Inversion Recovery Sequences for the Detection of Cortical Lesions in Multiple Sclerosis. Using a 7 Tesla MR Imaging System DISSERTATION

Transcription

1 Inversion Recovery Sequences for the Detection of Cortical Lesions in Multiple Sclerosis Using a 7 Tesla MR Imaging System DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Katharine Teal Bluestein Graduate Program in Biomedical Engineering The Ohio State University 2012 Dissertation Committee: Professor Petra Schmalbrock, Advisor Professor Michael Knopp Professor Bradley Clymer

2 Copyright by Katharine Teal Bluestein 2012

3 Abstract Although white matter lesions have been the target of a majority of research in multiple sclerosis imaging, they have been shown to have little correlation to the symptoms that multiple sclerosis patients experience as the disease progresses. Due to their small size and low tissue contrast in images, cortical lesions have only recently been implicated in the disease process and have been shown to be numerous in the later stages of disease. This work uses ultrahigh field 7 Tesla MRI capable of high image resolution and greater tissue contrast to develop an imaging protocol for cortical lesion detection in vivo. Using an inversion recovery turbo field echo sequence, white matter attenuation is shown to provide good cortical lesion and excellent white matter lesion detection. Other inversion recovery contrast options are explored as well. Related topics are discussed, such as the contributions to the logistic regression model for lesion detection, the process for training new readers in cortical lesion detection, and finally, a novel use of the gray matter attenuated inversion recovery contrast option is shown to potentially provide insight on the severity of tissue destruction in white matter lesions. With these project components, inversion recovery is optimized for cortical lesion detecion at 7 Tesla to improve disease diagnostics and treatment monitoring. ii

4 Dedication This document is dedicated to my family, who have sacrificed so much over the last five years. iii

5 Acknowledgments I would like to thank everyone at the Wright Center of Innovation in Biomedical Imaging in the Department of Radiology for giving me this opportunity to contribute to the study of a disease that has affected several members of my family and friends. Particular gratitude goes out to my advisor, Dr. Petra Schmalbrock, and my committee members, Drs. Michael Knopp and Bradley Clymer. To Peter Wassenaar, Dr. Seongjin Choi, Dr. Steffen Sammet, C. Renil Zachariah, Dr. David Pitt, Jonda Leser, Frankie Aguila, Grant Yang, and Sharon Schreiber I give my thanks for participating in thoughful discussions, assisting with data crunching and lesion counting, and generally keeping me sane through the years. I would also like to thank Melanie Senitko and Melanie Hughes for keeping all the paperwork straight when needed, and finally to the National Institute of Health for funding a portion of my studies via a Ruth Kirschstein Individual Fellowship. iv

6 Vita June Bishop Ready High School June B.S. Electrical Engineering University of Cincinnati December M.S. Biomedical Engineering The Ohio State University September 2007 to present... Graduate Fellow Department of Radiology The Ohio State University Publications Bluestein KT, D Pitt, S Sammet, CR Zachariah, U Nagaraj, MV Knopp, P Schmalbrock. Detecting Cortical Lesions in Multiple Sclerosis at 7T Using White Matter Signal Attenuation. Magnetic Resonance Imaging. In press Bluestein KT, D Pitt, MV Knopp, P Schmalbrock. T1 and Proton Density at 7T in Patients with Multiple Sclerosis: An Initial Study. Magnetic Resonance Imaging. 30(1): Richdale K, P Wassenaar, KT Bluestein, A Abduljalil, JA Christoforidis, T Lanz, MV Knopp, P Schmalbrock. 7 Tesla MR Imaging of the Human Eye In Vivo. Journal of Magnetic Resonance Imaging 30(5): Major Field: Biomedical Engineering Field of Study v

7 Table of Contents Abstract... ii Dedication... iii Acknowledgments... iv Vita... v Publications... v Field of Study... v Table of Contents... vi List of Tables... ix List of Figures... x Chapter 1: Introduction and Background... 1 Multiple Sclerosis... 2 Magnetic Resonance Imaging... 5 Chapter 2: T1 and Proton Density Measurements Introduction Methods Results Discussion Chapter 3: White Matter Attenuation Introduction Methods Sequence Theory Initial Tests and Parameter Selection Initial Evaluation of WHAT for Cortical Lesion Detection vi

8 WHAT Parameter Optimization Results Pilot Study Demonstrating the Utility of WHAT for Cortical Lesion Detection WHAT Sequence Parameter Optimization Discussion Chapter 4: Other Attenuated Sequences Introduction Gray Matter Attenuation Cortical Lesion Attenuation Cerebrospinal Fluid Attenuation Double Inversion Recovery Turbo Field Echo Contrast Comparisons Chapter 5: Contrast versus Resolution in Cortical Lesion Imaging Summary of Logistic Regression Probability of Lesion Detection Contrast and Resolution Conclusion Chapter 6: Reader Training for Cortical Lesion Detection Introduction Motivation Methods Results of New Training Method Chapter 7: White Matter Lesion Differentiation Introduction Methods Results Discussion Conclusion vii

9 Chapter 8: Concluding Remarks References Appendix A: IDL Code WHAT-TFE DIR-TFE GRAT/CLAT/CSFAT Appendix B: R code Logistic Resgression Bimodal T1 in White Matter Lesions Appendix C: Excel Data White Matter Lesion T1 Values viii

10 List of Tables Table 2.1: Median T1 and proton density values of white matter (WM), gray matter (GM), white matter lesions (WML) and cortical lesions (CL) measured in MS patients. T1 and PD values of white matter and gray matter of healthy controls are included for comparison Table 2.2: P-values are shown comparing the calculated WM and GM T1 and PD for MS patients and healthy controls Table 2.3: Summary of normal appearing white matter and gray matter T1 values in this study and of those in recent literature Table 3.1: Tissue T1, T2*, and proton density (PD) values used in simulated IR-TFE (MPRAGE) tissue signal calculations Table 3.2: SNR/CNR optimized WHAT sequence parameters at different resolutions for a 10 minute scan Table 5.1: Probability of lesion detection is based on several factors: The resolution and contrast information for the 4 tested sequences are listed. Derived from [Zachariah 2011, Table 8] Table 5.2: Significant regression coefficient p-values for lesion detection model (α = 0.05). Highlighted values are significant Table 5.3: P-values of model including interaction terms. Highlighted values are significant (α = 0.05) Table 5.4: Regression coefficients for model including interactions terms ix

11 List of Figures Figure 1.1: Visual depiction of the most common clinical courses of multiple sclerosis... 4 Figure 1.2: Image examples for (A) 7T T2-weighted spin echo, (B) 3T FLAIR, and (C) 3T DIR-TSE for different MS patients at the same anatomical level. Lesions are most visible in the DIR-TSE image... 8 Figure 1.3: Image examples for (A) 7T IR-TFE, (B) 7T T2*-weighted magnitude, and (C) 7T phase at the same anatomical level. (B) and (C) are the same patient Figure 2.1: (A) Whole brain high SNR white matter attenuated IR-TFE image with the inset box indicating the anatomical area of the white matter attenuated IR-TFE (top) and T2*-weighted FFE (bottom) images. A cortical lesion (arrows) can be identified via hyperintense contrast to adjacent gray matter in the white matter attenuated image and by hypointense borders in the T2*-weighted FFE sequence. (B) White matter lesions are easily visible in the white matter attenuated images (arrowheads) and are also seen on the T2*-weighted FFE images. Areas of the brain with signal loss due to B1-inhomogeneity greater than 50% were excluded from the study (dotted line) Figure 2.2: High SNR white matter attenuated IR-TFE images showing an example white matter lesion (A, arrowhead) and cortical lesion (B, arrow). The same lesions were identified on the T1 measurement IR-TFE images (C, D; TI = 700 ms and E, F; TI = 2000 ms) and used for the subsequent T1 and PD calculations. (A) and (B) have 2.8 times the image SNR than (C-F) Figure 2.3: (A) Box plot showing the relative distributions of T1 measurements for MS patients and healthy controls for white matter, gray matter, cortical lesions and white matter lesions. (B) Box plot showing the relative distributions of tissue proton densities for MS patients and healthy controls Figure 2.4: Graph showing a representative signal response curve for an IR-TFE sequence given the calculated T1s and PDs presented in this study. White matter, gray matter, white matter lesions, and cortical lesions are included. The simulated tissue contrast is similar to that seen in in vivo images. Minimal B1 inhomogeneity can be seen in the MR images. TS = 6000 ms, TR = 4.0 ms, TE = 1.9 ms, flip angle = x

12 Figure 3.1: A 180 inversion pulse is followed by a train of turbo field echo readouts. α = flip angle (FA), TE = echo time, TR = repetition time, TI = inversion time, TFE = turbo field echo factor (number of α-pulses), TD = delay time, and TS = shot interval. The cross-hatched boxes represent acquisition. Centric k-space encoding is used Figure 3.2: (A) Example simulation of the IR-TFE sequence signal as a function of TI for TS = 3700 ms for WM [solid] and GM [dashed], and (B) computation of the TIs nulling WM [solid] and GM [dashed] signal as a function of TS. Computations used TFE = 165, TR = 4.1 ms, FA = 8, and tissue parameters from Table 1. To simulate the effects of RF inhomogeneity, both the inversion pulse and the read-out flip angle, α, were scaled by a scaling factor. For c rf = 1: α = 8, IR pulse = 180 [black line]; for c rf = 0.80: α = 6.4, IR pulse = 144 *gray line+. Note that the TI nulling WM decreases from 595 ms to 510 ms for c rf = 1.0 and 0.80, respectively. For the TS/TI pairs in (B), absolute GM signal is larger than WM signal Figure 3.3: (A) WM nulling TI values as a function of TS for different TFE-factors: TFE = 165 *dashed+, TFE = 244 *solid+, TR = 4.1 ms, α = 8 ; (B) different readout flip angles: α = 8 *solid+, α = 4 [dashed], TFE = 165, TR = 4.1 ms; and (C) different TRs: TR = 4.1 ms [dashed], TR = 8.2 ms *solid+, TFE = 165, α = 8. In (D), (E), and (F), the computed GM [black] and cortical lesion signals [gray] for the condition in (A) through (C) are shown Figure 3.4: Examples of type I, II, III and IV cortical lesions showing their appearance in WHAT images Figure 3.5: (A) WHAT with default IR pulse compared to (B) WHAT image with a hyperbolic secant adiabatic pulse Figure 3.6: Measured WHAT SNR and simulated signal (lines) for fixed TFE = 244 in a healthy volunteer for white matter [diamonds], cortical gray matter [triangles], and cerebrospinal fluid [squares]. Measured data are for TS/TI pairs = 6000/750 ms, 5000/730 ms, 4000/700 ms, 3000/620 ms, and 2000/470 ms, with respective scan times of 10:07, 8:26, 6:45, 5:04, and 6:49 min. Other parameters were TR = 4.1 ms; TE = 1.6 ms; FA = 8 ; FOV = 220x170 cm; matrix = 316x244; 40 slices (51 shots); voxel size = 0.7x0.7x1.4 mm 3 ; BW = 600 Hz; and NSA = Figure 3.7: Experimental SNR per 10 minute scan time for different resolutions. Shown are the measured WM [diamonds] and cortical GM [triangles] SNR values for 3 volunteers, and the calculated GM and CL SNR using the theoretical signal computation with a readout flip angle of 8, matching the measured data and parameters from Tables 1 and 2. The computed signals were corrected for voxel size, matrix and bandwidth according to Eq. (5), and scaled by a factor of 16 to match the measured data. The experimental GM signal measured for a 1x1x2 mm 3 voxel xi

