The design of wall filter in ultrasonic microvascular imaging directly affects the resolution of blood flow imaging. We compared the traditional polynomial regression wall filter algorithm and two algorithms based on singular value decomposition (SVD), Full-SVD algorithm and RS-RSVD algorithm (random sampling based on random singular value decomposition) through experiments with simulated data and human renal entity data imaging experiments. The experimental results showed that the filtering effect of the traditional polynomial regression wall filter algorithm was limited, however, Full-SVD algorithm and RS-RSVD algorithm could better extract the micro blood flow signal from the tissue or noise signal. When RS-RSVD algorithm was randomly divided into 16 blocks, the signal-to-noise ratio was the same as that of Full-SVD algorithm, reduces the contrast-to-noise ratio by 2.05 dB, and reduces the execution time by 90.41%. RS-RSVD algorithm can improve the operation efficiency and is more conducive to the real-time imaging of high frame rate ultrasound microvessels.
超声微血管成像中壁滤波器的设计直接影响着血流成像的分辨率。本文通过仿真数据实验和人体肾脏实体数据成像实验对比了传统多项式回归壁滤波器算法以及基于奇异值分解（SVD）的Full-SVD算法和RS-RSVD算法（一种基于随机奇异值分解的随机下采样算法）。实验结果表明，传统的多项式回归壁滤波器算法滤波效果有限，而Full-SVD算法和RS-RSVD算法可以更好地将微血流信号从组织或噪声信号中提取出来。RS-RSVD算法在随机分块16次时与Full-SVD算法信噪比相同，对比噪声比降低了2.05 dB，执行时间降低了90.41%。RS-RSVD算法能够提升运行效率，更有利于高帧频超声微血管的实时成像。.
Keywords: Micro flow imaging; Singular value decomposition; Ultrasonic imaging; Wall filter.