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Doctoral Student Supervision
Dissertations completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest dissertations.
Investigating biomarkers of clinical progression in multiple sclerosis using magnetic resonance imaging, blood biomarkers, and machine learning (2026)
Multiple sclerosis (MS) is a chronic, inflammatory, and demyelinating disease of the central nervous system characterized by heterogeneous clinical presentations and variable progression. Sensitive biomarkers capable of detecting subtle tissue damage and predicting disease progression are needed to improve clinical management and therapeutic development. This thesis examined advanced MRI and blood biomarkers to better characterize tissue integrity, clinical progression, and predictors of disability in MS across spinal cord and brain imaging studies and multimodal prediction algorithms.The first research chapter investigated spinal cord myelin integrity using myelin water imaging (MWI) in individuals with relapsing-remitting and progressive MS. Lower mean myelin water fraction and greater perilesional myelin heterogeneity were associated with worsening disability over five years, suggesting that tissue surrounding spinal cord lesions is particularly vulnerable to ongoing degeneration.The second research chapter evaluated two definitions of clinical progression in the CanProCo cohort and found that a composite definition incorporating the Expanded Disability Status Scale (EDSS), processing speed, hand dexterity, and ambulation tests was more sensitive than EDSS alone in detecting early progression.The third research chapter demonstrated that diffusion tensor imaging (DTI) and MWI metrics can differentiate people who clinically progress from those who remain clinically stable over two years, with baseline and longitudinal changes in axonal integrity and myelin content corresponding to clinical worsening or stability.In the final research chapter, MRI, blood protein, and clinical data were integrated using an ensemble logistic regression model to predict 2-year progression, showing that multimodal integration significantly improved predictive performance over models that only integrated MRI or blood biomarker data with clinical data.The findings highlight the value of advanced MRI and molecular biomarkers to capture subtle disease processes and enhance early detection of clinical progression in MS, contributing to the development of personalized prognostic tools and improved monitoring of therapeutic efficacy.
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Characterizing white matter: adventures with quantitative magnetic resonance imaging (2025)
White matter in the central nervous system alters throughout the healthy lifespan as well as in disease. It is important to have non-invasive methods of characterizing white matter, such as through magnetic resonance imaging (MRI), so that changes to white matter in disease can be better understood. White matter is complex, and different quantitative MRI measures are sensitive to different aspects of its microstructure. In this thesis, I explored five MRI measures to characterize white matter: myelin water fraction (MWF), fractional anisotropy (FA), microscopic fractional anisotropy (µFA), a measure of tissue heterogeneity (CMD), and the magnetization transfer (MT) ratio.I first investigated the relationship between MWF (from myelin water imaging) and FA, µFA and CMD (from tensor-valued diffusion imaging) in twenty-five healthy volunteers through correlation analysis and tract profiling, and created atlases of these measures. I also characterized the measures in five example cases of multiple sclerosis (MS), to explore how they varied in pathology. I determined from this initial investigation that MWF, µFA and CMD would be useful to explore both healthy and pathological tissue.Next, I developed a data-driven tissue classification framework to classify tissue using only quantitative MRI measures and no spatial input, called Clustering for Anatomical Quantification and Evaluation (CAQE). In this framework, quantitative MRI measures from multiple healthy subjects were used to derive tissue classifications with specific microstructural signatures. I clustered MWF, µFA and CMD data from twenty-five healthy controls to create a classification scheme where clusters placed themselves into anatomically similar locations in healthy people even without spatial input. I applied the classification scheme to twenty-five people with MS and found regions of changes in white matter tissue classifications that were correlated with cognitive ability. Finally, I developed MT imaging for characterizing white matter on a new point-of-care ultra-low field 64 mT scanner, to enable myelin-sensitive monitoring in demyelinating diseases such as MS. I did this using an on-resonance approach with steady state free precession imaging, validated the approach in phantoms, assessed its reproducibility, and demonstrated it in a person with MS.
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Advances in quantitative magnetic resonance imaging of myelin (2023)
Myelin water imaging (MWI) is a quantitative magnetic resonance imaging (MRI) technique generally regarded as the most rigorous approach for non-invasive, in-vivo measurement of myelin content.Although MWI has proven valuable for the study of development, aging, disease, injury, genetics, and fundamental biology in the central nervous system, the power of its insights hinge on accurate characterization of normative values. To that end, we used MWI data from 100 adults (age 20-78) to create an optimized, unbiased myelin atlas and characterize how myelin content changes throughout the adult life span; an invaluable, openly available reference for future studies.In practice, lengthy acquisition times have limited the utility of MWI and often lead to alternative approaches being used to acquire surrogates for MWI. To compare the traditional multi-echo T2 relaxation and alternative steady-state MWI approaches, we created multivariate brain and spinal cord atlases and found an approximately linear relationship between myelin estimates, which broke down in the presence of unique relaxation times (spinal cord, tissue affected by disease pathology). This work will improve retrospective interpretation, and guide future design, of MWI studies.Next, we addressed lengthy MWI acquisition times using conventional compressed sensing before ultimately introducing the Constrained, Adaptive, Low-dimensional, Intrinsically Precise Reconstruction (CALIPR) framework. Drastically improved reconstruction performance allowed whole-brain MWI to be acquired using a previously unattainable sequence (fully sampled acquisition time 2h:57m:20s) in only 7m:26s with CALIPR (acceleration factor 23.9, 4.2% of the dataset). Reproducibility experiments demonstrated excellent precision, and CALIPR provided markedly increased sensitivity to demyelinating disease pathology (the hallmark application for myelin imaging). We implemented CALIPR for MWI of brain and spinal cord, and for two of the three largest MRI manufacturers. The CALIPR framework provides increased acceleration, precision, and sensitivity for MWI, and could be similarly transformative for other quantitative MRI applications.Finally, we implemented CALIPR on an ultra-low field (0.064T) portable, point-of-care MRI scanner to acquire accurate, quantitative T2 mapping data in 10 minutes. In combination with the accessible, low-cost imaging enabled by this platform, this work could help revolutionize the care of neurological disorders by enabling frequent, quantitative assessment of subtle tissue changes.
