IEEE Trans Ultrason Ferroelectr Freq Control. 2020 Nov;67(11):2363-2377. doi: 10.1109/TUFFC.2020.3005426. Epub 2020 Jun 29.
The mechanical properties of soft tissues can be quantitatively characterized through the estimation of shear wave velocity (SWV) using various motion estimation methods, such as the commonly used block matching (BM) methods. However, such methods suffer from slow computational speed and many tunable parameters. In order to solve these problems, Butterworth filter-based clutter filter wave imaging (BW-CFWI) is recently proposed to detect the mechanical wave propagation by highlighting the tissue velocity induced by mechanical wave, without using any motion estimation methods. In this study, in order to improve the SWV estimation performance of the clutter filter wave imaging (CFWI) method, we propose singular value decomposition (SVD)-based clutter filter for CFWI (SVD-CFWI) and further accelerate it using a randomized SVD (rSVD)-based clutter filter (rSVD-CFWI). Homogeneous phantoms with different Young's moduli are used to investigate the influences of the cutoff order of singular value and iteration time on the performance of SWV estimation. An elasticity phantom with stepped cylindrical inclusions is tested for comparison of rSVD-CFWI, SVD-CFWI, BW-CFWI, and normalized cross-correlation (NCC)-based BM (NCC-BM). The performances of the proposed methods are also evaluated on data acquired from the bicipital muscle in vivo. The results of phantom experiments show that rSVD-CFWI and SVD-CFWI reconstruct SWV maps with improved shape of the inclusions. For the softest inclusion with a diameter of 10.40 mm, the contrast-to-noise ratios (CNRs) between the inclusions and background obtained with rSVD-CFWI (3.78 dB) and SVD-CFWI (3.71 dB) are higher than those obtained with BW-CFWI (0.55 dB) and NCC-BM (0.70 dB). For the stiffest inclusion with a diameter of 10.40 mm, higher CNRs are also achieved by rSVD-CFWI (5.68 dB) and SVD-CFWI (5.07 dB) than by BW-CFWI (2.92 dB) and NCC-BM (2.36 dB). In the in-vivo experiments, more homogeneous SWV maps and smaller standard deviations of SWVs are obtained with rSVD-CFWI and SVD-CFWI than with BW-CFWI and NCC-BM. Besides, RSVD-CFWI has lower computational complexity than SVD-CFWI and NCC-BM and has lower memory space requirement than SVD-CFWI. The computational speed of rSVD-CFWI is comparable to that of BW-CFWI and over 10 times higher than that of SVD-CFWI. Therefore, RSVD-CFWI is demonstrated to be a competitive tool for fast shear wave imaging.
软组织的力学特性可以通过使用各种运动估计方法（例如常用的块匹配（BM）方法）来估计剪切波速度（SWV）进行定量描述。然而，这些方法存在计算速度慢和许多可调参数的问题。为了解决这些问题，最近提出了基于巴特沃斯滤波器的杂波滤波器波成象（BW-CFWI），通过突出由机械波引起的组织速度来检测机械波的传播，而无需使用任何运动估计方法。在这项研究中，为了提高杂波滤波器波成象（CFWI）方法的 SWV 估计性能，我们提出了基于奇异值分解（SVD）的杂波滤波器 CFWI（SVD-CFWI），并进一步使用基于随机化 SVD（rSVD）的杂波滤波器（rSVD-CFWI）对其进行加速。使用具有不同杨氏模量的均匀体模来研究奇异值截断阶数和迭代时间对 SWV 估计性能的影响。使用具有阶跃圆柱形夹杂的弹性体模来比较 rSVD-CFWI、SVD-CFWI、BW-CFWI 和基于归一化互相关（NCC）的 BM（NCC-BM）。还在体内肱二头肌采集的数据上评估了所提出方法的性能。体模实验结果表明，rSVD-CFWI 和 SVD-CFWI 可以重建具有改进的夹杂形状的 SWV 图。对于直径为 10.40mm 的最软夹杂，rSVD-CFWI（3.78dB）和 SVD-CFWI（3.71dB）获得的夹杂与背景之间的对比度噪声比（CNR）高于 BW-CFWI（0.55dB）和 NCC-BM（0.70dB）获得的 CNR。对于直径为 10.40mm 的最硬夹杂，rSVD-CFWI（5.68dB）和 SVD-CFWI（5.07dB）也比 BW-CFWI（2.92dB）和 NCC-BM（2.36dB）获得更高的 CNR。在体内实验中，rSVD-CFWI 和 SVD-CFWI 获得的 SWV 图比 BW-CFWI 和 NCC-BM 更均匀，SWV 的标准差更小。此外，rSVD-CFWI 的计算复杂度低于 SVD-CFWI 和 NCC-BM，并且对内存空间的要求低于 SVD-CFWI。rSVD-CFWI 的计算速度与 BW-CFWI 相当，并且比 SVD-CFWI 快 10 多倍。因此，rSVD-CFWI 被证明是一种用于快速剪切波成象的有竞争力的工具。