13 size may be too large due to partial volume averaging with adjacent CSF. The dotted line shows the average for measured WM SNR, and represents the measured background noise Figure 4.1: Gray matter attenuated IR-TFE. (A) TS/TI pairs producing minimal GM signal, and (B) tissue signal calculations using TS/TI pairs in (A). (C) Example image in healthy subject Figure 4.2: Cortical lesion attenuated IR-TFE. (A) TS/TI pairs producing minimal CL signal, and (B) tissue signal calculations using TS/TI pairs in (A). (C) Example image in healthy subject Figure 4.3: CSF attenuated IR-TFE. (A) TS/TI pairs producing minimal CSF signal, and (C) tissue signal calculations using TS/TI pairs in (A). (C) Example image in healthy subject Figure 4.4: White matter and CSF attenuated DIR-TFE. (A) TS/TI1/TI2 pairs producing minimal WM and CSF signal, and (B) tissue signal calculations using TS/TI1/TI2 pairs in (A) Figure 4.5: Effects of changing DIR-TFE acquisition parameters on the performance of the sequence in nulling both white matter and cerebrospinal fluid. TS = 4550 ms Figure 4.6: Effects of changing DIR-TFE acquisition parameters on the performance of the sequence in nulling both white matter and cerebrospinal fluid. TS = 10,000 ms Figure 4.7: (A) Calculated signal for each tissue type for each attenuated IR-TFE sequence, including DIR-TFE. TS = 8000 ms. (B) Calculated tissue contrast between white matter-cortical lesions and gray matter-cortical lesions. TS = 8000 ms. (C) Same as (A) but calculated for TS = 4550 ms. (D) Same as (B) but calculated for TS = 4550 ms. (E) Same as (A) but calculated for TS = 3700 ms. (F) Same as (B) but calculated for TS = 3700 ms Figure 4.8: Typical appearance of a Type 1 cortical lesion in various IR-TFE contrast options Figure 5.1: Logistic regression and the S-curve Figure 5.2: Type Classifications of lesions seen independently on MR and histological images Figure 5.3: The image readers missed more smaller lesions than larger ones, and the bias Figure 5.4: Histogram of lesion size as measured in histology sections (from [Zachariah 2011, Figure 5]) Figure 5.5: Logistic regression curves for model of lesion detection shows that contrast has the greatest effect the detection of smaller lesions and resolution has the greatest effect on larger lesions xii

14 Figure 5.6: Tissue signal as measured in sequences with two different in-plane resolutions and several slice thicknesses. In-plane resolutions used: 0.4x0.4 mm 2 (solid line, slice thickness 0.8 mm and 1.6 mm) and 0.5x0.5 mm 2 (dashed line, slice thickness 0.5 mm, 1.0 mm, and 2.0 mm). The ROIs used for the measurements are shown on the right Figure 5.7: Comparing different slice thicknesses. Boundary areas are more difficult to interpret in images with larger slice thicknesses (circle). Smaller slice thicknesses lead to noisier images. In-plane resolution 0.5x0.5 mm 2, slice thickness (A) 0.5 mm, (B) 1.0 mm, and (C) 2.0 mm Figure 6.1: More "lesions" are identified over time for a new reader. (A) For all acquisition sequences. The neurologically normal controls were counted later than the MS patients, showing a very high number of false positives. (B) The new reader also unknowingly recounted several image files: the gradual increase in the number of lesions found over time indicates that a more systematic training method is needed Figure 6.2: Example Unit 1 slides introducing new readers to cortical lesions detection using information from specimen and histology comparisons and their relationship to in vivo detection Figure 6.3: Example slides from Unit 2 showing (A) prospective MRI reading, (B) retrospective MRI reading, and (C) MRI reading feedback Figure 6.4: Example slides from Unit 3 showing (A) prospective in vivo MRI reading and (B) a retrospective reading with lesions identified that were previously marked by expert readers Figure 6.5: Example slides from Unit 4 showing (A) in vivo SWI image and (B) in vivo WHAT image for a comparison read Figure 7.1: (A) Measured T1s in white matter lesions show a broad distribution with two main peaks occuring around 2200 ms and 4300 ms. (B) Plotting the tissue signal response for both white matter lesion T1s along with normal appearing white matter shows that there is a short range of TI at which the low T1 WML signal is dark and the high T1 WML signal is bright compared to normal WM Figure 7.2: Distribution of proton density values as measured in 39 white matter lesions xiii

15 Chapter 1: Introduction and Background Multiple Sclerosis (MS) is one of the most common neurological disorders worldwide, often beginning in young adulthood [Noseworthy 2000]. White matter (WM) lesions have traditionally been the focus of MS research, however white matter lesion load has not been shown to correlate well with the neurological symptoms experienced by the patients, resulting in the so-called "clinico-radiological paradox" [Barkhof 2002]. Cortical lesions have been noted in post mortem studies since the nineteenth century [Sander 1898, Taylor 1892], however only recently have they become the subject of intense research [Geurts 2008]. Although the pathological origins of cortical gray matter lesions are unknown, they are thought to contribute significantly to the clinical deficits that accumulate during secondary progressive MS [Kutzelnigg 2005, Rinaldi 2010]. Standard clinical magnetic resonance (MR) scanners ( 1.5 Tesla) are not able to achieve sufficient image contrast and resolution to depict lesions located in the cortex [Geurts 2005]. The development of high (3 Tesla) and ultrahigh (7 Tesla) MR systems can overcome these issues, as the higher field strengths result in images with greater tissue contrast differences and higher resolution [Nakada 2008]. In this work, an inversion recovery turbo field echo sequence is optimized for maximal cortical 1

16 lesion contrast to adjacent gray and white matter. Chapter 2 describes the determination of the T1 and PD values for cortical lesions and white matter lesions that will be used throughout the remainder of this manuscript. Since echo times are short, T2* effects are neglected in the tissue signal simulations unless otherwise mentioned. Chapter 3 looks into the development and optimization of a white matter signal attenuating IR-TFE sequence in which minimal white matter signal is shown to increase the conspicuity of cortical lesions. Chapter 4 expands upon the optimization scheme presented in Chapter 3 for four other IR-TFE sequence derivatives: gray matter attenuation, cortical lesion attenuation, cerebrospinal fluid attenuation, and a double inversion recovery sequence that nulls both white matter and cerebrospinal fluid. Chapters 5, 6, and 7 all present interesting applications of the IR-TFE sequences described in the previous chapters. The development of a probabilistic model for cortical lesion detection is described in Chapter 5. The gray matter attenuated IR-TFE sequence is used to define two subgroups of white matter lesions in Chapter 6. And the method for training the volunteer image readers is discussed in Chapter 7. Multiple Sclerosis Multiple Sclerosis is a common neurological disorder, primarily beginning in young adulthood. There are an estimated 350,000 cases in the United States alone. MS affects twice the number of women than men. Although an exact cause of the disease has yet to be determined, both genetic and environmental factors are thought to contribute. MS is generally not considered a terminal illness, though it may be a complicating factor in the final cause of death. [Noseworthy 2

17 2000] Multiple Sclerosis is an auto-inflammatory disease of the central nervous system, primarily characterized by neuronal demyelination and axonal loss. MS affects nearly all neurological pathways including: gross motor, fine motor, sensory systems (especially visual), and memory. There are three main clinical courses of the disease and several rare variants. The most common sequence, representing about 70% of diagnoses, is a period of relapsing-remitting MS (RRMS) characterized by neurological episodes of varying onset and symptom severity. This period lasts typically between ten to fifteen years. The relapsing-remitting phase is then followed by a secondary progressive MS (SPMS) phase lasting the duration of the patients' lifetime. This phase is characterized by a gradual increase in the severity of the symptoms, with no intermittent, symptom-free states. Primary progressive MS (PPMS) is less common and is identified by a gradual increase in the severity of symptoms from disease onset. See Figure 1.1 for a visual depiction of these clinical diagnoses. [Noseworhty 2000] White matter lesions have been the traditional focus of MS research due to their conspicuity in MR imaging and have been extensively studied to determine their pathological origins. Areas of high inflammation with large number of T-lymphocytes and macrophages show significant damage via demyelination and axonal loss, though other mechanisms have been identified; such as the presence of nitric oxide and tumor necrosis factor, increased electrical activity of demyelinated lesions, or by the production of antibodies against axonal antigens [Comi 2003]. Axonal transection is most common in the acute relapsing-remitting phase of MS and decreases 3

18 during the progressive phase of MS. [Kuhlmann 2002] Neuronal reassignment is initially able to compensate for the loss of neuronal and dendritic axons, though it eventually becomes insufficient once a critical mass of brain tissue has been damaged. [Trapp 2009] Figure 1.1: Visual depiction of the most common clinical courses of multiple sclerosis Cortical gray matter lesions have recently been implicated in the cognitive decline of MS patients [Kutzelnigg 2006], though the primary cause of these lesions has not been determined. Meningeal inflammation, neuronal injury, Wallerian / transynaptic degeneration, and demyelination have all been suggested. [Rinaldi 2010] Cortical lesions have been subdivided into 4 groups: Type 1 lesions encompass both white matter and gray matter regions, Type 2 lesions are small, round, and often manifest around a small blood vessel, Type 3 lesions are large and flat, extending from the pial surface to cortical layer 3 or 4 and perhaps spanning 4

19 multiple gyri and sulci, Type 4 lesions extend the full width of the cortex from the pial surface to the white matter-cortical junction. [Peterson 2001] Since tissue damage in MS is cumulative, early treatment is required for positive disease management and cognitive sparing. [Comi 2003] Current disease modifying drugs are intended to counter the auto-inflammatory responses that are very active in the early stages of the disease. Since the efficacy of these drugs is much reduced by the time the secondary progressive phase of MS is reached, better neuroprotective treatments are being developed to address the needs of long term patients. [Buck 2011] Magnetic Resonance Imaging Magnetic resonance imaging (MRI) has been included in the clinical workup of patients with suspected MS due to the conspicuity of white matter lesions in both T1- and T2-weighted images. Despite the low correlation with disability, periodic MRIs can provide a visual indication of the lesion activity and changes in inflammatory incidence over time. [Miller 1996] To date, standard field MRI ( 1.5 Tesla) has been sufficient for depicting white matter lesions, but the change in focus to cortical lesions and other, more subtle pathological processes has necessitated imaging systems with greater capacity for producing images with higher contrast and image resolution. High and ultrahigh field systems are capable of meeting these criteria and are currently being used to develop next-generation imaging techniques. Conventional spin echo sequences have well established their clinical utility for white matter 5

20 lesion detection by producing images with good tissue signal-to-noise (SNR) with a variety of contrast options, while also resisting image artifacts due to radiofrequency and/or static field perturbations. [Mugler 2000] T2-weighted spin echo (T2-SE, see Figure 1.2A) continues being used clinically mainly out of tradition rather than any specific aptitude for depicting lesions. Partial volume effects with gray and white matter coupled with flow artifacts from cerebrospinal fluid (CSF) limit white matter lesion detection [DeCoene 1992]. Kollia et al. [2009] used a PD/T2 weighted TSE sequence in their study of cortical lesions at 7T. They determined that the sequence at 7T could better show lesion boundaries, leading to more accurate lesion characterization than at lower field strengths. To cover the whole brain, several acquisition scans with offsets had to be used in order to compensate for the SAR limitations. Overall, T2-SE continues to lag behind fluid attenuated inversion recovery and other, newer inversion-recovery based sequences. [Filippi 1996; Geurts 2005a; Bedell 1998; Rugg-Gunn 2006] Fluid attenuated inversion recovery (FLAIR, Figure 1.2B) was developed to address the CSF flow artifacts common in T2-SE. FLAIR is T2-weighted and image data is acquired when the CSF signal is around zero. FLAIR images of MS brains show cortical and white matter lesions that are hyperintense to adjacent normal-appearing tissue. Gray matter-white matter contrast is diminished, however, and can interfere with the image reader's ability to distinguish white matter lesions and leukocortical lesions. [DeCoene 1992, Filippi 1996, Nelson 2007] FLAIR has been used in cortical lesion studies with moderate success. At 3T, classifying cortical lesions as intracortical, juxtacortical, or leukocortical was difficult because of the diminished white matter/gray matter contrast differences and may result in miscategorization of these lesions. 6

21 The detection of intracortical lesions was disproportionately low using FLAIR compared with a high resolution MP-RAGE sequence. [Tallantyre 2010] Inversion recovery turbo field echo (IR-TFE; aka MPRAGE, see Figure 1.3A) has also been implemented at 7T with success. [Mainero 2009, Kollia 2009] Using a gradient echo acquisition, this sequence avoids the SAR pitfalls of a spin echo sequence, and with T1-weighting depicts lesions as hypointense to adjacent normal appearing tissue [Blüml 1996]. 3D MP-RAGE allows whole-head high resolution images to be obtained in a reasonable amount of time. The detail in the images has been shown to be beneficial in the classification of cortical lesions [Nelson 2008] and the T1-weighting could be used in a series of follow-up scans to determine lesions that represent permanent tissue damage, or, "persistent black hole" white matter lesions. [Van Waesberghe 1998] Cortical lesion specific MP-RAGE sequences at 7T is useful in differentiating subcortical and leukocortical lesions [Kollia 2009, Tallantyre 2010], assisted by the higher resolution and SNR achievable with the 7T scanner. Tallantyre et al. did report, however, that multiple sequences and contrasts should be used since one isolated sequence cannot account for the full cortical lesion load. Double inversion recovery (DIR, Figure 1.2C) was developed by Redpath and Smith in 1994 to simultaneously suppress signal from two tissues using two inversion pulses. [Redpath 1994] In MS, white matter and CSF signals are nulled leaving only gray matter signal in the resulting image. Removal of white matter signal addresses the white matter-gray matter isointensity that hampers lesion classification in FLAIR images. In studies using DIR, lesion conspicuity has been 7

