= (1+ 1)/2)). Specifically, decreased axial diffusivity has been demonstrated to reflect axonal injury (50) while the increased radial diffusivity correlates with the myelin damage (51).
1.3.6 The limitations of DTI By processing DTI data in each voxel, three main directions of diffusion can be defined at each location. Assuming that the direction associated with the highest diffusivity
20
corresponds to the underlying orientation of the tissue, two-dimensional maps of this orientation over the brain can be easily built. However, a more important shortcoming of DTI is that whatever the robustness of the tracking algorithm used, the model cannot deal correctly with voxels that contain several populations of fibers that are not necessarily characterized by the same orientation, and may be crossing. As shown by Assaf et al(52), the effective diffusion tensor derived by DTI is a powder average of diffusion effects from a mixture of underlying tissue structure and pathology in the image voxel. The DT model assumes a homogeneous population inside the voxel (free, Gaussian diffusion) and fails at describing more realistic, heterogeneous populations.
To address this limitation, more advanced diffusion MRI techniques, such as diffusion spectrum imaging(53,54), Q-ball imaging(55,56), and spherical convolution(57), have been proposed to accurately estimate the diffusion and fiber orientation distribution function (ODF) of multiple fibers, thereby providing accurately estimated angular orientation. The ODF does not aim to quantify directional diffusion properties and the corresponding fraction of individual fiber components.
Moreover, with the presence of cell and water components manifesting as restricted and nonrestricted isotropic diffusion tensor components, respectively, especially under the pathologies with the inflammation-associated cell infiltration and vasogenic edema, the current DTI model leads to an increase in the apparent diffusion coefficient (ADC)(41), and an underestimation of the diffusion anisotropy of white-matter tracts(41), probably due to the decreased intracellular water diffusion and extracellular volume fraction(58).
21
To address this limitation, multiple tensor models have been proposed to account for free isotropic water components arising from the CSF or edema by modeling the nonrestricted diffusion with known diffusivity (41,59). Pasternak et al.(41) did not consider cell components or crossing fibers since they developed the model to fit the current DTI pipeline. In addition to ignoring cell component, Caan et al.(59) also assumed the crossing fibers of equal axial diffusivity thus the model would not be ideal to assess axonal integrity in MS. Glial cells were modeled as a restricted isotropic component in a comprehensive three-component analytical model(60). However, authors only analyzed diffusion weighted images parallel and perpendicular to the optical nerve with fixed orientation. This model did not deal with multiple-fiber crossing effects. Most recently, Alexander et al.(61) proposed a four-tensor model to include the restricted isotropic cell and the extracellular isotropic water component. However, the authors quantified the orientationally invariant indices of axonal diameter and density without actually solving the cell component since they argued that the restricted isotropic component was an artifact. There is not yet a method capable of modeling of crossing fibers, cell population, and edema in a single setting.
Recently, our group proposed a novel method, diffusion basis spectrum imaging (DBSI), with a flexible framework considering the multiple anisotropic and isotropic diffusion tensor components (fibers, cells, and edematous water) without predetermining a tissue model. A model selection procedure driven by the data is set up allowing each image voxel to form its own model. This approach naturally models tissue and pathology heterogeneity.
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Chapter 2 Diffusion weighted MRI detects retina degeneration in rd1 mice 2.1 Diffusion weighted MRI in the C57BL/6 wildtype mouse retina 2.1.1 Introduction Vision begins with the retina. Visual information is converted to nerve impulses in the photoreceptor cell layers including the outer segments (OS), inner segments (IS), and outer nuclear layer (ONL). The nerve impulses are transferred and processed in the outer plexiform layer (OPL), inner nuclear layer (INL), inner plexiform layer (IPL), ganglion cell layer (GCL), and finally reached the optic nerve axons in the nerve fiber layer (NFL).(62) Abnormalities of individual or multiple retinal cell layers are major causes of vision loss and blindness.
