4D MR phase and magnitude segmentations with GPU parallel computing
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.
MRI, Segmentation, GPU, Flow, Phase, Magnitude, Parallel, Aorta, Physics