22 excellent at field strengths ranging from 1.5 T to 7 T and thus has garnered a loyal following, focused on determining its utility as a clinical sequence for cortical and white matter lesion detection. [e.g., Madelin 2008, Nelson 2007, Nelson 2008, Wattjes 2007, Calabrese 2008, Geurts 2005, Pouwels 2006] DIR sequences use, however, a turbo spin echo (TSE) acquisition which as with conventional T2-SE has a high SAR requirement. Coupled with inherently low signal as a result of having two inverting magnetization preparation pulses, DIR has limited utility at ultrahigh field strengths without significant optimization work [Nelson 2007; Madelin 2008]. Cortical lesions are also difficult to differentiate from isolated signal hyperintensities later identified with other sequences as flow artifacts [Tallantyre 2010]. DIR using a turbo field echo (TFE) acquisition has the potential to reduce SAR requirements and make this sequence more versatile at 7T. This will be explored later in this work. Figure 1.2: Image examples for (A) 7T T2-weighted spin echo, (B) 3T FLAIR, and (C) 3T DIR-TSE for different MS patients at the same anatomical level. Lesions are most visible in the DIR-TSE image. 8

23 T2*-weighted images take advantage of the increased magnetic susceptibility between different tissues when scanned by ultrahigh field systems [Abduljalil 2003]. Acquired as complex data, both the magnitude (Figure 1.3B) and phase (Figure 1.3C) components can provide interesting insight into the anatomical structure of MS cortical lesions, including identification of the central vein and a multi-layer appearance. [Mainero 2009, Kollia 2009] Deep gray matter lesions were also much more visible in 7T T2*-weighted images than others. Phase images in particular are showing promise in lesions characterization, as is has been shown that pathological iron accumulation in MS is correlated with disease duration [Hammond 2008]. Figure 1.3: Image examples for (A) 7T IR-TFE, (B) 7T T2*-weighted magnitude, and (C) 7T phase at the same anatomical level. (B) and (C) are the same patient. In this work, we chose to use the IR-TFE (MP-RAGE) sequence, capitalizing on a wide range of contrast options dependent on the acquisition inversion time (TI). Capable of achieving good 9

24 tissue contrast, this sequence family can collect a whole-head, three-dimensional dataset in ten minutes or less; ideal for MS patients with compromised motor control. Good image resolution, lower SAR requirements, and the ability to reduce B1 heterogeneity artifacts with an adiabatic inversion pulse are also advantages for this sequence at 7T. Optimized for cortical lesion detection, IR-TFE with white matter signal attenuation has the potential to be an important component of a standard clinical MS imaging protocol. 10

25 Chapter 2: T1 and Proton Density Measurements Introduction In order to properly optimize the IR-TFE sequence for imaging cortical lesions, the lesion T1 and proton density (PD) values need to be known. The T1 and PD values for cortical MS lesions have not been previously published in the literature. Likewise, the proton density value for white matter lesions is also unknown and although published for other magnetic field strengths, white matter lesion T1 at 7T has also yet to be determined. The first step in the optimization project was to determine these values at 7T which could then be used in the optimization process. In this chapter, the T1 and PD for white matter (WM), gray matter (GM), white matter lesions (WML), cortical lesions (CL), and cerebrospinal fluid (CSF) were measured. Methods Eight MS patients (mean age 45.3 years [range = 30-58], 3 male/ 5 female, 5 SPMS/ 3 RRMS) and seven healthy control subjects (mean age 43.9 years [range=28-56], 3 male/ 4 female) were recruited and scanned at 7T (Philips Achieva, Cleveland, OH) using a 16-channel phased array head coil (NOVA Medical, Boston, MA). Written informed consent and local IRB approval were obtained from each participant. A series of six short IR-TFE scans with a shot interval of TS = 4200 ms and inversion times of TI = 11

26 70 ms, 200 ms, 700 ms, 1000 ms, 2000 ms, and 2900 ms were used. Other relevant sequences parameters included: TR = 4.0 ms; TE = 1.94 ms; flip angle = 8 ; TFE factor = 300; acquired voxel size = 0.5 x 0.55 x 1.4 mm 3 ; reconstructed voxel size = 0.23 x 0.23 x 0.70 mm 3 ; 60 slices; total scan time = 2:41 min. per scan. The short scan times were selected primarily for patient comfort and compliance. To assess any bias that may be introduced in the T1 measurements by using a TI less than twice the estimated tissue T1, a second set of IR-TFE scans were acquired on three control subjects using a longer TS of 6000 ms, allowing for an additional scan with a TI of 4500 ms to be included in the series for a total of seven image sets. Cortical and white matter lesions used in the T1 measurements were identified using an additional high signal-to-noise ratio (SNR) white matter attenuated IR-TFE and 2D T2*-weighted Fast Field Echo (FFE) scans [Mainero 2009; Rinaldi 2010; Kollia 2009]. Relevant acquisition parameters for the IR-TFE scan were TS = 3700 ms; TI = 550 ms; TR = 4.1 ms; TE = 1.6 ms; flip angle = 8 ; TFE factor = 165; acquired voxel size = 0.4 x 1.0 x 1.4 mm 3 ; reconstructed voxel size = 0.38 x 0.38 x 0.70 mm 3 ; 128 slices; averages = 2; total scan time = 10:04 min, and for the 2D FFE scan: TR = 1000 ms; TE1 = 6.5 ms; TE2 = 22 ms; flip angle = 30 ; acquired voxel size = 0.33 x 0.33 x 1.0 mm 3 ; reconstructed voxel size = 0.3 x 0.3 x 1.0 mm 3 ; 34 slices; averages = 1; total scan time = 8:34 min. B1 inhomogeneity is a consistent issue at ultrahigh field, so B1 maps were acquired for each subject and areas with insufficient flip angle (<50% of specified value) were excluded from analysis (see Figure 2.1A). White matter lesions were identified in the high SNR white matter attenuated (WHAT) IR-TFE 12

27 images as bright, focal hyperintensities within the white matter regions. Cortical lesions were identified independently by two readers using structural information from the 2D FFE images [Mainero 2009] and gray matter/cortical lesion contrast from the high SNR white matter attenuated IR-TFE images using the method described in [Bluestein 2010] (Figure 2.1). Figure 2.1: (A) Whole brain high SNR white matter attenuated IR-TFE image with the inset box indicating the anatomical area of the white matter attenuated IR-TFE (top) and T2*-weighted FFE (bottom) images. A cortical lesion (arrows) can be identified via hyperintense contrast to adjacent gray matter in the white matter attenuated image and by hypointense borders in the T2*-weighted FFE sequence. (B) White matter lesions are easily visible in the white matter attenuated images (arrowheads) and are also seen on the T2*-weighted FFE images. Areas of the brain with signal loss due to B1-inhomogeneity greater than 50% were excluded from the study (dotted line). To correct for subject motion within the scan session, the low SNR IR-TFE T1 measurement scans were registered to each other using FSL (Oxford U., UK) [Woolrich 2009, Smith 2004]. Regions of interest (ROIs) for white matter, gray matter, white matter lesions, cortical lesions, and 13

28 cerebrospinal fluid were manually drawn and the mean signal intensities were recorded along with the standard deviation. To ensure that the same pixels were analyzed in each IR-TFE image set, the ROI pixel locations were saved and masked onto the corresponding slices of the other 5 IR-TFE image sets at different TI. In the MS patients, 64 ROIs were drawn total in WM, 62 in GM, 22 in CLs, 23 in WMLs, and 17 in CSF. In the control subjects, a total of 57 ROIs were each drawn in WM and GM and 5 in CSF. In the long TS scans on the control subjects, 32 ROIs were measured in WM and GM. SNR was determined in volunteer studies by repeating identical scans of both the high SNR IR-TFE sequence and the low SNR IR-TFE T1 series. The repeated scans were subtracted and the SNRs for selected ROIs were determined as the signal average of the two scans divided by the standard deviation of the same ROI in the subtracted image. Using IDL (ITT Visual Information Systems, Boulder, CO), the T1 of WM, GM, WML, and CL was estimated with a non-linear least squares curve fitting algorithm, MPFIT [Markwardt 2008], for which the source code is freely available online. The algorithm iteratively fits the measured signal intensities to a calculated relaxation curve. The fitting function in this case is the IR-TFE signal equation developed and described by Deichmann, et al. [Deichmann 2000] and is dependent on T1 and PD (see Chapter 3, Eq. [3.4]). CSF T1 was fitted directly using the Deichmann model for each CSF ROI and averaged. For each tissue, any T1s that exceeded this average T1 CSF value were also fitted directly. If any of the T1 values remained larger than the average T1 CSF they were excluded from further quantitative analysis. The scaling factors that were output from the curve fit algorithm were used to compute proton density (PD) by normalizing them to the scaling factor of CSF, such that PD CSF = 1.0 equivalent to the proton 14

29 density of free water. As with T1, PD calculations that exceeded PD CSF = 1.0 were first fitted manually; any that remained above PD CSF were then excluded from further analysis. Welch's two sample t-test was implemented in the R statistical package [R Development 2008], and used to analyze significance between the targeted tissues or subject groups (α = 0.05): WML/CL vs. normal-appearing WM/GM, MS vs. control, and short TS vs. long TS. This particular test was chosen because it can account for the possibility that unequal variances may exist between the groups. To test the validity of the methods presented here, the calculated T1 and PD values were included in the Deichmann model [Deichmann 2000] and plotted. The signal curves were then compared qualitatively to in vivo images. Results Figure 2.1 shows example images of cortical and white matter lesions that were identified using the high SNR IR-TFE and 2D FFE images. Figure 2.2 shows example images of cortical and white matter lesions that were located using the high SNR IR-TFE images, along with the corresponding slice in the T1 measurement data sets at TI = 700 ms and 2000 ms. The relative increase in SNR between the high SNR IR-TFE white matter attenuated images and the T1 measurement data was 2.8; determined by voxel and matrix size, number of averages, and bandwidth. On average, the ROIs contained 38 pixels (about 2 mm 2 with an average radius of 0.8 mm). B1 inhomogeneity was most noticeable in the high SNR IR-TFE images and regions with B1-field induced signal loss greater than 50% (see Figure 3.1A) were excluded from 15

30 analysis. Figure 2.2: High SNR white matter attenuated IR-TFE images showing an example white matter lesion (A, arrowhead) and cortical lesion (B, arrow). The same lesions were identified on the T1 measurement IR-TFE images (C, D; TI = 700 ms and E, F; TI = 2000 ms) and used for the subsequent T1 and PD calculations. (A) and (B) have 2.8 times the image SNR than (C-F). The average calculated T1 and PD values are summarized in Table 2.1 and p-values are tabulated in Table 2.2. Figure 2.3A shows a box and whisker plot comparing the T1 distributions of MS patients and healthy controls. Both cortical (T1 = 2420 ± 610 ms) and WM lesions (T1 = 2530 ± 890 ms) had longer T1 than adjacent normal appearing white matter (T1 = 1350 ± 440 ms) and gray matter (T1 = 2000 ± 580 ms) with all p < T1 values of normal appearing white matter and cortical gray matter in MS patients tended to be higher than those in healthy subjects (both p < 0.001). The T1 relaxation times for cortical lesions and white matter lesions were not statistically different from one another. 16

31 Table 2.1: Median T1 and proton density values of white matter (WM), gray matter (GM), white matter lesions (WML) and cortical lesions (CL) measured in MS patients. T1 and PD values of white matter and gray matter of healthy controls are included for comparison. MS patients Controls WM WML GM CL WM GM T1 (ms) 1350± ± ± ± ± ±530 PD 0.68± ± ± ± ± ±0.20 In comparing the long TS and short TS IR-TFE series in controls, both WM (T1 long = 1190 ± 180 ms) and GM (T1 long = 2240 ± 540 ms) were significantly larger in the longer TS scans (both p < 0.001). Since the short TS model was insufficient for automatically fitting CSF data, the signal curves were fit manually, resulting in an average T1 CSF = 4470 ms. Table 2.2: P-values are shown comparing the calculated WM and GM T1 and PD for MS patients and healthy controls. T1 p-value PD p-value MS-WM : Control-WM <0.001 NS* MS-GM : Control-GM <0.001 NS MS-WM : MS-GM <0.001 <0.005 MS-WM : MS-CL <0.001 <0.005 MS-WM : MS-WML <0.001 <0.001 MS-GM : MS-CL <0.05 NS MS-GM : MS-WML <0.005 <0.005 MS-WML : MS-CL NS NS *NS: not significant Figure 2.3B shows the relative distributions for the calculated proton densities for both MS and control subjects. The calculated proton density for white matter lesions (PD = 0.91 ± 0.13) was 17

