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A robust and sensitive voxel-based method for measuring cortical thickness change using fuzzy correspondence Saurabh, Saurabh


The human cerebral cortex is a deep folded structure of grey matter and is responsible for maintaining cognitive functions. The cortical thickness has emerged as an important surrogate biomarker in many neurodegenerative diseases. In this thesis, we propose a longitudinal method called LCT (Longitudinal Cortical Thickness) for measuring cortical thickness changes over time in magnetic resonance scans. We adopt a voxel-based approach rather than surface-based to gain computational efficiency and sensitivity to subtle changes but aim to achieve the scan-rescan reproducibility of surface-based methods. The existing surface-based methods are very time-consuming and the topological and smoothness constraints imposed during surface reconstruction may blunt longitudinal sensitivity. Also, longitudinal processing requires establishing anatomical correspondence across time, and most current methods use deformable registration for this purpose, which requires setting many parameters that can directly affect the measurements. In contrast, LCT establishes cortex-specific matches by using three intuitive features: normal direction, spatial coordinates and shape context that are defined on the cortical skeleton. We also introduce the concept of fuzzy correspondence, which allows a skeletal point in one scan to be partially matched to multiple points in another scan, thereby enhancing the stability of the matches. LCT was evaluated using three longitudinal datasets: 1) same-day pairs of scans of 15 subjects to test for scan-rescan reproducibility, 2) paired scans of 50 Alzheimer’s disease (AD) and 50 healthy subjects taken one-year apart, to test our method’s ability to distinguish between AD and normal, and 3) paired scans of 100 secondary progressive multiple sclerosis (MS) subjects taken two years apart, to test for sensitivity to change over time. Tests on the scan-rescan dataset show that LCT is comparable in scan-rescan reproducibility to two other state-of-the-art methods: FreeSurfer and minimum line integral (MLI). Tests on the AD dataset show that LCT detected larger group differences between AD and normal subjects than FreeSurfer and MLI. Further, the results on the MS dataset demonstrate that LCT is more sensitive to change over time and produced stronger correlations between cortical thickness changes and changes in clinical scores than FreeSurfer, which did not produce any statistically significant correlations.

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