The structure and function of retina in mouse is similar to that in human.(63) Experimental mouse models are being used to study retinal abnormalities, such as glaucoma(64) and retina neovascularization.(65,66)
The ready manipulation of the
mouse genome has permitted development of transgenic mice for retinitis pigmentosa, cataract, glaucoma, and other ocular diseases.(67) Thus, the mouse has become a widely used animal model for investigating biochemical and anatomical alterations in the retina. Noninvasive methods such as ophthalmoscope (68), electroretinography (69,70), and optical coherence tomography (OCT) (71) play an important role in clinical diagnosis of
23
retinal diseases. However, visual inspection using the ophthalmoscope is limited to retinal surface changes due to the penetration limit of the light. OCT allows inspection of the retinal cell layers at high resolution but with limited field of view (FOV) and lack functional information. Electroretinography provides an integrated measurement of all neuronal cell activity without anatomical information. We reasoned that these methods may be augmented by a noninvasive diagnostic technique that is capable of assessing panretinal (e.g., from ora serrata to ora serrata) and intraretinal structural and functional abnormalities in vivo.
Magnetic resonance imaging (MRI) is a noninvasive imaging method for evaluating retina structure and function (72-77), although its application in the mouse has been limited. Using high-resolution T1-weighted, T2-weighted, and diffusion-weighted MRI (DWI), multiple “MR-detected retina layers” and the choroid layer have been observed in rat and cat models (72,76,77). Manganese-enhanced MRI of retina from rats has demonstrated differential functional adaptation to light and dark on the extent of manganese uptake in the MR-detected inner and outer retina layers (72). Blood oxygen level-dependent MRI has been performed to demonstrate the responses of retinal and choroidal vasculature on hypercapnia and hyperoxia challenges (76). However, in MRI studies of the mouse retina, to date only relatively low-resolution images (compared to the retinal thickness) were collected, so the retina appeared as a single MR-detected layer (78,79). To achieve more precise delineation of pathology, higher-resolution images are needed to differentiate multiple retina layers.
24
The objective of the present study is to utilize high resolution MRI to measure T1, T2, and apparent diffusion coefficient (ADC) of the adult C57BL/6 wildtype mouse retina and to establish normative metrics for defining pathological changes of retinal cell layers in mice. Specifically, T1 and T2 weighted images were acquired at 47 47 m2 in-plane resolution with 400m thick on mouse retina at 11.74T. A series of T1 and T2-weighted images of mouse retina with different echo and repetition times were obtained to calculate T1 and T2 relaxation time constants, respectively. In addition, images with diffusion weighting gradients applied in three orthogonal directions were acquired at the same resolution as T1 and T2-weighted images. These imaging strategies allowed direct visualization of three retinal layers on T1 and T2-weighted images and in the calculated T1, T2, ADC maps in mice retina.
2.1.2 Methods All procedures in this study conformed to the guidelines set forth by Animal Studies Committee of Washington University in St. Louis and the ARVO Statement for the Use of Animals in Ophthalmic and Vision Research. Experimental Protocol
Two to four months old male C57/BL6 mice, weighing approximately 25 g, were used. Mice were anesthetized with an initial intraperitoneal injection of a cocktail containing ketamine (87mg/kg) and xylazine (13mg/kg). A drop of Tropicamide was applied to the left eye of the mouse followed by the application of a thin layer of a water-based lubricating gel to cover the surface of the eye to minimize dehydration. A pneumatic pillow was placed under the abdomen of the mouse for monitoring the respiratory motion, 25
and the body temperature was monitored using a rectal temperature probe (SA Instruments, NY). A subcutaneous catheter was implanted to allow continuous infusion of the aforementioned ketamine/xylazine cocktail at a constant infusion rate of 2.6 ml/kg/hr to achieve a sustained anesthesia during MRI. For Gd-DTPA enhanced T1weighted imaging, a tail-vein catheter was inserted for intravenous delivery of Gd-DTPA (0.4 mmol/kg). Finally, a single-turn solenoid radio-frequency (RF) coil was positioned on top of the left eye for MRI. During the period of image acquisition, the body temperature of the mouse was maintained at 37ºC and the respiration rate was monitored using a MR compatible small animal heating and monitoring system (SA Instruments,
Figure 3 (Figure 2-1): The sketches of mouse holder for retina imaging.
