4D MR phase and magnitude segmentations with GPU parallel computing

  1. Get@NRC: 4D MR phase and magnitude segmentations with GPU parallel computing (Opens in a new window)
DOIResolve DOI: http://doi.org/10.1016/j.mri.2014.08.019
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Journal titleMagnetic Resonance Imaging
Pages134145; # of pages: 12
Subjectalgorithm; aorta flow; cardiovascular magnetic resonance; central processing unit; contrast radiography; graphics processing unit; image acquisition; image analysis; image processing; information processing; magnitude segmentation; mathematical computing; normal distribution; phase transition; segmentation algorithm; waveform
AbstractThe increasing size and number of data sets of large four dimensional (three spatial, one temporal) magnetic resonance (MR) cardiac images necessitates efficient segmentation algorithms. Analysis of phase-contrast MR images yields cardiac flow information which can be manipulated to produce accurate segmentations of the aorta. Phase contrast segmentation algorithms are proposed that use simple mean-based calculations and least mean squared curve fitting techniques. The initial segmentations are generated on a multi-threaded central processing unit (CPU) in 10. seconds or less, though the computational simplicity of the algorithms results in a loss of accuracy. A more complex graphics processing unit (GPU)-based algorithm fits flow data to Gaussian waveforms, and produces an initial segmentation in 0.5. seconds. Level sets are then applied to a magnitude image, where the initial conditions are given by the previous CPU and GPU algorithms. A comparison of results shows that the GPU algorithm appears to produce the most accurate segmentation.
Publication date
AffiliationNational Research Council Canada (NRC-CNRC); Medical Devices
Peer reviewedYes
NPARC number21275718
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Record identifierf87ae55b-8664-493f-9ac6-3dc9e73948e0
Record created2015-07-14
Record modified2016-05-09
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