32 larger than adjacent normal appearing white matter (PD = 0.68 ± 0.18; p < 0.001), while cortical lesions (PD = 0.86 ± 0.17) and normal appearing gray matter (PD = 0.78 ± 0.19) were statistically similar (p > 0.15). White matter and gray matter PD values in MS are not statistically different to those from healthy controls (both p > 0.06), and the proton densities calculated for cortical lesions and white matter lesions were statistically identical (p > 0.32). Comparing the short and long TS/TI measurements in controls, the proton densities for white matter were statistically similar (p > 0.13) and for gray matter were not (p < 0.005). Figure 2.4 shows that the calculated T1 and PD values were commensurate with the tissue contrast obtained in in vivo images. Figure 2.3: (A) Box plot showing the relative distributions of T1 measurements for MS patients and healthy controls for white matter, gray matter, cortical lesions and white matter lesions. (B) Box plot showing the relative distributions of tissue proton densities for MS patients and healthy controls. Discussion In healthy subjects, short IR-TFE scans with TS / TI max = 4200 / 2900 ms. resulted in slightly shorter T1 values than previously published data [Rooney 2007; Wright 2008; Ikonomidou 2006; 18

33 Li 2006; de Graaf 2010]. T1 values in controls for TS / TI max = 6000 / 4500 ms were better matched, indicating a longer TS is needed in future measurements, ensuring the full relaxation curve is included in the analysis. Scan times for relaxation time measurements can be somewhat long, but methods proposed in this study require acquisitions of several image sets with a duration of less than 4 min. each (TS = 6000 ms). This was well tolerated by patients and control subjects alike. Figure 2.4: Graph showing a representative signal response curve for an IR-TFE sequence given the calculated T1s and PDs presented in this study. White matter, gray matter, white matter lesions, and cortical lesions are included. The simulated tissue contrast is similar to that seen in in vivo images. Minimal B1 inhomogeneity can be seen in the MR images. TS = 6000 ms, TR = 4.0 ms, TE = 1.9 ms, flip angle = 8. 19

34 The T1 values at 7T for WM and GM found in literature are summarized in Table 2.3. Although a variety of methods are used, all the calculated T1 values are comparable to one another, including the values determined in this study. Rooney, et al. [Rooney 2007] used a modified Look-Locker sequence with 32 samples acquired after an adiabatic excitation pulse. This resulted in a single slice acquisition that required 23 min. of scan time. This is excessive for use with patients and is not suitable for those with compromised motor control. Several authors have presented other methods for measuring tissue T1: DESPOT1 [Li 2006], IR-EPI [Wright 2008, Ikonomidou 2006, de Graaf 2010], IR-TSE [Wright 2008], and IR-TFE (aka. MPRAGE) [Wright 2008]. The SPGR-based DESPOT1 is fast and efficient for creating T1 maps, though the sequence is quite sensitive to B0 and B1 inhomogeneity requiring significant post-processing [Li 2006]. The IR-EPI and IR-TFE sequences resulted in very similar T1 measurements [Wright 2008]. Though fast, the EPI readout is inherently low resolution, and as such is not a good choice for measuring small anatomical structures in this case, cortical lesions in MS. Wright's [Wright 2008] IR-TSE sequence consistently measured lower T1 values than the other two tested sequences, perhaps due to magnetization transfer effects and/or imperfect modeling of the slice profile and longitudinal relaxation curves. The IR-TSE sequence also requires high SAR levels so its application at ultrahigh field is limited. 20

35 Table 2.3: Summary of normal appearing white matter and gray matter T1 values in this study and of those in recent literature First Author Year Published White Matter T1 (ms) Gray Matter T1 (ms) Population Method This Study ± ±580 MS IR-TFE This Study - 890± ±530 Healthy IR-TFE de Graaf ± ±132 MS IR-EPI de Graaf ± ±109 Healthy IR-EPI Wright ± ±150 Healthy IR-TFE Wright ± ±125 Healthy IR-TSE Wright ± ±125 Healthy IR-EPI Rooney ± ±103 Healthy Mod. Look- Locker Ikonomidou ± ±45 Healthy IR-EPI Li ± ±100 Healthy DESPOT1 Similar to our study, Parry, et al. [Parry 2002], Stevenson, et al. [Stevenson 2000] and de Graaf, et al. [de Graaf 2010] reported on finding in MS patients that the T1s of normal-appearing white matter and gray matter tended to be higher than in healthy control subjects. Our work here supports that finding as well. The increase in T1 relaxation times of normal appearing GM and WM may be attributed to having a higher concentration of free water throughout the brain as a result of neuronal apoptosis occurring during the process of lesion formation. [Peterson 2001, Wegner 2006] Our PD results for normal appearing white matter and gray matter are in line with those presented by Tofts [Tofts 2003]. More recent proton density measurements could not be found, but we expect no change of PD with increasing B0 fields. To our knowledge, cortical and white matter lesion T1 and proton density values have not been previously published at 7T, thus our 21

36 results represent an important step forward in the optimization of MR sequence for detecting cortical lesions. While analyzing the data for white matter lesions, it was noted that the distribution of T1 values was distinctly bimodal. It is suggested that lesions with longer T1s may represent older, more established lesions whose high T1 values represent greater tissue damage and axonal loss [van Walderveen 1998, Bitsch 2001]. This will be discussed later in Chapter 7. The calculated CSF T1 is consistent with previously published results [Rooney 2007]. Extreme values beyond the theoretical limit imposed by the measured CSF T1 and PD were excluded from analysis. There are wide standard deviations in both the T1 and PD values of all the tissues included in this study, due to significant inter-subject variation. Intra-subject variation is much lower. The shorter TS used in this study may have led to shorter T1s due to some poor curve fits. Nevertheless, statistical significance was obtained for differentiating WM, GM, CL, and WML in MS patients and WM and GM in control subjects. Furthermore, the precision achieved in this initial study is sufficient for our primary objective; determining white matter lesion and cortical lesion T1s and PDs to be used in contrast optimization calculations for different pulse sequences. Future studies aimed at characterization of normal appearing tissue and lesions will require longer TS/TI max and also should be controlled more tightly for ROI location and other factors including age, gender and MS treatment regimen. Such studies will also require a larger number of patients, though the data presented here can serve as a guide for these important studies. 22

37 Chapter 3: White Matter Attenuation Introduction A MR sequence that minimizes white matter signal may generate high GM-WM contrast and also lessen the effects of partial voluming for adequately visualizing cortical lesions. In this Chapter, the tissue parameters measured in Chapter 3 are applied to simulations of the white matter attenuated (WHAT) sequence. The sequence is optimized to achieve maximal GM-CL contrast for a 10 minute scan. This chapter was recently published in Magnetic Resonance Imaging [Bluestein, 2012]. Methods Sequence White matter attenuation (WHAT) is achieved using an Inversion Recovery Turbo Field Echo sequence with the acquisition parameters adjusted such that the center of k-space is acquired as the white matter longitudinal magnetization crosses zero during relaxation back to equilibrium. Figure 3.1 shows a schematic of the WHAT sequence with the main sequence parameters indicated. The shot interval, TS, is the time between successive 180 inversion pulses. The inversion time, TI, is the time between the inversion pulse and the center of k-space (using centric phase encoding). The delay time, TD, is the duration between the last k-space line and the next inversion pulse. The repetition time, TR, is the time between adjacent α-pulses; and the turbo field echo factor, TFE, is the number of α-pulses during the turbo field echo 23

38 readout. The echo time, TE, is the time that elapses between each α-pulse and the resulting turbo field echo. Since sequence contrast is dependent on a multitude of interdependent parameters, sequence optimization was done by a series of numerical simulation and confirmatory experiments; including preliminary studies of patients with multiple sclerosis for initial assessment of the WHAT sequence for cortical lesion detection. Figure 3.1: A 180 inversion pulse is followed by a train of turbo field echo readouts. α = flip angle (FA), TE = echo time, TR = repetition time, TI = inversion time, TFE = turbo field echo factor (number of α-pulses), TD = delay time, and TS = shot interval. The cross-hatched boxes represent acquisition. Centric k-space encoding is used. Theory The mathematical model was based off the IR-TFE (a.k.a. MPRAGE) signal equation developed and described by Deichmann, et al [Deichmann 2000]. The MR signal with centric k-space encoding, S C, is related to the tissue magnetization, M, by: (3.1) 24

39 where α is the flip angle and M 1 is defined as: (3.2) with, ( ) (3.3a) ( ) ( ) * + (3.3b) where: M * 0 = the asymptotic limit of longitudinal magnetization during turbo field echo as the number of α-pulses is increased, T1 * = the effective time constant for the approach to M * 0, -pulses (TR * TFE factor), and M 0 = the longitudinal equilibrium magnetization. Proton density (PD) and T2* scaling can be included in the simulations with the additional term,, modifying Eq.(3.1) to: (3.4) The empirically determined tissue parameters T1 and PD for WM, GM and cortical lesions (CL), and cerebrospinal (CSF) used in this study are listed in Table 4 [Bluestein 2012]. Also listed are the average published T2* values for WM and GM [Li 2006a, Peters 2007]. Since the TEs were 25

40 selected to be as short as possible ( ms), the T2* term ( ) was neglected in the simulations. Table 3.1: Tissue T1, T2*, and proton density (PD) values used in simulated IR-TFE (MPRAGE) tissue signal calculations. T1 (ms) T2* (ms) [Li 2006; Peters 2007] PD White matter Gray matter Cortical lesions Cerebrospinal fluid Initial Tests and Parameter Selection Initial studies indicated that images with excellent GM-WM contrast are obtained when TI is selected such that WM signal is nulled [Bluestein 2012]. Accordingly, our initial studies and simulations were aimed at finding conditions that nulled WM signal. Figure 4.2 shows example calculations of the signal as a function of TI. Since initial experiments used the scanner default inversion pulse an optimized, but non-adiabatic pulse we simulated the effects of radiofrequency (RF) inhomogeneity by scaling both the inversion pulse and the read-out flip angle, α, with a scaling factor, c rf. The computations demonstrate that image contrast is extremely sensitive to RF inhomogeneity. For small RF inhomogeneity (0.8 < c rf < 1.0) the TI that nulls WM shifts slightly downward (Figure 4.2B), however GM signal remains brighter than WM (Figure 4.2A). With larger RF inhomogeneity (0.67 < c rf < 0.80), the signal curves shift to even lower TI, and for c rf < 0.67, GM signal is nulled, WM is brighter and the contrast is inverted (not 26

41 shown). Figure 3.2: (A) Example simulation of the IR-TFE sequence signal as a function of TI for TS = 3700 ms for WM [solid] and GM [dashed], and (B) computation of the TIs nulling WM [solid] and GM [dashed] signal as a function of TS. Computations used TFE = 165, TR = 4.1 ms, FA = 8, and tissue parameters from Table 1. To simulate the effects of RF inhomogeneity, both the inversion pulse and the read-out flip angle, α, were scaled by a scaling factor. For c rf = 1: α = 8, IR pulse = 180 [black line]; for c rf = 0.80: α = 6.4, IR pulse = 144 *gray line+. Note that the TI nulling WM decreases from 595 ms to 510 ms for c rf = 1.0 and 0.80, respectively. For the TS/TI pairs in (B), absolute GM signal is larger than WM signal. Though RF inhomogeneity is a dominant determinant of image contrast, the TFE-factor, TR and the readout flip angle, α, also affect the null point TI for WM. Figure 4.3 shows example calculations for different TFE-factors, TRs, and readout flip angles. In all cases, the WM-nulling TI shifts significantly for TS < 4000 ms. Smaller TFE factors, TRs and flip angles all increase the TI for nulling WM. In addition, the GM signal decreases with the flip angle, proportional to, as well as with increasing TR and TFE. 27

42 Based on these initial tests, parameters were selected for a pilot study aimed at exploring cortical lesion detection with the WHAT sequence. However, it was evident that adiabatic inversion pulses (which would stabilize inversion throughout the image volume) and subsequent further parameter optimization would be necessary to identify optimal conditions for cortical lesion detection. Figure 3.3: (A) WM nulling TI values as a function of TS for different TFE-factors: TFE = 165 *dashed+, TFE = 244 *solid+, TR = 4.1 ms, α = 8 ; (B) different readout flip angles: α = 8 *solid+, α = 4 *dashed+, TFE = 165, TR = 4.1 ms; and (C) different TRs: TR = 4.1 ms *dashed+, TR = 8.2 ms *solid+, TFE = 165, α = 8. In (D), (E), and (F), the computed GM [black] and cortical lesion signals [gray] for the condition in (A) through (C) are shown. Initial Evaluation of WHAT for Cortical Lesion Detection Eight patients (4 RRMS, 4 SPMS; mean age = 42 years [range 24-58]; 3 male/5 female) were scanned at 7T (Philips Healthcare, Cleveland, OH) with a 16-channel phased array head coil 28