NY). Infusion rate of ketamine/xylazine cocktail for individual mouse was adjusted to maintain the respiratory rate between 150 and 210 min-1 throughout the experiment.
26
MR Imaging MR experiments were performed on an 11.74 T Varian UNITY-INOVA spectrometer (Varian Associates, Palo Alto, CA) equipped with an 8-cm inner diameter gradient insert (maximum gradient strength = 120 G/cm). During the in vivo MRI measurement, it is very important to immobilize the subject. In order to obtain high quality MR images, and to maintain the subject's comfort for long scans, a custom-made animal holder has been designed and built (Fig. 2-1, 2-2).
Figure 4 (Figure 2-2): Animal holder for in vivo MRI measurements. A tooth bar is used to hold the teeth of mice for immobilizing the head. A cross-bar is placed gently over the mouse’s neck-shoulder to isolate the respiratory motion originating from the abdominal wall. A pneumonic sensor is taped to the abdomen of the mouse to monitor the respiratory motion. A rectal temperature probe is placed in the rectum of the mouse to monitor the body temperature.
Synchronization of MRI data acquisition to the respiratory cycle is often crucial for collecting high-resolution images of small animals. This is especially true for DWI or DTI of the mouse retina since respiratory motion displacement is orders of magnitude 27
larger than water diffusion displacement. Data collected during at-rest periods (breathhold or between breaths) do not suffer from the blurring effects of respiratory motion
seen in unsynchronized images. The respiration was monitored using a small animal heating and monitoring system (SA instruments, NY). In the system, the Control/Gating Module receives data from the pneumatic sensor connected to the optical fiber cables. The delay from the respiration wave peak to the scanner gate is user selectable as is the expiration gate delay and width. The gating voltage is converted by the module’s microprocessor as a TTL signal feeding to the scanner. This system allows the synchronization of respiratory motion and MRI data acquisition through the modified imaging sequence.
The symmetrical capacitive coupling coil will be positioned on top of the mouse eye for MRI
CM
experiments. The coil consists of one tuning capacitor and two matching capacitors connecting both ends of the coil, one to the ground and the other to the cable central connection (Fig. 2.3).
Scout images were
acquired using a standard multi-slice spin echo
CM
Figure 5 (Figure 2-3): Diagram of the capacitive coupling coil.
sequence. A transverse slice that bisects the eye through the optic nerve was located from scout images using a 3D planning program written in Matlab (The Mathworks Inc., Natick, MA). A standard spin-echo sequence was used to acquire multiple T1- and T2-weighted images of the mouse eye. T1-weighted images of the mouse eye (n = 5) were acquired using the following parameters: repetition
28
time (TR), 500, 1000, 2000, 4000, and 8000ms; echo time (TE), 21 ms. T2-weighted images of the mouse eye (n = 10) were acquired from the same five mice undergone T1 measurements and an additional five mice without T1 measurements with the following parameters: TR, 1500 ms; TE, 21, 28, 38, and 50 ms.
A spin-echo sequence
incorporating a pair of diffusion sensitizing gradients was used for DWI of the mouse eye with the following parameters: TR, 1500 ms; TE, 35 ms; , 15 ms; , 5 ms; b-value, 0 and 955 s/mm2. Diffusion-weighted images of the mouse eye (n=5) were acquired with diffusion weighting gradients applied in three orthogonal directions, i.e., in-plane parallel to the optic nerve (॥), in-plane perpendicular to the optic nerve (), and out-of-plane perpendicular to the optic nerve ().
To minimize the background magnetic field
gradient effect on the diffusion measurement, a pair of diffusion-weighted images were acquired in each direction with positive and negative diffusion gradients, respectively.(80) To facilitate the identification of the choroid vasculature, T1-weighted spin-echo images before and after intravenous Gd-DTPA injection was acquired with the following parameters: TR, 500 ms; TE, 21 ms. For all images, the following acquisition parameters were used to achieve a reasonable SNR for quantification of T1, T2, and ADC: slice thickness, 400 m; FOV, 66 mm2; in-plane resolution, 4747 m2; data matrix, 128128 zero filled to 256256; number of averages 4. All T2- and diffusion-weighted images were acquired with respiratory gating. Data Analysis
The T1 map was estimated pixel-by-pixel using least-square fitting of the T1-weighted images modeling the T1 saturation recovery function with a single exponential function
29
and two parameters (relative signal intensity and T1 relaxation time constant). The T2 map was similarly derived employing the single-exponential T2 decay function with two parameters (relative signal intensity and T2 relaxation time constant).
