Zhang Chen, Liang Tianzhu, Mok Philip K T, Yu Weichuan
IEEE Trans Nanobioscience. 2017 Jul;16(5):314-325. doi: 10.1109/TNB.2017.2705104. Epub 2017 May 17.
In ultrasound image analysis, the speckle tracking methods are widely applied to study the elasticity of body tissue. However, "feature-motion decorrelation" still remains as a challenge for the speckle tracking methods. Recently, a coupled filtering method and an affine warping method were proposed to accurately estimate strain values, when the tissue deformation is large. The major drawback of these methods is the high computational complexity. Even the graphics processing unit (GPU)-based program requires a long time to finish the analysis. In this paper, we propose field-programmable gate array (FPGA)-based implementations of both methods for further acceleration. The capability of FPGAs on handling different image processing components in these methods is discussed. A fast and memory-saving image warping approach is proposed. The algorithms are reformulated to build a highly efficient pipeline on FPGA. The final implementations on a Xilinx Virtex-7 FPGA are at least 13 times faster than the GPU implementation on the NVIDIA graphic card (GeForce GTX 580).
在超声图像分析中，散斑跟踪方法被广泛应用于研究人体组织的弹性。然而，“特征运动去相关”仍然是散斑跟踪方法面临的一个挑战。最近，提出了一种耦合滤波方法和一种仿射变形方法，用于在组织变形较大时准确估计应变值。这些方法的主要缺点是计算复杂度高。即使是基于图形处理单元（GPU）的程序也需要很长时间才能完成分析。在本文中，我们提出了基于现场可编程门阵列（FPGA）的这两种方法的实现，以进一步加速。讨论了FPGA在处理这些方法中不同图像处理组件方面的能力。提出了一种快速且节省内存的图像变形方法。对算法进行了重新设计，以在FPGA上构建高效的流水线。在Xilinx Virtex-7 FPGA上的最终实现比在NVIDIA显卡（GeForce GTX 580）上的GPU实现至少快13倍。
