The calculation of correlation is critically important for ultrasound strain elastography. The sum-table based method for the calculation of the normalized correlation coefficient (ST-NCC) can greatly improve computational efficiency under an environment of serial computing. Its implementation and performance are yet to be investigated when given a parallel computing platform, particularly, under a GPU environment. In this study, the published ST-NCC method was implemented into GPU and its performance was evaluated for speckle tracking. Particularly, the performance of the ST-NCC method was compared to the classic method of computing NCC using simulated ultrasound data. Our preliminary results indicated that, under the GPU platform, the implemented ST-NCC method did not further improve the computational efficiency, as compared to the classic NCC method implemented into the same GPU platform.
Performance analysis of a sum-table-based method for computing cross-correlation in GPU-accelerated ultrasound strain elastography
First published at:Jun 01, 2019
1 Jiang J, Hall T J. A parallelizable real-time motion tracking algorithm with applications to ultrasonic strain imaging[J]. Physics in Medicine & Biology, 2007, 52(13): 3773-3790. DOI:10.1088/0031-9155/52/13/008
2 Chen L J, Treece G M, Lindop J E, et al. A quality-guided displacement tracking algorithm for ultrasonic elasticity imaging[J]. Medical Image Analysis, 2009, 13(2): 286-296. DOI:10.1016/j.media.2008.10.007
3 Peng B, Wang Y Q, Hall T J, et al. A GPU-accelerated 3-D coupled subsample estimation algorithm for volumetric breast strain elastography[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2017, 64(4): 694-705. DOI:10.1109/TUFFC.2017.2661821
4 Zhou Y J, Zheng Y P. A motion estimation refinement framework for real-time tissue axial strain estimation with freehand ultrasound[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2010, 57(9): 1943-1951. DOI:10.1109/TUFFC.2010.1642
5 Luo J W, Konofagou E E. A fast normalized cross-correlation calculation method for motion estimation[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2010, 57(6): 1347-1357. DOI:10.1109/TUFFC.2010.1554
6 Zhu Y N, Hall T J. A modified block matching method for real-time freehand strain imaging[J]. Ultrasonic Imaging, 2002, 24(3): 161-176. DOI:10.1177/016173460202400303
7 D'Hooge J, Bijnens B, Thoen J, et al. Echocardiographic strain and strain-rate imaging: a new tool to study regional myocardial function[J]. IEEE Transactions on Medical Imaging, 2002, 21(9): 1022-1030. DOI:10.1109/TMI.2002.804440
8 Konofagou E E, D'Hooge J, Ophir J. Myocardial elastography--a feasibility study in vivo[J]. Ultrasound in Medicine & Biology, 2002, 28(4): 475-482. DOI:10.1016/S0301-5629(02)00488-X
10 Yang X, Deka S, Righetti R. A hybrid CPU-GPGPU approach for real-time elastography[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2011, 58(12): 2631-2645. DOI:10.1109/TUFFC.2011.2126
11 Peng B, Huang L. GPU-accelerated sub-sample displacement estimation method for real-time ultrasound elastography[J]. Opto-Electronic Engineering, 2016, 43(6): 83-88. DOI:10.3969/j.issn.1003-501X.2016.06.014
彭博, 黄丽. GPU加速的高精度位移估计方法及超声弹性成像应用[J].光电工程, 2016, 43(6): 83-88. DOI:10.3969/j.issn.1003-501X.2016.06.014
12 Peng B, Chen Y, Liu D Q. Investigation of GPU-based ultrasound elastography[J]. Opto-Electronic Engineering, 2013, 40(5): 97-105. DOI:10.3969/j.issn.1003-501X.2013.05.014
彭博, 谌勇, 刘东权.基于GPU的超声弹性成像并行实现研究[J].光电工程, 2013, 40(5): 97-105. DOI:10.3969/j.issn.1003-501X.2013.05.014
13 Rosenzweig S, Palmeri M, Nightingale K. GPU-based real-time small displacement estimation with ultrasound[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2011, 58(2): 399-405. DOI:10.1109/TUFFC.2011.1817
14 Chang L W, Hsu K H, Li P C. GPU-based color Doppler ultrasound processing[C]//2009 IEEE International Ultrasonics Symposium. Rome, Italy, 2009.
15 Sun X, Wang S S, Song J J, et al. Toward parallel optimal computation of ultrasound computed tomography using GPU[J]. Proceedings of SPIE, 2018, 10580: 105800R.
16 Sengupta S, Harris M, Garland M, et al. Efficient parallel scan algorithms for GPUs[M]//Kurzak J, Bader D A, Dongarra J. Scientific Computing with Multicore and Accelerators. Boca Raton: Taylor & Francis, 2008.
17 Blelloch G E. Scans as primitive parallel operations[J]. IEEE Transactions on Computers, 2002, 38(11): 1526-1538. DOI:10.1109/12.42122
18 Jensen J A. Field: A program for simulating ultrasound systems[J]. Medical & Biological Engineering & Computing, 1996, 34(1): 351-352.
19 Luo J W, Bai J, He P, et al. Axial strain calculation using a low-pass digital differentiator in ultrasound elastography[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2004, 51(9): 1119-1127. DOI:10.1109/TUFFC.2004.1334844
20 Du H N, Liu J, Pellot-Barakat C, et al. Optimizing multicompression approaches to elasticity imaging[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2006, 53(1): 90-99. DOI:10.1109/TUFFC.2006.1588394
Scientific Innovation Program of Sichuan Province (Major Engineering Project: 2018RZ0093) and Nanchong Scientific Council (Strategic Cooperation Program Between University and City: NC17SY4020)
Get Citation: Peng Bo, Luo Shasha, Yang Feng, et al. Performance analysis of a sum-table-based method for computing cross-correlation in GPU-accelerated ultrasound strain elastography[J]. Opto-Electronic Engineering, 2019, 46(6): 180437.