期刊
IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL
卷 65, 期 8, 页码 1370-1379出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TUFFC.2018.2841346
关键词
Carotid strain imaging; compute unified device architecture (CUDA); elastography; graphics processing unit (GPU); ultrasound
资金
- National Institutes of Health [R01 NS064034, 2R01 CA112192]
- Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison
- Wisconsin Alumni Research Foundation for the development of the GPU-based strain estimation algorithm
A multilevel Lagrangian carotid strain imaging algorithm is analyzed to identify computational bottlenecks for implementation on a graphics processing unit (CPU). Displacement tracking including regularization was found to be the most computationally expensive aspect of this strain imaging algorithm taking about 2.2 h for an entire cardiac cycle. This intensive displacement tracking was essential to obtain Lagrangian strain tensors. However, mast of the computational techniques used for displacement tracking are parallelizable, and hence GPU implementation is expected to be beneficial. A new scheme for subsample displacement estimation referred to as a multilevel global peak finder was also developed since the Nelder-Mead simplex optimization technique used in the CPU implementation was not suitable for GPU implementation. GPU optimizations to minimize thread divergence and utilization of shared and texture memories were also implemented. This enables efficient use of the CPU computational hardware and memory bandwidth. Overall, an application speedup of 168.75x was obtained enabling the algorithm to finish in about 50 s for a cardiac cycle. Last, comparison of GPU and CPU implementations demonstrated no significant difference in the quality of displacement vector and strain tensor estimation with the two implementations up to a 5% interframe deformation. Hence, a CPU implementation is feasible for clinical adoption and opens opportunity for other computationally intensive techniques.
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