43 (NOVA Medical, Wilmington, MA). The study was approved by the local IRB board and written consent was obtained from each patient. The sequence parameters for the axial WHAT sequence used to evaluate cortical lesion detection were: TS = 3700 ms, TI = 550 ms, TR = 4.1 ms, TE = 1.6, TFE-factor = 165, FA = 8, FOV = 220x165 mm 2, matrix = 548x165, acquired voxel size = 0.4x1.0x1.4 mm 3, reconstructed voxel size = 0.38x0.38x0.7 mm 3, reconstructed slices = 128, NSA = 2, scan time = 10:04 min. An axial B1 map was acquired using scanner tools [Yarnykh 2007] and used to mask regions where B1 dropped below the contrast inversion threshold. These regions were excluded from further analysis. The WHAT images were read and analyzed independently by two experienced readers, who were also blinded to the disease status of each patient. The readers were advised to mark and classify cortical lesions into two groups: leukocortical (type 1) and intracortical (types 2-4). A third reader tabulated the counting results and moderated a consensus reading of the images. A kappa statistic was used to evaluate inter-reader agreement of marked lesions. WHAT Parameter Optimization Once an adiabatic hyperbolic secant inversion pulse became available, further signal simulations and tests were done. First, the settings for the adiabatic pulse were optimized to achieve good inversion throughout the image volume. This was done by setting a control parameter nominal flip angle ranging from 750 to 2000, corresponding to pulse lengths from 8.5 ms to 61 ms. 29

44 Using this strong inversion pulse increased the shot interval, TS, due to specific absorption rate (SAR) limits and instituted an absolute minimum TS of 3500 ms. Next, the minimal TSs achievable for different voxel sizes were recorded. This included checking pulse sequence interdependencies on TR, α, TFE-factor, image plane orientation and SAR settings. TI values for nulling WM were computed for these minimal TSs. From these tests, scan parameters achieving highest SNR per scan time were selected. These are listed in Table 3.2. We opted to use 10 minutes as our reference scan time. Finally, SNR and contrast-to-noise ratio (CNR) measurements were performed in eight healthy volunteers (mean age = 39 years [range 20-56], 6 male/2 female). Subjects were scanned at 7T with a 16-channel phased array head coil with IRB approval and written, informed consent. SNR measurements were obtained by repeating scans with RF on and off, or by repeating identical scans and subtracting the images. The SNRs for the selected tissue regions of interest (ROIs) were determined as the signal average of the two scans divided by the standard deviation of the same ROI in the noise image (the RF off or the difference image). SNRs were measured for different TS/TI settings and voxel sizes using the optimized parameters in Table 3.2. Parallel imaging was not used in these studies (SENSE factor = 1). 30

45 31 Table 3.2: SNR/CNR optimized WHAT sequence parameters at different resolutions for a 10 minute scan. Voxel Size Matrix N z FOV z TS min TR min TE min BW Calc. TI WM=0 Exp. TI WM=0 Calc Exp Est. (mm 3 ) N x x N y (mm) (ms) (ms) (ms) (Hz) (ms) (ms) * GM SNR GM ** CNR GM-CL 0.33x0.33x x x0.35x x x0.4x x x0.5x x x0.6x x x0.7x x x1.0x x FOV = 220x180 cm; TFE-factor= Ny; minimal TR, TE and water/fat shift; partial echo; FA = 8 o ; orientation orthogonal to gradient axes; and NSA = 1. The number of slices, N z, was adjusted to give a total scan time of 10 min; this includes the scanner s default oversampling by a factor of = * The experimental null was determined from scans of healthy subjects by changing TI = ms near the computed value and recording the TI value with minimal WM signal. ** The experimental SNR for GM was determined from a set of RF on/rf off scans under the listed conditions, except the number of slices which was reduced to shorten scan time for the SNR measurement. The listed experimental SNR was corrected by to account for using fewer slices in the acquisition. 31

46 The signal, S c, was computed for each of these conditions according to Eqs. ( ) and scaled to the experimentally measured SNR exp using: (3.5) where is the scaling factor matching the theoretical signal, S c, to the measured SNR exp. Results Pilot Study Demonstrating the Utility of WHAT for Cortical Lesion Detection WHAT-TFE images showed excellent GM-WM and GM-CL contrast, with cortical lesions depicted as focal hyperintensities within the cortex (Figure 3.4). Type 1 lesions were especially visible with this tissue contrast. In the eight MS patients a total of 292 cortical lesions were identified, of which 209 were marked by both readers (72%), resulting in moderate inter-reader agreement (κ = 0.53). A majority (62%) of the lesions marked by both readers were classified as type 1 lesions. In vivo SNR measurements of WM, GM, and CLs confirmed the excellent tissue contrast noted by the readers: SNR WM = , SNR GM = , and SNR CL = ; making the gray matter cortical lesion CNR =

47 Figure 3.4: Examples of type I, II, III and IV cortical lesions showing their appearance in WHAT images. WHAT Sequence Parameter Optimization Figure 3.5 shows the significant improvement achieved with the adiabatic hyperbolic secant pulse when compared to the default inversion pulse in a healthy subject. With the adiabatic pulse, the chosen GM-WM contrast was achieved throughout the image volume. Significant contrast inversion between GM and WM occurred due to lower than 180 degree inversion pulses with the default pulse. Testing different settings for the adiabatic inversion pulse showed a GM signal increase from 8.7 (1200 ; 21.9 ms pulse length), to 11.1 (1600 ; 38.9 ms pulse length), and 13.3 (2000 ; 61 ms pulse length). The 2000 pulse was selected as best compromise 33

48 between optimal inversion and SAR limitations. The signal simulations in Figure 3.2A show that best bright GM black WM contrast is achieved when TI is selected to null WM. Figure 3.2B shows the TS/TI pairs that null WM signal. GM signal increases with increasing TS (Figure 3.3D-F). Thus longer TSs are favorable in terms of increasing GM and cortical lesion SNR. Figure 3.6 confirms this with experimental data. However, long TS also increases scan times beyond clinically acceptable lengths, especially for very high spatial resolution. Scan parameters were thus further evaluated to find conditions with optimal scan efficiency, providing highest SNR per unit scan time with particular emphasis on high spatial resolution imaging important for cortical lesion imaging. This optimization involved testing feasible settings based on scanner limitations and numerical signal simulations. Figure 3.5: (A) WHAT with default IR pulse compared to (B) WHAT image with a hyperbolic secant adiabatic pulse. 34

49 Figure 3.6: Measured WHAT SNR and simulated signal (lines) for fixed TFE = 244 in a healthy volunteer for white matter [diamonds], cortical gray matter [triangles], and cerebrospinal fluid [squares]. Measured data are for TS/TI pairs = 6000/750 ms, 5000/730 ms, 4000/700 ms, 3000/620 ms, and 2000/470 ms, with respective scan times of 10:07, 8:26, 6:45, 5:04, and 6:49 min. Other parameters were TR = 4.1 ms; TE = 1.6 ms; FA = 8 ; FOV = 220x170 cm; matrix = 316x244; 40 slices (51 shots); voxel size = 0.7x0.7x1.4 mm 3 ; BW = 600 Hz; and NSA = 2. For axial brain scans, the best scan efficiency is achieved for a rectangular field-of-view (FOV) when the TFE-factor equals the number of in-plane phase encoding steps, N y (all in-plane phase encode steps are acquired in one shot interval TS). Alternatively, two shot intervals could be used to acquire the data, but this method would only be worthwhile if the minimum shot interval, TS, for TFE = N y /2 is half of the minimum TS for TFE = N y. The minimal achievable TS depends on SAR limitations, phase encoding order, and = TR TFE. TR in turn depends on voxel size, TE, the readout bandwidth (BW), and the image plane orientation. We chose to assess scan efficiency for a range of near isotropic voxel sizes, making slice thickness twice the in plane resolution, Δx = Δy = 2Δz. Using a FOV of 220x180 mm (suitable for most head sizes), minimal 35

50 TS, TR and TE were determined for each voxel size using maximal SAR and gradient strengths (Table 3.2). Also included in Table 3.2 is the maximal number of slices, N z, that can be acquired in 10 minutes for the listed minimum TS; including the scanner default slice direction oversampling of N shot /N z = 1.28 used to prevent slice aliasing. Figure 3.7: Experimental SNR per 10 minute scan time for different resolutions. Shown are the measured WM [diamonds] and cortical GM [triangles] SNR values for 3 volunteers, and the calculated GM and CL SNR using the theoretical signal computation with a readout flip angle of 8, matching the measured data and parameters from Tables 1 and 2. The computed signals were corrected for voxel size, matrix and bandwidth according to Eq. (5), and scaled by a factor of 16 to match the measured data. The experimental GM signal measured for a 1x1x2 mm 3 voxel size may be too large due to partial volume averaging with adjacent CSF. The dotted line shows the average for measured WM SNR, and represents the measured background noise. The range of TFE, TR, TE, flip angle, and BW were then further evaluated to determine if further scan time efficiency could be achieved. First, Figure 3.3D indicates that, the GM signal changes little with TFE. Minimal TSs were determined for TFE = N y /2, but, in all cases, they were larger 36

51 than half the minimal TS for TFE = N y. We explored the feasibility of using larger readout segment flip angles to increase GM signal [Eq. (3.1), Figure 3.3E]. However, because the minimal TS is SAR limited, the flip angle cannot be increased without increasing TS. For low spatial resolution (>0.7 mm in-plane), TS and FA could be further balanced to increase SNR, however for high resolution scans, increasing the flip angle is not possible or advantageous. Finally, SNR can be increased by decreasing the readout bandwidth. However this will increase TE and in turn, TR, and result in decreased GM and cortical lesion signal (Figure 3.3F). Figure 3.7 shows measured cortical GM SNR for different spatial resolutions using a flip angle of 8 and the optimized parameters listed in Table 3.2. Also shown is the computed GM SNR based on signal calculations [Eqs. ( ), GM: S c = 0.033] and scaling for the resolution parameters according to Eq. (3.5), with a scaling factor = 16 to match the experimental SNR. Also shown is the computed SNR for cortical lesions (CL: S c = 0.058). The flip angle may be further increased to 15, increasing the computed GM and cortical lesion signal to and 0.072, respectively. The estimated CNR GM-CL values listed in Table 3.2 also indicate that lower resolution scans have higher GM-CL contrast. Discussion In summary, cortical lesion detection at 7T is feasible by using a WHAT sequence which produces high contrast between white matter, gray matter and cortical lesions. SNR efficiency was maximized as a function of spatial resolution (Table 3.2) through testing a wide range of parameters and signal simulations. 37

52 In our study, two independent readers detected 209 lesions in 8 patients (26 lesions per patient on average). This lesion detection rate is high in comparison to previously published work even though much of the imaged volume had to be excluded due to RF inhomogeneity with the nonadiabatic pulses in our pilot study of MS patients. In comparison, Mainero, et al. [Mainero 2009] recently published a study using a T2*-weighted FLASH sequence for cortical lesion detection at 7T and reported detection of 199 lesions in 16 patients (average of 12 lesions per patient). Our study detected mostly type 1 lesions, however, Mainero, et al. report cortical lesion type distributions mirroring those reported in histology literature [Peterson 2001, Wegner 2006]. Cortical lesion studies at 1.5T and 3T using DIR or T1- weighted sequences report much lower lesion detection rates of 6-8 lesions per patient [Nelson 2008, Calabrese 2008, Calarese 2009, Nelson 2007]. DIR sequences use two inversion pulses timed such that the signal from both WM and CSF is eliminated, thus generating high contrast for gray matter. This enhances subtle gray matter abnormalities and reduces partial volume artifacts, and DIR studies of MS patients at 1.5T and 3T show very promising results for cortical lesion assessment [Wattjes 2007, Nelson 2008, Calabrese 2008, Calabrese 2009, Nelson 2007, Geurts 2005, Bagnato 2006]. However, DIR suffers from inherently low SNR and is susceptible to flow-related artifacts, which may lead to false-positive lesion identification [Nelson 2008]. Additionally, only DIR turbo spin echo (TSE) sequences have been implemented, but due to high SAR requirements of the TSE acquisition, 38