Apparent diffusion coefficients (ADC) maps were derived according to the single exponential decay function using non-diffusion-weighted b0 (b-value = 0 s/mm2) and the diffusion-weighted images (b-value = 955 s/mm2). (36) In each direction, directional dependent ADC (ADC॥, ADC, or ADC) map was calculated as the mean of the two estimated ADC maps using positive or negative diffusion weighting gradient.(80) Mean ADC ( ADC ) was estimated as the average value of ADC॥, ADC, and ADC.
For quantification of T1, T2, and ADC, the MR-detected retina and choroid layers were manually segmented based on signal intensities on non-diffusion-weighted images. First, both vitreous and sclera regions were manually selected for the calculation of the mean and standard deviation of signal intensity for each structure, respectively. The combined retina/choroid layers were then segmented as regions of intensity that is one standard deviation above the sclera and one standard deviation below the vitreous. The MRdetected choroid was further segmented as the region of voxel intensity 10% higher than the mean of the layers containing both retina and choroid. This threshold was set to exclude the voxels containing choroidal partial volume effect from the retina for accurate quantification of T1, T2, and ADC in the retina. Thus, the MR-detected choroid may also contain retina information.
Finer definition of the MR-detected retina layers was
determined based on the MRI signal intensity differentials. 30
MR-detected layer thickness was measured from the five mice examined with DWI. Since diffusion-weighted images provided a clear definition of the combined retina/choroid (Fig. 2-3C), the retina/choroid was segmented as the region of intensity that was one standard deviation above the mean of vitreous. The identification of MRdetected choroid, retina, and three retina layers was performed on b0 image using the method described above.
Quantitative data analysis was performed in the selected region of interest at the central retina. Specifically, a circle concentric with the combined layers of retina/choroid was created for the consistent region of interest determination. A pair of arcs spanning 20º on the circle at each side of the optic nerve head, 10 from the center, was selected to represent the region of interest for all measurements (Fig. 2-3D). By this definition, the two ends of each arc were about 250 m and 800 m away from the optic nerve head, respectively. The thickness of the MR-detected choroid, retina, and individual retina layer, was measured as the mean thickness within the two arcs. Histology
Histology analyses were performed on eyes from eight mice. Paraffin-embedded tissues were prepared for optimal examination of cell morphology. Frozen-cut tissues were prepared to avoid fixation and dehydration induced tissue distortion(81).
Eyes from four mice were perfusion-fixed with 4% paraformaldehyde, enucleated, placed in 4% paraformaldehyde overnight, embedded with paraffin, and sectioned at five-m 31
thick. Eyes from another four mice were enucleated, flash frozen, sectioned at eight-m thick, and fixed with 10% formalin for 20 min. A total of eight paraffin-embedded and four frozen eyes sectioned through the optic nerve head and parallel to the optic nerve were analyzed. Sections were stained with hematoxylin and eosin (H&E) to delineate the multiple cell layers of the retina, and with picrosirius red to visualize the collagen fiber and other connective tissues. The thickness of retina and choroid layers were measured on H&E stained slices corresponding to the MRI region of interest. Statistical Analysis
All statistical analyses were performed using SAS software (SAS Institute, Cary, NC). Quantitative data are expressed as mean ± SD.
For comparisons between two
experimental groups, the significance of the difference between the means was calculated. Unpaired student t-test was performed to compare the layer thickness measured between MR image and frozen-cut tissue sections, or between frozen-cut and paraffin-embedded tissue sections. One-way analysis of variance (82) was used to test for differences of T1 or T2 among vitreous, retina, and choroid, as well as among the three MR-detected retina layers. Two-way ANOVA was used for analysis of ADC॥, ADC, and ADC in vitreous/retina/choroid, and among the three MR-detected retina layers. When overall significance of p < 0.05 was attained by ANOVA, comparisons between means were performed using the Freeman-Tukey test. In all cases, a p < 0.05 was taken to indicate statistically significant difference.
