Ewart Mark Haacke, Ph.D. (Committee Co-Chair); Thomas Wischgoll, Ph.D. (Committee Co-Chair); Tanvi Banerjee, Ph.D. (Committee Member); Yong Pei, Ph.D. (Committee Member)
Doctor of Philosophy (PhD)
Quantitative susceptibility mapping (QSM) is a powerful technique that reveals changes in the underlying tissue susceptibility distribution. It can be used to measure the concentrations of iron and calcium in the brain both of which are linked with numerous neurodegenerative diseases. However, reconstructing the QSM image from the MRI phase data is an ill-posed inverse problem. Different methods have been proposed to overcome this difficulty. Still, the reconstructed QSM images suffer from streaking artifacts and underestimate the measured susceptibility of deep gray matter, veins, and other high susceptibility regions. This thesis proposes a structurally constrained Susceptibility Weighted Imaging and Mapping (scSWIM) method to reconstruct QSM for multi-echo, multi-flip angle data collected using strategically acquired gradient echo (STAGE) imaging. scSWIM performs a single step regularization-based reconstruction technique that takes advantage of the unique contrast of the STAGE T1 weighted enhanced (T1WE) image to extract reliable geometry constraints to protect the basal ganglia from over-smoothing. Furthermore, the multi-echo, multi-flip angle data from STAGE can all be used to improve the contrast-to-noise ratio in QSM through a weighted averaging scheme. scSWIM was tested on both simulated and in vivo data. Results show that the unique contrast and tissue boundaries from T1WE and an earlier approach called iterative SWIM enable the accurate definition of the edges of high susceptibility regions. scSWIM achieved the best overall root mean squared error and structural similarity index metrics as well as the lowest deviation from the expected susceptibility in deep gray matter compared to other published methods. Finally, susceptibility measurements of the basal ganglia extracted from the scSWIM data for a cohort of Parkinson’s disease patients and healthy control subjects were in agreement with the literature.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
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