53 this sequence is not favorable for use at 7T. Thin-section three-dimensional T1-weighted imaging improves image SNR and spatial resolution compared to DIR. Both 3D-magnetizationprepared rapid acquisition with gradient echo (3D MP-RAGE) at 3T [Nelson 2008] and T1- weighted 3D-spoiled gradient echo (3D SPGR) at 1.5T [Bagnato 2006] have shown promise in cortical imaging where lesions are dark compared to adjacent GM. Since it is impossible to validate in vivo cortical lesion counts in the same way as MRI/histological comparisons are used for postmortem specimens [Pitt 2010], reader bias is a significant issue. In this study, readers were trained in cortical lesion detection via repeated exposure to both MS specimen images with histologically confirmed lesions and in vivo images of MS brains with various levels of lesion activity. Although the MRI appearance of formalinfixed and living tissue is different [Zachariah 2010], repeated and sustained exposure to both is currently the best way to guide future reader training. The WHAT sequence is very promising for cortical lesion detection, nevertheless it has several shortcomings. Since cortical lesions and CSF appear hyperintense, it can be difficult to differentiate them. Type 3 lesions manifest at the cortical GM-CSF boundary, and can be easily obscured or mimicked by similar GM and CSF signal intensities. Similarly, small vessels show inflow enhancement and it may be difficult to distinguish vessels and small type 2 lesions. Furthermore, WHAT image contrast is solely based on tissue T1 differences, unlike FLAIR and DIR that are based on turbo spin echo (TSE) sequences using long TEs, introducing T2 contrast. Further studies are needed to assess the importance of T2 effects in enhancing contrast 39

54 between cortical lesions and adjacent GM. Finally, our study only evaluated SNR and CNR efficiency, which decrease with increasing spatial resolution (Figure 14). It is known that lesion detection depends on both CNR and spatial resolution [Zachariah 2010]. However, the best balance between CNR and voxel size including the relative advantage of isotropic resolution vs. high in-plane resolution with thicker slices has yet to be determined. Future studies testing different resolutions with fixed SNR and CNR are needed. Table 3.2 and Figure 3.7 show that for high spatial resolution (voxel sizes smaller than 0.5x0.5x1.0mm 3 ), SNR efficiency and GM-CL contrast cannot be further optimized by sequence parameter optimization alone. Thus, no increase in scan efficiency can be achieved by collecting the phase encoding steps in two rather than one TS interval. This also showed that the minimal achievable TS is SAR limited due to the adiabatic inversion pulse. Better adiabatic pulses requiring less RF power or multi-channel transmit could help overcome RF inhomogeneity problems [Katscher 2003]. Further improvement may also be achieved with larger numbers of receiver arrays. For lower spatial resolutions, SNR is high and additional optimization options are available. Scan time can be reduced by using parallel imaging and/or by using the turbo field echo readout train not only for in-plane, but also for slice phase encoding. Our study focused on optimizing the IR-TFE sequence for white matter attenuation (WHAT). However, the IR-TFE sequence through selection of TI can be adjusted to give a multitude of different tissue contrasts. For example, TI could be adjusted to null cortex such that lesions would appear hyperintense. This may increase lesion conspicuity, but may not improve lesion- 40

55 vessel or lesion-csf differentiation. Alternatively, TI could be selected to null cortical lesion signal, which would allow differentiation of lesions and vessels, but may reduce GM-CSF contrast and hinder depiction of cortex boundaries. Finally, TI can be adjusted to give classic T1 contrast showing CSF black, WM hyperintense, and GM with mid-range signal. Cortical lesions would appear darker than surrounding GM, allowing differentiation from vessels. Finally, at least for moderate spatial resolution, it may be possible to implement a multi-contrast sequence, where readout segments for different effective TI are placed in the center of k-space and combined with some kind of view sharing. Optimization of all these different options can follow the methods outlined in this work, and initial estimation of SNR and CNR are possible using the results in Table 3.2 and Figure 3.7. White matter attenuated (or otherwise optimized IR-TFE) sequences are very promising for cortical lesion MRI. This approach may be especially promising at 7T, since T1s are longer and spread further apart for different tissue types. Since IR-TFE is exclusively dependent on T1 tissue differences, WHAT and other IR-TFE sequences could be combined with T2*/phaseweighted gradient echo images for evaluating different tissue characteristics such as iron content [Pitt 2010]. Pathophysiology of cortical lesions and their evolution with disease progression is not yet fully understood. Thus MRI with a combination of contrast mechanisms could play an important role as predictor for MS disease progression and/or monitoring treatment outcomes and is a valuable step forward in expanding MRI MS research. 41

56 Chapter 4: Other Attenuated Sequences Introduction Although white matter attenuated IR-TFE sequences have shown good promise for detecting cortical lesions, other contrast options are available with IR-TFE and can also be explored. Attenuating gray matter, cortical lesion, or CSF signal may all assist in cortical lesion detection by selectively changing the partial voluming effects of the tissues adjacent to the one being attenuated. In addition, a double inversion recovery turbo field echo (DIR-TFE) sequence could be developed for use at 7T, nulling both WM and CSF signals and achieving similar contrast as DIR-TSE, though with smaller SAR requirements. These other contrast options will be explored further in this chapter. The T1 and PD values used in this chapter are those listed in Table 4.1. Because of the short echo times used in these sequences, T2* effects are considered negligible and are not included in the simulations. Gray Matter Attenuation Gray matter attenuated (GRAT) IR-TFE is analyzed by solving Eq. (4.1), repeated below, for TS in terms of TI when gray matter signal is at its minimum. Relevant sequence parameters for the (4.1) 42

57 tissue signal computations are TR = 3.0 ms, TE = 1.19 ms, TFE = 360, FA = 8, and perfect inversion due to using an adiabatic RF pulse. This results in a similar TS/TI curve as in WHAT (Figure 4.1A), although the TI GRAT becomes larger than TI WHAT as TS increases (Figure 4.1B). When WM, CL, and CSF signal is calculated at the TS/TI GRAT pairs, WM and CL signals converge as TS gets longer. CSF signal intersects WM around 4600 ms and continues getting more hyperintense as TS increases. Signal analysis at TS = 4550 ms shows that the corresponding TI GRAT = 950 ms, and that there is little difference in WM and CSF signal. GM signal is hypointense and CL signal is midrange at this shot interval. The TS/TI GRAT pairs are shown in Figure 4.1A and the tissue signal calculations at those TS/TI pairs is shown in Figure 4.1B. Cortical Lesion Attenuation Repeating the process for cortical lesion signal attenuated (CLAT) IR-TFE shows that TI CLAT increases yet further for each TS compared to WHAT and GRAT. Relevant sequence parameters for the tissue signal computations are TR = 3.0 ms, TE = 1.19 ms, TFE = 360, FA = 8, and a 100% inversion from using an adiabatic inversion pulse. Looking at the tissue signal changes at each TS/TI CLAT pair show that white matter would be hyperintense and GM and CSF would be nearly isointense for TSs up to about 8000 ms. Beyond that point CSF signal continues to increase as GM signal reaches equilibrium. For a TS = 4550 ms, the corresponding TI CLAT would be 1200 ms. Figure 4.2 shows the TS/TI CLAT pairs (A) and the tissue signal calculations for each TS/TI CLAT pair (B). 43

58 (A) Figure 4.1: Gray matter attenuated IR-TFE. (A) TS/TI pairs producing minimal GM signal, and (B) tissue signal calculations using TS/TI pairs in (A). (C) Example image in healthy subject. (B) (C) (A) (B) (C) Figure 4.2: Cortical lesion attenuated IR-TFE. (A) TS/TI pairs producing minimal CL signal, and (B) tissue signal calculations using TS/TI pairs in (A). (C) Example image in healthy subject. Cerebrospinal Fluid Attenuation Repeating the process once more for CSF gives a true T1-weighted IR-TFE scan: WM signal is the brightest followed by GM, CL and finally CSF. Relevant sequence parameters for the tissue signal computations are TR = 3.0 ms, TE = 1.19 ms, TFE = 360, FA = 8, and 100% inversion due to 44

59 the use of an adiabatic inversion pulse. TI CSFAT continues to increase for each TS and there is good tissue contrast between all tissues. For a TS = 4550 ms, the corresponding TI CSFAT would be 1500 ms. Figure 4.3 shows the TS/TI CSFAT pairs as well as the signal changes for WM, GM, CL, and CSF. (A) (B) (C) Figure 4.3: CSF attenuated IR-TFE. (A) TS/TI pairs producing minimal CSF signal, and (C) tissue signal calculations using TS/TI pairs in (A). (C) Example image in healthy subject. Double Inversion Recovery Turbo Field Echo Selecting both white matter and CSF to be nulled from the resulting image can be accomplished by using two inversion pulses and two TIs in the sequence, each determined precisely to remove white matter (TI1) and CSF (TI2) signal from the image. A TFE acquisition is preferred at higher field strengths because of the lower SAR requirements compared to a TSE acquisition will prevent excessive power deposition and tissue heating. DIR-TFE is developed as en extension of the Deichmann equations described in Chapter 3. To account for the extra inversion pulse, A 4 and B 4 have been added to Eqs. (4.2) and (4.3) such that: 45

60 (4.6) where α is the flip angle and M 1 is defined as: (4.7) with, ( ) (4.8A) ( ) ( ) ( ) * + (4.8B) Analyzed for TS = 4550 ms, TI1 and TI2 are calculated to be 1710 ms and TI2 = 490 ms, respectively. Increasing TS corresponds to an increase in GM and CL signal while WM and CSF are nulled, as desired. Figure 4.4A shows how TS and TI1/TI2 are related and Figure 4.4B demonstrates that this sequence maintains GM and CL signals while nulling WM and CSF signals for a range of sequence TS. GM-CL contrast, however, remains small. 46

61 (A) (B) Figure 4.4: White matter and CSF attenuated DIR-TFE. (A) TS/TI1/TI2 pairs producing minimal WM and CSF signal, and (B) tissue signal calculations using TS/TI1/TI2 pairs in (A). It is also noteworthy to account for the effects of the acquisition parameters on the performance of the DIR-TFE sequence. As Figure 4.5 shows, at shorter TSs, the CSF null point is sensitive to TR, TFE, and the flip angle. The white matter nulling TI is sensitive to the T1 value included in the simulation. The echo time has little effect on the sequence due to it being quite short, and thus negligible. Using a longer TS reduces the dependency on these parameters. Figure 4.6 shows that increasing the TS to 10,000 ms indeed makes the sequence more stable to a variety of input parameters. 47

62 Figure 4.5: Effects of changing DIR-TFE acquisition parameters on the performance of the sequence in nulling both white matter and cerebrospinal fluid. TS = 4550 ms. 48

63 Figure 4.6: Effects of changing DIR-TFE acquisition parameters on the performance of the sequence in nulling both white matter and cerebrospinal fluid. TS = 10,000 ms. Contrast Comparisons Analyzing the calculated signals for each alternate IR-TFE contrast (Figure 4.7) shows that the WHAT sequence has the highest average signal levels over all tissues at a TS of 4550 ms, followed by CSFAT, CLAT, GRAT, and finally DIR-TFE. The GRAT sequence shows relatively small tissue differentiation, especially for WM-CL. The lack of tissue signal contrast could cause 49

64 problems with identifying cortical lesion boundaries, especially Type 1 lesions. Gray matter attenuation also shows potential as a quick method for identifying persistent T1 "black hole" lesions without the need for continued follow-up scans. Chapter 7 discusses this further. CLAT has excellent WM signal, although GM and CSF signals are small and nearly isointense, making tissue boundary definitions difficult to interpret. CSFAT also has excellent WM signal and the GM and CL signals are decent. As a T1-weighted sequence the lesions would be darker than the surrounding tissues. The appearance of a typical Type 1 lesion is shown in Figure 4.8. Despite relatively good overall tissue differentiation, WM-CL and GM-CL contrasts are similar in three of the sequences: WHAT, CLAT, and CSFAT, and much smaller in GRAT. GM-CL contrast is nearly constant in each single IR-TFE variation, suggesting that the image resolution and WM / CSF signal levels would be the primary criterion for ranking the usefulness of each sequence for cortical lesion detection. As with DIR-TSE currently reported in literature [e.g. Calabrese 2008/2009; Nelson 2007/2008, Wright 2007, Madelin 2008], that despite the isolation of the cortical gray matter strip and the hyperintensities of cortical lesions, the DIR-TFE sequence presented herein also suffers from low tissue signal and even lower tissue contrast than can be achieved with the TSE acquisition. TFE is preferred for ultra-high field applications due to the lower SAR levels than with a comparable TSE acquisition. Another factor in the utility of DIR sequences is the relative T1 vs. T2 signal weighting. Shorter acquisition echo times will lead to more T1 weighting while longer TEs will increase T2 effects. De Graaf, et al. [2012] used a strong T2 weighting and minimized T1 effects 50