32
Figure 6 (Figure 2-4): MRI images of the mouse eye and the H&E stained slice of the retina. A T1- (A) and a T2-weighted (B) images of the mouse eye show four MR-detected layers between the hyper-intense vitreous and hypo-intense sclera.
These MR-
detected layers are hyper-intense on the diffusion-weighted image of the mouse eye (C). Two 20º () arcs from the retina and choroid on each side of the optic nerve head are selected for data analysis (D). A composite T1-weighted image shows GdDTPA enhancement in the choroid (E). Regions with >10% signal enhancement after Gd-DTPA treatment is overlaid on the pre-enhanced T1-weighted image.
The
expanded view of T2-weighted image adjacent to the optic nerve head shows the MRdetected three retina layers, choroid, and sclera (F). An H&E stained slice of retina from a paraffin-embedded mouse eye shows retinal cell layers adjacent to the optic nerve head (G). The scale bar represents 50 µm. Black arrow, four MR-detected layers; white arrow, hyper-intense retina/choroid layers; open arrow, optic nerve head. 33
2.1.3 Results Delineation of three MR-detected retina layers
Both T1- and T2-weighted images exhibited four distinct MR-detected layers with different signal intensities between the hyper-intense vitreous and the hypo-intense sclera. Starting from the vitreous, an alternating dark-bright-dark-bright pattern was observed (Figs. 2-4A, B, and F). These MR-detected layers were also seen on the b0 image (image not shown) and all layers were hyper-intense on the diffusion-weighted image comparing with that of vitreous and sclera (Fig. 2-4C), reflecting the more restricted water diffusion in the retina and choroid. Pre- and post-contrast enhanced T1-weighted images acquired before and ~5 min after intravenous injection of Gd-DTPA highlighted the vascular choroid adjacent to the sclera (Fig. 2-4E), confirming the hyper-intense outmost layer contains the choroid. Thus, three MR-detected retina layers, i.e., inner (dark), middle (bright), and outer (dark) layers, and a MR-detected choroid layer were observed in the mouse eye (Fig. 2-4F). Measurement of the layer thickness
As expected due to shrinkage during processing, the thickness of retina measured from paraffin-embedded tissues was significantly lower than that measured from the frozen-cut tissues (p < 0.05) and from murine retinal thickness values in vivo in the literature.(83-85) Thus, frozen-cut retinal tissue was used to compare retinal and choroidal thickness and paraffin-embedded retina was used for qualitative cellular-identification. The thickness of choroid from paraffin-embedded tissues has not been measured due to tissue distortion.
34
The measured thickness of the MR-detected retina as a whole, individual retina layers, and choroid was reported and compared with the measured thickness of overall retina, NFL/GCL/IPL, INL/OPL, ONL/IS/OS, retinal pigment epithelium (RPE)/choroid from frozen-cut tissues (Table 2-1). The thickness of MR-detected choroid was significantly higher than that of the RPE/choroid measured from frozen-cut tissues (p < 0.05) while the overall thickness of MR-detected retina was significantly lower than that measured from frozen-cut tissues (p < 0.05), indicating the MR-detected choroid contains retina
Table 1 (Table 2-1): MRI and histology determined thickness of the retinal cell layers, overall retina, and choroid.
MR-detected Layer Thickness from MRI
Middle
Outer
Overall
retina
retina
retina
31 3*
71 5*
182 7*
52 7*
ONL/IS/
Overall
RPE/
OS
retina
Choroid
Inner retina
80 6*
Choroid
(µm) H&E stained neuron
NFL/GCL/ INL/OPL
cell layer
IPL
Thickness from frozen-cut tissues
75 6
50 8
95 13
220 17
28 5
56 7*
37 1*
65 3*
159 10*
N/A
(µm) Thickness from paraffin- embedded tissues (µm) *, p < 0.05 compared to the thickness measured from frozen-cut tissues. 35
information. Within the retina, the difference between MRI and corresponding frozencut tissue determined layer thicknesses was less than 47 µm for all layers (p
