National Research Council of Canada. Medical Devices
algorithm; 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
The 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.
Magnetic Resonance Imaging33, no. 1 (January 2015): 134–145.