65 by using a specific magnetization preparation pulse sequence. A comparison of short vs. long TEs regarding lesion detection was not a goal of this study, though once DIR-TFE is refined for ultra-high field use, could be an interesting endeavor. Shortening TS to 3700 ms (Figure 4.5E-F) does not confer any advantages for any of the above sequences as the calculated tissue signals are on average over 28% smaller, and the WM-CL and GM-CL contrasts are diminished as well. Increasing TS to 8000 ms results in greater tissue signal and contrast, though also at the cost of longer scan times which may not be advisable for patients with compromised motor abilities. In addition, increasing the image resolution will cause greater loss of tissue boundary fidelity as noise levels are increased. Likewise, reducing image resolution hinders detection of small cortical lesions due to partial voluming effects. For these reasons, a midrange 0.5x0.5x1.0 mm 3 image resolution is recommended as the best compromise for cortical lesion imaging. 51

66 (A) (B) (C) (D) (E) (F) Figure 4.7: (A) Calculated signal for each tissue type for each attenuated IR-TFE sequence, including DIR-TFE. TS = 8000 ms. (B) Calculated tissue contrast between white matter-cortical lesions and gray matter-cortical lesions. TS = 8000 ms. (C) Same as (A) but calculated for TS = 4550 ms. (D) Same as (B) but calculated for TS = 4550 ms. (E) Same as (A) but calculated for TS = 3700 ms. (F) Same as (B) but calculated for TS = 3700 ms. 52

67 Figure 4.8: Typical appearance of a Type 1 cortical lesion in various IR-TFE contrast options. 53

68 Chapter 5: Contrast versus Resolution in Cortical Lesion Imaging Summary of Logistic Regression Logistic regression is a statistical tool that enables the user to analyze the effect of well-defined numerical or categorical input parameters on a binary response variable (output) in the form of a probabilistic distribution ranging from 0 to 1. Logistic regression has a characteristic curve, the so-called "S-curve", and is used to model the probability of a data point's existence within the two output states (see Figure 5.1). Figure 5.1: Logistic regression and the S-curve Logistic regression represents the log odds of a particular outcome using what is referred to as a 54

69 logit function. A general linear model (GLM) is then used to determine the coefficients that describe the model response. ( ) ( ) ( ) (5.1) where: E(Y) = predicted values of Y, the response variable, π(x i ) = probability of the desired outcome given the i-th input parameter, β i = regression coefficient of the i-th input parameter, and x i = independent input parameters that may contribute to the response C. Renil Zachariah [Thesis 2011] developed the logistic regression model to describe cortical lesion detection for two sequences WHAT and susceptibility weighted imaging (SWI) based on the input parameters of lesion size, lesion contrast to adjacent normal appearing gray matter, in-plane resolution, slice thickness, lesion type, acquisition sequence, and reader bias. Zachariah's work focused on the effect and interactions of lesion size and acquisition sequence on the probability of lesion detection. In this chapter, we will more fully explore the effects of tissue contrast and resolution. Probability of Lesion Detection The model for the probability of lesion detection given certain input parameters was based off two MR sequences: WHAT-TFE and SWI. Both sequences were evaluated with two different image resolutions and exhibited different GM-CL tissue contrast. These parameters are listed in Table 5.1. Other parameters that likely play a role in detection are lesion size and lesion type 55

70 as identified on MR images and histological sections. Table 5.1: Probability of lesion detection is based on several factors: The resolution and contrast information for the 4 tested sequences are listed. Derived from [Zachariah 2011, Table 8] Sequence Voxel Size, mm 3 GM-CL CNR WHAT Medium Res SWI High Res WHAT Low Res SWI Medium Res Zachariah [2011] collected MS brain specimens from a multiple sclerosis brain bank. The specimens were fixed in formalin and imaged using a 7 Tesla MRI system using the four sequences briefly described in Table 5.1 (more specific acquisition information can be obtained from Zachariah's thesis [Table 1, 2011]). After MR imaging the specimens were sliced to closely match the orientation of the MR images and stained using CD68, myelin-based protein (MBP) and Perl. Lesions were identified on the stained histological slices and categorized into their respective cortical lesion types. Two readers were recruited to count and classify the lesions seen in the MR images. With the histological sections serving as the ground truth for lesion location, knowing the seen/not seen dynamics of the counts along with the acquisition sequence differences may provide insight on the most important imaging parameters for in vivo cortical lesion detection. Analyzing the lesion data first gives an indication of what lesions could and could not be seen by 56

71 either and both readers. It is evident from Figure 5.2 that the Type 1 lesions identified on the histological sections consisted of both true Type 1 lesions and included any lesions identified in MRI as Type 1-3, and likely Type 1-4 and Type as well. MR and histological images both showed Type 2, Type 3, and Type 4 lesions fairly equally. Figure 5.3 shows that, overall, the readers missed more of the smaller lesions than the larger ones, which is not surprising. There does not appear to be a significant difference in the readers' abilities in this aspect either. It is notable though that both readers missed several larger lesions. It could be speculated that the image contrast in those areas may not have been ideal, making the lesions difficult to decipher from the adjacent normal appearing tissue. Figure 5.2: Type Classifications of lesions seen independently on MR and histological images 57

72 Figure 5.3: The image readers missed more smaller lesions than larger ones, and the bias between readers is negligible. As measured on the histological sections, there are more small lesions than there are large (see Figure 5.4). High image resolution and good tissue contrast are needed to differentiate these lesions. 58

73 Figure 5.4: Histogram of lesion size as measured in histology sections (from [Zachariah 2011, Figure 5]). Developing the logistic regression model for cortical lesion detection initially involves identifying the dependent response variable and the independent input parameters that may play a role in the resulting binominal response. All statistical analysis was completed using R, a freeware statistics package available online [R Development 2008]. There are four options for the independent response variable. There were two readers that read the image data and marked any visible lesions, and we can thus look at the seen/not seen tabulations of reader #1, reader #2, both readers together (logical AND), and either reader separately (logical OR). We will define the potential input parameters that may affect lesion depiction as lesion size, lesion CNR to adjacent gay matter, in-plane resolution, and slice thickness. Table 5.2 shows the regression coefficients of the logistic regression analysis for α =

74 Table 5.2: Significant regression coefficient p-values for lesion detection model (α = 0.05). Highlighted values are significant Reader #1 Reader #2 Both Readers Either Reader (Intercept, β 0 ) Lesion Size 8.74 x x x x 10-9 CL-GM CNR In plane resolution Slice thickness For a response of: Since the convergence of the seen/not seen response by both readers (the logical AND case) most likely represents the results by a broader range of readers, this response output will be used for the remainder of the chapter. Lesion size can be seen to be the most significant factor in the detection of cortical lesions, though slice thickness was a factor in lesion visibility when both readers were analyzed together. Tissue CNR and in-plane resolution effects are not significant in the logical AND case, though they were both factors in Reader #2's results. For deeper analysis, we should look into the effect of the interactions between these parameters. Table 5.3 summarizes the results. Lesion size maintains the only significance in the model, though its influence is decreased, and absent from the logical AND scenario. The coefficients themselves are listed in Table 5.4. Using these coefficients in the logit lesion detection model equation, Figure 5.5 shows that high resolution images improve lesion detection for both sequences and the medium resolution SWI outperforms the low resolution WHAT sequence. 60

75 Table 5.3: P-values of model including interaction terms. Highlighted values are significant (α = 0.05). Reader #1 Reader #2 Both Readers Either Reader (Intercept, β 0 ) Lesion Size CL-GM CNR In plane resolution Slice thickness Lesion size : CL-GM CNR Lesion size : In plane resolution Lesion size : Slice thickness For a response of: Table 5.4: Regression coefficients for model including interactions terms Both Readers (Intercept, β 0 ) Lesion Size 1.33 CL-GM CNR In plane resolution Slice thickness Lesion size : CL-GM CNR Lesion size : In plane resolution Lesion size : Slice thickness Smaller lesions are however best detected by using a medium resolution WHAT sequence, indicating that the contrast advantages of the WHAT sequence outperform the better image resolution obtainable with the SWI sequence. Increasing the image resolution has the greatest impact on improving the probability of lesion detection with the WHAT sequence and relatively little effect on the SWI sequence. Figure 5.5 indicates that larger lesions have a greater chance 61

76 of being identified with higher image resolution, while smaller lesions rely more heavily on tissue contrast differences. Figure 5.5: Logistic regression curves for model of lesion detection shows that contrast has the greatest effect the detection of smaller lesions and resolution has the greatest effect on larger lesions. Contrast and Resolution Now that the effects of contrast and image resolution in the detection of cortical lesions have been presented, it is necessary to quantify the changes that can be expected while using an isotropic voxel size (e.g., 0.5x0.5x0.5 mm 3 ) versus an anisotropic size (as was used in this model 62

77 development). Five WHAT IR-TFE sequences with two different in-plane resolutions and different slice thicknesses were tested on one volunteer diagnosed with multiple sclerosis. The volunteer was scanned on a 7T MR system (Philips Medical, Cleveland, OH) and informed consent was obtained. A 16-channel phased array head coil (NOVA Medical, Wilmington, MA) was used with SENSE encoding turned off. The first in-plane resolution tested was 0.5 x 0.5 mm 2 with slice thicknesses of 0.5 mm, 1.0, mm and 2.0 mm. For SNR and CNR comparisons, two sequences with an in-plane resolution of 0.4 x 0.4 mm 2 were tested with slice thicknesses of 0.8 mm and 1.6 mm, respectively. One ROI for white matter, gray matter, white matter lesion, cortical lesion, and cerebrospinal fluid was drawn manually and the mean signal and standard deviation was recorded. SNR was calculated by dividing the measured tissue signal by the standard deviation of the signal measured in the surrounding air. Tissue CNR was calculated by taking the absolute value of the difference of the tissue SNRs. As expected, the voxel size plays and important role in the tissue contrast and lesion detectability. A voxel size of 0.4x0.4x0.8 mm 2 had lower SNR than one with a slice thickness of 1.6 mm. Gray matter-cortical lesion CNR was reduced, however, both points lie within the measurement error fot he graph (± 2). Increasing the in-plane resolution to 0.5x0.5 mm 2, a slice thickness of 0.5 mm (making the voxel isotropic) results in the least amount of SNR being detected by the scanner. 63 As the slice

78 thickness is increased the SNR levels increase significantly almost tripling up to a slice thickness of 2.0 mm. Figure 5.6: Tissue signal as measured in sequences with two different in-plane resolutions and several slice thicknesses. In-plane resolutions used: 0.4x0.4 mm 2 (solid line, slice thickness 0.8 mm and 1.6 mm) and 0.5x0.5 mm 2 (dashed line, slice thickness 0.5 mm, 1.0 mm, and 2.0 mm). The ROIs used for the measurements are shown on the right. Compared with the 0.4x0.4 mm 2 cases, 0.5x0.5x0.5 mm 3 SNR is lower than the 0.4x0.4x0.8 mm 3 but higher than the 0.4x0.4x1.6 mm 3 case. The signal levels of a voxel 0.5x0.5x1.0 mm 3 are higher than the 0.4x0.4x1.6 case and the GM-CL contrast is also moderately higher. Conclusion Despite achieving better signal with the larger voxel sizes (and thicker slices) there is also a loss of signal fidelity in boundary areas that could compromise lesion detection, (see Figure 5.6), which makes larger slice thicknesses not particularly advantageous. On the other hand, voxels 64

79 that are too small (including small slice thickensses) are not able to render a sufficient amount of SNR for detecting or differentiating lesions and as such are also not recommended for this purpose. Figure 5.7: Comparing different slice thicknesses. Boundary areas are more difficult to interpret in images with larger slice thicknesses (circle). Smaller slice thicknesses lead to noisier images. In-plane resolution 0.5x0.5 mm 2, slice thickness (A) 0.5 mm, (B) 1.0 mm, and (C) 2.0 mm. Future Work This preliminary investigation involved only one MS patient and can only be used to draw minimal conclusions on the determination of the "best" image CNR/resolution combination for cortical lesion imaging. To get a better understanding of the effects on lesion identification, it is recommended that a larger study capturing image data with different resolutions be conducted. Lesions would then be identified in each data set and their location, type and size recorded. 65

80 Since multiple readers would be involved, the kappa statistic could be used to measure interreader variability. The "best" resolution would be one in which the readers had the best overall agreement. 66

