Evaluation of Injury and Repair in Multiple Sclerosis Using Advanced MRI Methods
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2023-01-16
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Abstract
Multiple sclerosis (MS) is an inflammatory demyelinating and neurodegenerating disease of the central nervous system impacting more than 2.8 million people worldwide. Many of the people experience paramount disability after 10-15 years of disease onset. Optimal management requires accurate measurement of tissue pathology, including the likelihood of repair in lesions. However, there is no established marker of lesion severity in vivo. This project aimed to develop methods to characterize tissue injury and repair as seen in focal lesions based on brain magnetic resonance imaging (MRI) of relapsing-remitting MS (RRMS). The focus was on image processing techniques ranging from development, validation, to application. Initially, based on histology-informed MRI of postmortem MS brains, I conducted texture analysis using an optimized method known as gray level co-occurrence matrix (GLCM) and compared texture analysis to advanced MRI measures using machine learning models for classifying MS pathology, including de- and re-myelinated lesions. Based on the selected MRI measures, I then developed a percentile approach for characterizing MS lesion severity in clinical MRI, and for assessing lesion recovery in clinical trial MS participants. Overall, brain MRI texture measures performed the best in differentiating de- and re-myelination. These measures characterized 2 extreme types of MS lesions on de- and re-myelination, which differentiated men from women, and detected significant recovery in acute MS lesions with treatment. Collectively, advanced texture analysis in clinical MRI is promising for characterizing lesion injury and repair in MS. This ability is critical for improved evaluation of both disease activity and treatment response for MS participants.
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Hosseinpour, Z. (2023). Evaluation of injury and repair in multiple sclerosis using advanced MRI methods (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.