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Investigating cognitive impairment in multiple sclerosis using myelin water imaging (2021)
Cognitive impairment is a common symptom in multiple sclerosis (MS) that presents in up to 70% of patients. Cognitive symptoms in MS typically manifest as deficits in attention, memory and/or processing speed, with processing speed being most frequently affected. MS-related cognitive impairment represents a major burden as it can significantly lower quality of life and is a main contributor to unemployment.Conventional magnetic resonance imaging (MRI) with T1-weighted and T2-weighted contrast is the mainstay of MS diagnosis and monitoring. However, conventional MRI is limited in that it is qualitative, lacks biological specificity and correlates poorly with clinical and cognitive status. In contrast, myelin water imaging (MWI) is an advanced MRI technique that measures the signal from water in the myelin bilayers, providing a quantitative myelin-specific measurement (myelin water fraction, MWF). The aim of this thesis is to investigate the relationship between myelin damage and cognitive performance in MS using MWI.First, we demonstrate that MWF in normal appearing white matter (NAWM) was significantly associated with processing speed performance in MS in 3 a priori selected white matter tracts associated with cognition. Next, we show that the relationship between NAWM MWF and cognitive performance extends to additional cognitive domains in a larger cohort. Finally, rather than selecting brain regions a priori, we employed an assumption-free data driven approach using permutation testing to show that myelin damage extent and anatomical location is unique to the cognitive domain being investigated, with greater myelin damage in these regions in cognitively impaired versus cognitively preserved patients. Further, we demonstrate that the severity and spatial extent of myelin damage in cognitive domain-specific white matter regions is strongly associated with cognitive performance.This thesis demonstrates that there is a strong relationship between the location and severity of myelin damage and MS-related cognitive impairment. As the treatment landscape for MS moves toward the development of remyelination therapies, understanding the role of myelin pathology in cognitive symptoms is critical for translating findings to clinical trials. These results also highlight the promise of MWI for monitoring myelin changes and their relationship to cognitive worsening and improvement when investigating new therapies.
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Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Assessing processing speed impairments in radiologically isolated syndrome and multiple sclerosis with advanced brain MRI measures of myelin (2024)
Multiple sclerosis (MS) is a demyelinating disease of the central nervous system that results in characteristic discrete lesions visible on conventional magnetic resonance imaging (MRI), and diffuse damage to the surrounding normal appearing white matter (NAWM). Clinically, MS presents with a wide range of symptoms which often include cognitive impairment (CI), of which processing speed is commonly implicated. Improving our understanding of the biological basis of MS-related CI and future cognitive performance may help to uncover relevant biomarkers and provide opportunities to target these symptoms at earlier stages. People with radiologically isolated syndrome (RIS) can convert to MS and can present with CI patterns similar to MS. Thus, RIS represents a unique opportunity to explore the evolution of CI and associations with early disease biomarkers. Myelin water imaging (MWI) is a histologically validated advanced MRI technique capable of measuring myelin content. From MWI scans, the mean myelin water fraction (MWF) and standard deviation (SD) within a region of interest can be extracted, and the myelin heterogeneity index (MHI) can be calculated (MHI = SD/mean MWF). Here, we used MWI as a biomarker to explore myelin differences in NAWM of people with RIS compared to neurologically healthy controls and investigate the relationship between MWI metrics and processing speed. We detected NAWM myelin damage in RIS, particularly in the corpus callosum, and found that increased NAWM myelin damage, particularly in the cingulum, was associated with slower processing speed in RIS. Additionally, we used MWI to explore longitudinal changes in processing speed in people with MS. In people with progressive MS, lower baseline NAWM MHI, indicative of less myelin damage, was associated with a greater decline in processing speed over time. Overall, these results demonstrate the utility of MWI as a biomarker of myelin specific pathology relevant to clinical symptoms and highlights the complexity of MS-related CI across the spectrum of MS.