81 Chapter 6: Reader Training for Cortical Lesion Detection Introduction Cortical lesions are small and difficult to differentiate from adjacent gray matter due to low tissue contrast. These qualities combined make it important to train potential readers to detect these lesions consistently in each reading. This chapter proposes a method for training that can be used with any new readers to help eliminate reader bias and work towards consistency in the results. Motivation Reader training initially involved giving the new reader the in vivo images that needed to be read and telling them the contrast changes in WHAT and structural information in SWI that could indicate the presence of a cortical lesion in the MR image. In an effort to analyze the effectiveness of this method, a new volunteer reader was recruited to read a series of PowerPoint files. Each file contained every slice from one acquisition sequence. Three sequences were tested: 2D SWI, 3D SWI, and WHAT. The small data set consisted of both MS patients and neurologically normal controls. The reader was then asked to mark any lesions in the PowerPoint files. Several PowerPoint files were repeated 4 to 5 times in an effort to determine intra-reader variability. 67

82 When the number of lesions per acquisition sequence was plotted against the order that the PowerPoint files were read, the later files were found to have more lesions than the earlier trials. It was found that with this method that not only was there a marked increase in the number of lesions that were identified by the new reader over time, the later image file contained images from the normal controls indicating that the reader was not able to perceptually distinguish cortical lesions from image artifacts and local contrast variations. This was visible in both the repeated PowerPoint files as well as all the files as a whole (Figure 6.1). These results indicated an urgent need for a systematic training method for new readers of cortical lesions that focused on identifying true lesions and being able to disregard local contrast variations that could lead to a high false positive rate. Figure 6.1: More "lesions" are identified over time for a new reader. (A) For all acquisition sequences. The neurologically normal controls were counted later than the MS patients, showing a very high number of false positives. (B) The new reader also unknowingly recounted several image files: the gradual increase in the number of lesions found over time indicates that a more systematic training method is needed. 68

83 Methods Since lesion locations can only be fully determined by direct comparisons between the MR images and histological slices, analyzing specimen images is the best way to acquaint new readers with the appearance and location of cortical lesions. Although in vivo and post mortem tissues have different relaxation parameters, it must be used as a surrogate since there is no ground truth for in vivo cortical lesions. The training method described in this chapter consists of 4 units, each with a fundamental goal for the reader to obtain. The content and goals of each unit are described below in paragraph form and followed by a summary list. Figures show slide examples from each unit. This new method was used to train 2 new readers in MS cortical lesion detection. Since the amount of time spent reading lesions is a big factor in developing expertise, the training process is spread out over several weeks to allow the new reader to read and reread images, as necessary to ensure proficiency. Unit 1 requires no response from the trainee nor does it require feedback. Unit 1 focuses on the background of why we are interested in cortical lesions, their histological classifications, their appearance in histology sections and corresponding MR images of those sections. The appearance of in vivo lesions is introduced using knowledge from specimen imaging as the ground truth for cortical lesion visualization. [Pitt 2010] 69

84 Unit 2 focuses on developing and refining the trainee's skills for detecting cortical lesions. It consists first of a series of MR images of MS specimens in 2 different resolutions and sequences. The reader is asked to mark the cortical lesions in the MR images using colored circles corresponding to each lesion type: e.g., red for Type 1 lesions, orange for Type 2 lesions, yellow for Type 3 lesions, and green for Type 4 lesions. After the prospective count is complete, it is followed by a retrospective count. This is done by showing the reader the corresponding histological slices with all lesions marked and requesting that the reader then find as many as possible on the blank MR images. Finally the reader is given the marked up MR images as a feedback mechanism for their initial prospective and retrospective reads. Unit 3 consists of a prospective count of in vivo MR images of MS patients using two sequences: WHAT and SWI. The reader is once again asked to mark the images prospectively for cortical lesions using the same color-coded circles as in unit 2. Once the prospective count is complete, the trainee is given feedback by looking at the same set of images, though the cortical lesions have been previously marked by expert readers. Finally, unit 4 reinforces in vivo cortical lesion detection by using the complimentary information provided by the SWI and WHAT images for lesion visualization. The WHAT and SWI images are viewed side-by-side such that the reader can use any structural information from the SWI images and contrast from the WHAT images in making his/her determination. Also included in this step is the inclusion of adjacent slices, both above and below the target slice. These are included to assist the reader in differentiating blood vessels and for noting the appearance and 70

85 disappearance of gyri and sulci, which may be falsely identified as lesions without this additional slice information. If more than one reader is being trained simultaneously, a fifth unit can be incorporated into the training protocol. Convening a consensus read after each step allows the trainees to interact with each other as well as with more expert readers. This process assists in refining and standardizing their cortical lesion detection techniques. Unit 1 Content: Goal: Unit 2 Content: Goal: Unit 3 Content: Goal: Define the four types of cortical lesions Direct specimen versus histology examples with lesions precircled Comparison of types between specimen and in vivo lesions, precircled lesions are those identified previously by expert readers In vivo examples with lesions are those identified previously by expert readers pre-circled Introduce reader to cortical lesions and their appearance in MR images, using both specimen and in vivo examples Prospective counting of MR images of specimen block with different contrasts, image resolutions, and image orientations Retrospective counting of MR images of specimen block against marked histological slices Reader then given marked MR and histological images Introduce reader to finding lesions without aid. Feedback in the form of retrospective count to continue training eyes for lesion detection Prospective counting of MR images of in vivo examples with different contrasts and image resolutions. Feedback in the form of marked lesions from experienced readers for cross-checking on prospective count. Introduce in vivo images and the challenges in detecting lesions 71

86 without a gold standard to fall back on. Unit 4 Content: Goal: Two in vivo MR sequences side-by-side for comparison read. Reinforce lesion detection and introduce utility of using several sequences to make determination Figure 6.2: Example Unit 1 slides introducing new readers to cortical lesions detection using information from specimen and histology comparisons and their relationship to in vivo detection 72

87 (A) (B) (C) Figure 6.3: Example slides from Unit 2 showing (A) prospective MRI reading, (B) retrospective MRI reading, and (C) MRI reading feedback. 73

88 (A) (B) Figure 6.4: Example slides from Unit 3 showing (A) prospective in vivo MRI reading and (B) a retrospective reading with lesions identified that were previously marked by expert readers. (A) (B) Figure 6.5: Example slides from Unit 4 showing (A) in vivo SWI image and (B) in vivo WHAT image for a comparison read. 74

89 Results of New Training Method Two new readers were trained using the new method of reader training. Twenty-five PowerPoint files were given to the readers, each containing a set of in vivo MR images in one of two acquisition sequences: WHAT and SWI. To expedite the reading process, only the anterior third of the brain was included for analysis. The readers were instructed to circled any identified lesions using the same color-coded circles as in the training set: red for Type 1, orange for Type 2, yellow for Type 3, and green for Type 4. The totals for each lesions type were tallied for each reader, then compared to one another to determine the level of inter-reader agreement, calculated by κ (defined in Chapter 3). As of the writing of this document, this process is still being completed. The outcome of training new readers will be continually adjusted and improved as more and more readers are trained for cortical lesion detection. 75

90 Chapter 7: White Matter Lesion Differentiation Introduction Although white matter lesions (WML) are not indicative of the cognitive impairment of the patient, the pathological cause of the lesions is still being actively studied [Barkhof 2002]. In Chapter 2, the T1 and proton densities of white matter, gray matter, cortical lesions, white matter lesions and CSF were determined at 7T. It was noted in the chapter that the T1 distribution for white matter lesions appeared to be bimodal. This behavior could represent an artifact of the measurement method or it could indicate two subgroups of white matter lesions. In this chapter, a T1 weighted IR-TFE sequence is used to differentiate these lesions. Methods In Chapter 2, it was noted that the T1 distribution of white matter lesions was bimodal, with one peak around 2200 ms and the other roughly 4300 ms (Figure 7.1A). Using the T1 and PD values determined in Chapter 2: T1 NAWM = 1350 ms, T1 WML-low = 2200 ms, T1 WML-high = 4300 ms, PD NAWM = 0.68, PD WML-low = 0.88, and PD WML-high = 0.95, we simulated the signal response of normal appearing white matter (NAWM), short T1 white matter lesions, and long T1 white matter lesions (Figure 7.1B). The simulated tissue response shows that if TS = 4550 ms and TI between ms, images will show the long T1 WMLs brighter and short T1 WMLs darker than surrounding NAWM. 76

91 Four MS patients (2 RRMS, 2 SPMS) were scanned with IRB approval using Philips' 3T and 7T Achieva scanners to test the new sequence. The patients underwent a standard clinical 3T FLAIR (TR = ms, TI = 2800 ms, TE = 125 ms, TSE factor = 31), and three 7T MPRAGE sequences (Conventional T1: TS/TI = 4550/1800 ms, white matter attenuated (WHAT) TS/TI = 4550/500 ms, and WML T1-differentiating TS/TI = 4550/925ms). Other parameters for the MPRAGE sequences were: TR/TE = 4.1/1.6 ms, flip angle = 8, and TFE factor = 360. The images were then compared to one another and the appearance of white matter lesions was noted with each sequence. To assess whether the lesions with long T1 represent so-called persistent "black holes", previous T1-weighted images from 1 year ago were compared with the current T1-weighted images and the corresponding slice of the WML T1-differentiating sequence. Figure 7.1: (A) Measured T1s in white matter lesions show a broad distribution with two main peaks occuring around 2200 ms and 4300 ms. (B) Plotting the tissue signal response for both white matter lesion T1s along with normal appearing white matter shows that there is a short range of TI at which the low T1 WML signal is dark and the high T1 WML signal is bright compared to normal WM. 77

92 Figure 7.2: Distribution of proton density values as measured in 39 white matter lesions. Figure 7.3: (A) 3T FLAIR, (B) 7T WHAT, (C) 7T T1-weighted MPRAGE, and (D) 7T MIX sequences showing that white matter lesion T1 contrast is variable. A persistent black hole (bottom) and a less severe lesion (top) are highlighted. 78

93 Results The T1-differentiating sequence could indeed separate WM lesions: one group being brighter and the other being darker than the surrounding white matter tissue. The two WML contrasts were observed in the patients (an example is shown in Figure 7.2D). The 3T FLAIR sequence showed all WMLs evenly hyperintense (Figure 7.2A). The 7T WHAT scan also showed all WMLs evenly hyperintense, with some slightly brighter than others (Figure 7.2B). The 7T T1-W sequence showed all lesions as well, though some were black and some were gray compared to adjacent NAWM (Figure 7.2C). Our preliminary results indicate that bright, "long T1" WML in the T1-differentiating sequence are more prevalent in progressive patients rather than those who were more recently diagnosed, even though the total lesion activity is lower. To determine the applicability of the WML T1-differentiating IR-TFE sequence, 1 year old T1- weighted images were compared with the current T1-weighted images and the images produced by the T1-differentiating sequence. An example of the lesions' change over time is shown in Figure 7.3. The bright lesions in the new sequence tend to be the darker and more established in the normal T1-weighted images from the previous year. Discussion Demyelination and axonal loss are considered to be the two leading causes of white matter lesion contrast differences in MRI [Van Walderveen, 1998]. An IR-TFE sequence with TS/TI = 4550 ms/925 ms demonstrates that white matter lesions can be separated into at least two groups depending on the severity of the focal tissue damage. More established lesions with 79

94 greater demyelination and axonal loss will have longer T1, whereas lesions with less severe damage have shorter T1s. This may be observable at lower field strengths, however T1s are longer and thus better differentiated at 7T. T2 weighted sequences such as FLAIR cannot differentiate white matter lesions in this manner. Initial analysis of lesion appearances over the course of one year suggest that the new WML T1-differentiating IR-TFE sequence could be used to prospectively identify persistent black holes, though a larger longitudinal study would need to be conducted to confirm this hypothesis. (A) (B) (C) (D) (E) Figure 7.4: Comparing the white matter lesion T1-differentiating sequence with a 2011 T1-TFE sequence and the previous (2010) year's T1-TFE sequence. The bright lesion in the WML T1- diff image (A, red arrow) is a lesion that has been present at least one year. The two dark lesions in the WML T1-diff sequence (A, yellow arrows) are less established and are much less easier to decipher in the current and previous year's T1-TFE images. Clinical 3T T1w spin echo images are included for comparison. (A) 7T WML T1-diff 2011, (B) 7T T1-TFE post Gd 2011, (C) 7T T1-TFE post Gd 2010, (D) 3T T1-SE pre-gd 2011, and (E) 3T T1-SE post Gd Conclusion 80