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dc.contributor.supervisor Bidinosti, Chris (Physics & Astronomy) en_US
dc.contributor.author Bergen, Robert
dc.date.accessioned 2014-05-26T13:38:26Z
dc.date.available 2014-05-26T13:38:26Z
dc.date.issued 2014-05-26
dc.identifier.uri http://hdl.handle.net/1993/23594
dc.description.abstract Analysis of phase-contrast MR images yields cardiac flow information which can be manipulated to produce accurate segmentations of the aorta. New phase contrast segmentation algorithms are proposed that use mean-based calculations and least mean squared curve fitting techniques. A GPU is used to accelerate these algorithms and it is shown that it is possible to achieve up to a 2760x speedup relative to the CPU computation times. Level sets are applied to a magnitude image, where initial conditions are given by the previous segmentation algorithms. A qualitative comparison of results shows that the algorithm parallelized on the GPU appears to produce the most accurate segmentation. After segmentation, particle trace simulations are run to visualize flow patterns in the aorta. A procedure for the definition of analysis planes is proposed from which virtual particles can be emitted/collected within the vessel, which is useful for future quantification of various flow parameters. en_US
dc.subject MRI en_US
dc.subject Segmentation en_US
dc.subject GPU en_US
dc.subject Flow en_US
dc.subject Phase en_US
dc.subject Magnitude en_US
dc.subject Parallel en_US
dc.subject Aorta en_US
dc.subject Physics en_US
dc.title 4D MR phase and magnitude segmentations with GPU parallel computing en_US
dc.degree.discipline Physics and Astronomy en_US
dc.contributor.examiningcommittee Pistorious, Stephen (Physics & Astronomy) Alexander, Murray (University of Winnipeg, Physics) Thomas, Gabriel (Electrical & Computer Engineering) Lin, Hung-yu (Radiology) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note October 2014 en_US


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