Advanced MRI methods for probing disease severity and functional decline in multiple sclerosis

Oladosu, Olayinka Adeoluwa

Advanced MRI methods for probing disease severity and functional decline in multiple sclerosis

dc.contributor.advisor	Zhang, Yunyan
dc.contributor.author	Oladosu, Olayinka Adeoluwa
dc.contributor.committeemember	Joshi, Manish
dc.contributor.committeemember	Dunn, Jeffrey Frank
dc.contributor.committeemember	Frayne, Richard
dc.contributor.committeemember	Le, Lawrence Trong-Huan
dc.date.accessioned	2023-12-14T22:03:35Z
dc.date.available	2023-12-14T22:03:35Z
dc.date.issued	2023-12-14
dc.description.abstract	Multiple sclerosis (MS) is a chronic and severe disease of the central nervous system characterized by complex pathology including inflammatory demyelination and neurodegeneration. MS impacts >2.8 million people worldwide, with most starting with a relapsing-remitting form (RRMS) in young adulthood, and many of them worsening to a secondary-progressive course (SPMS) despite treatment. So, there is a clear need for improved disease characterization. MRI is an ideal tool for non-invasive assessment of MS pathology, but there is still no established measure of disease activity and functional consequences. This project aims to overcome the challenge by developing novel imaging measures based on brain diffusion MRI and phase congruency texture analysis of conventional MRI. Through advanced modeling and analysis of clinically feasible brain MRI, this thesis investigates whether and how the derived measures differentiate MS pathology types and disease severity and predict functional outcomes in MS. The overall process has led to important technical innovations in several aspects. These include: innovative modeling of simple diffusion acquisitions to generate high angular resolution diffusion imaging (HARDI) measures; new optimization and harmonization techniques for diffusion MRI; innovative neural network models to create new diffusion data for comprehensive HARDI modeling; and novel methods and a graphic user interface for optimizing phase congruency analyses. Assisted by different machine learning methods, collective findings show that advanced measures from both diffusion MRI and phase congruency are highly sensitive to subtle differences in MS pathology, which differentiate disease severity between RRMS and SPMS through multi-dimensional analyses including chronic active lesions, and predict functional outcomes especially in physical and neurocognitive domains. These results are clinically translational and the new measures and techniques can help improve the evaluation and management of both MS and similar diseases.
dc.identifier.citation	Oladosu, O. A. (2023). Advanced MRI methods for probing disease severity and functional decline in multiple sclerosis (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/117738
dc.identifier.uri	https://doi.org/10.11575/PRISM/42581
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Multiple Sclerosis
dc.subject	Diffusion MRI
dc.subject	Texture Analysis
dc.subject.classification	Neuroscience
dc.subject.classification	Radiology
dc.title	Advanced MRI methods for probing disease severity and functional decline in multiple sclerosis
dc.type	doctoral thesis
thesis.degree.discipline	Medicine – Neuroscience
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.