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Fixel based analysis of relapsing remitting multiple sclerosis subjects Lesack, Nikolai Ingvald
Abstract
Magnetic resonance imaging (MRI) is an important tool in the diagnosis and monitoring of multiple sclerosis (MS). Diffusion MRI can be used to probe changes in normal appearing white matter (NAWM) through its ability to image changes in water diffusivity. Standard diffusion imaging techniques such as diffusion tensor imaging (DTI) are limited in their ability to quantify regions containing complex fibre geometries. The technique of fixel-based analysis (FBA) extracts information on the properties of fibre bundles within white matter and has shown advantages in analyzing areas containing complex fibre geometries. In this work, FBA and DTI analysis were applied to a set of relapsing remitting MS subjects (RRMS) and healthy controls. An image analysis pipeline was created to extract fixel-based and DTI metrics for MS lesions, NAWM adjacent lesions, and all NAWM. Comparisons were made between fixel-based and DTI metrics for differing disease modifying therapy (DMT) treatments in the three brain regions. We found significant differences in both fixel-based and DTI metrics between RRMS subjects and healthy controls. We found that the NAWM adjacent to MS lesions showed significant differences in fixel-based and DTI metrics compared to MS lesions and NAWM. Differences in fixel-based and DTI metrics between MS lesions and NAWM may indicate that fixel-based metrics can probe disease that extends out from MS lesions into NAWM. Analysis was performed to compare fixel-based and DTI metrics based on DMT. No significant trends were found between DMTs in terms of fixel-based and DTI metrics. This analysis is limited by the low sample sizes in this work. Further studies are needed to determine if small differences in fixel-based and DTI metrics can be observed between DMTs.
Item Metadata
Title |
Fixel based analysis of relapsing remitting multiple sclerosis subjects
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Creator | |
Supervisor | |
Publisher |
University of British Columbia
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Date Issued |
2022
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Description |
Magnetic resonance imaging (MRI) is an important tool in the diagnosis
and monitoring of multiple sclerosis (MS). Diffusion MRI can be used
to probe changes in normal appearing white matter (NAWM) through its
ability to image changes in water diffusivity. Standard diffusion imaging
techniques such as diffusion tensor imaging (DTI) are limited in their ability
to quantify regions containing complex fibre geometries. The technique
of fixel-based analysis (FBA) extracts information on the properties of fibre
bundles within white matter and has shown advantages in analyzing areas
containing complex fibre geometries.
In this work, FBA and DTI analysis were applied to a set of relapsing
remitting MS subjects (RRMS) and healthy controls. An image analysis
pipeline was created to extract fixel-based and DTI metrics for MS lesions,
NAWM adjacent lesions, and all NAWM. Comparisons were made between
fixel-based and DTI metrics for differing disease modifying therapy (DMT)
treatments in the three brain regions.
We found significant differences in both fixel-based and DTI metrics
between RRMS subjects and healthy controls. We found that the NAWM
adjacent to MS lesions showed significant differences in fixel-based and DTI
metrics compared to MS lesions and NAWM. Differences in fixel-based and
DTI metrics between MS lesions and NAWM may indicate that fixel-based
metrics can probe disease that extends out from MS lesions into NAWM.
Analysis was performed to compare fixel-based and DTI metrics based on
DMT. No significant trends were found between DMTs in terms of fixel-based
and DTI metrics. This analysis is limited by the low sample sizes in
this work. Further studies are needed to determine if small differences in
fixel-based and DTI metrics can be observed between DMTs.
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Genre | |
Type | |
Language |
eng
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Date Available |
2022-08-12
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0417295
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2022-09
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International