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Subspace informed compressed sensing reconstruction techniques for diffusion tensor imaging (2024)
Magnetic resonance imaging (MRI) is a technique that allows non-invasive investigation of the properties of tissue through manipulation of the properties of the hyperfine splitting of the hydrogen atom in a magnetic field. Diffusion MRI is a variant of MRI that sensitises the imaging process to diffusion of water molecules in tissue through the use of strong gradient magnetic fields to dephase the signal of any protons undergoing diffusion, leading to a reduction in signal magnitude in that voxel.There is increasing use of iterative reconstruction algorithms of MRI data using the principles of compressed sensing (CS) that rely on the sparsity of information in the image domain to improve reconstructions of undersampled MRI data. Further refinements to the CS algorithm not only rely on the sparsity of single MRI images, but take advantage of low rank properties of some MRI data along a further dimension to permit even higher undersampling.Diffusion tensor imaging (DTI) acquires many sampled volumes of the same field of view encoded along different diffusion vectors. A reconstruction using low rank properties in the diffusion dimension could improve DTI image reconstructions. However, the random phase of diffusion imaging due to subject motion during the diffusion sensitising gradients of the acquisition destroys the low-rank nature of diffusion datasets. Phase correction of a randomly undersampled k-space is impossible, but the application of a phase correction factor to the sensitivity maps used in multicoil acquisitions can correct the phase error to restore low rank properties to the data.Alternatively, one can use a similar technique to perform a secondary reconstruction of diffusion tensor image data, as the magnitude images of a diffusion scan already discard all phase information, which includes the motion corruption. This secondary reconstruction efficiently denoises and simultaneously interpolates the images to a higher resolution by leveraging a projection into a low-rank subspace that shares information across different diffusion shots. This type of denoising performs on par with other contemporary denoising algorithms while reducing upsampling blurring through the use of zero-padded interpolation.
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Investigating how demyelination is reflected using advanced magnetic resonance imaging in Susac syndrome and multiple sclerosis (2021)
Conventional magnetic resonance imaging (MRI) is a useful qualitative clinical tool for diagnosing and monitoring neurodegenerative diseases. However, it is not capable of measuring diffuse and microstructural changes in normal-appearing white matter (NAWM). Advanced MRI can quantitatively describe the microenvironment in NAWM. Myelin water imaging (MWI) is a technique that uses multi-component T2 relaxation to quantify the amount of myelin present within a region of interest. The resulting measurement is termed the myelin water fraction (MWF) and has been shown to correlate with histological myelin measurements. The standard deviation of the MWF is thought to represent the heterogeneity of myelin within a region of interest. By dividing the standard deviation of the MWF by the mean MWF, the coefficient of variation is calculated. Known as the myelin heterogeneity index (MHI), this is thought to be the most sensitive measure derived from MWI since it captures both myelin damage and variability. Diffusion basis spectrum imaging (DBSI) is another quantitative MRI technique that detects the diffusion of water. DBSI separates the isotropic and anisotropic diffusion components within each voxel and uses the degree and direction of water molecule movements to describe the microstructure. Importantly to this study, the measure radial diffusivity is thought to negatively correlate with myelin content. I apply MWI and DBSI to Susac syndrome (SuS), a rare demyelinating disease often mistaken for the more common demyelinating disease multiple sclerosis (MS), and find that both advanced MRI techniques describe diffuse myelin damage seen in SuS, but not in MS or healthy controls. This suggests a newly identified pathology of SuS. I next focused on characterizing the 3 aforementioned MWI metrics (mean, standard deviation or MHI) in MS. The measures differentiated MS from healthy controls and correlated with disability due to MS. Different stages of the disease were better characterized by different metrics, depending on the amount, uniformity, and extent of myelin damage. Lastly, MWI discerned longitudinal changes in MS over 2 years. This thesis shows that advanced MRI techniques are able to measure microstructural damage not detectable by clinical MRI and furthers the understanding of pathologies of two demyelinating diseases.
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Application of myelin water imaging to detect diffuse white matter damage in multiple sclerosis (2019)
While conventional magnetic resonance imaging (MRI) is qualitatively useful for the diagnosis and clinical management of multiple sclerosis (MS), it has limitations in terms of detecting specific myelin loss and diffuse damage in the normal-appearing white matter. A non-conventional MRI technique, called myelin water imaging (MWI) can be achieved using multicomponent T₂ relaxation to provide a quantitative in vivo measurement of myelin, termed myelin water fraction (MWF). MWF has been proposed as a candidate MR marker of myelin content in the central nervous system. In this study, I present the application of MWI to gain a deeper insight into the diffuse white matter damage in MS. First, we found lower myelin content and higher myelin heterogeneity in brain and cervical spinal cord, as well as correlations between myelin heterogeneity and clinical disability in cervical spinal cord in progressive MS compared to healthy controls. We also found myelin abnormalities in the regional and global white matter in progressive solitary sclerosis, which has recently been proposed as a potential variant of MS. Finally, we demonstrated good global white matter MWF reproducibility (coefficient of variation = 2.77 %; Pearson’s r = 0.91, p 0.001; mean bias = 0.002) between two sites using different scanner vendors. Together, our findings support that MWI is a useful quantitative imaging technique that may be used to improve our understanding of the pathological processes in MS. Furthermore, inter-vendor reproducibility takes MWI a step closer to routine use for multicenter studies and clinical applications.
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