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UBC Theses and Dissertations

Robust and fast T2 decay analysis for measuring myelin water in MRI Yoo, Youngjin


Myelin is an essential component of nerve fibers and monitoring its health is important for studying neurological diseases that attack myelin, such as multiple sclerosis. The amount of water trapped within myelin, which is a surrogate for myelin content and integrity, can be measured in vivo using MRI relaxation techniques that acquire a series of images at multiple echo times to produce a T₂ decay curve at each voxel. These curves are then analyzed, most commonly using non-negative least squares (NNLS) fitting, to produce T₂ distributions from which water measurements are made. T₂ decay analysis using NNLS has two main challenges: instability and high computational demands. The main contributions of this thesis are: 1) we propose a new regularization algorithm in which local and non-local information is gathered and used adaptively for each voxel and 2) we propose a hybrid utilization method of multicore CPUs and GPUs to improve the efficiency of a T₂ decay analysis, with consideration of the increased computational burden of regularization and careful analysis of which algorithmic components would benefit from multicore CPU vs. GPU parallelization. Our results demonstrated that the proposed regularization method provided more globally consistent myelin water measurements, yet preserved fine structures. Our experiment with real patient data suggested that the algorithm improved the reproducibility and the ability to distinguish between the myelin maps of multiple sclerosis patients and healthy subjects. We also demonstrated our optimized implementation’s performance by comparing with a parallelized implementation written in MATLAB which is the most commonly used platform for the analysis of T₂ relaxation data. We found an improvement in speed of over 4× when computing single seven-slice myelin map and over 14× for a batch processing using the same number of CPU cores.

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