GPU加速的高精度位移估计方法及超声弹性成像应用

GPU-accelerated Sub-sample Displacement Estimation Method for Real-time Ultrasound Elastography

高精度的位移估计方法对提高超声弹性成像的质量非常重要。在本研究中,通过英伟达公司的CUDA架构实现了一种新颖的可以同时提高轴向和横向运动估计精确度的位移估计方法在GPU上的高效并行计算。对比于原始方法在C Mex编译条件下的实现,GPU实现的方法显示最多可实现76X的加速,同时保持了较高的位移估计精度。

Highly accurate displacement estimation method is critical for Ultrasound Elastography. In this study, a novel sub-sample displacement estimation algorithm where axial and lateral motion estimates are simultaneously performed to enhance the accuracy of motion tracking the proposed method has been implemented in CUDA. Compared to the original C Mex（MATLAB VERSION 2014 B, Mathswork Inc., MA, USA） implementation, the GPU implementation showed a factor of 70＋ acceleration while maintaining the excellent motion tracking accuracy.