Wang Baoyu, Zhang Miao, Liu Ruilin, Zhang Shi
School of Computer Science & Engineering, Northeastern University, Shenyang 110000, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Aug 25;39(4):740-748. doi: 10.7507/1001-5515.202203032.
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血流成像的分辨率。我们通过模拟数据实验和人体肾脏实体数据成像实验,比较了传统的多项式回归壁滤波器算法以及两种基于奇异值分解(SVD)的算法,即全奇异值分解(Full-SVD)算法和随机采样奇异值分解(RS-RSVD)算法。实验结果表明,传统的多项式回归壁滤波器算法的滤波效果有限,然而,全奇异值分解算法和随机采样奇异值分解算法能够更好地从组织或噪声信号中提取微血管血流信号。当随机采样奇异值分解算法被随机划分为16个块时,其信噪比与全奇异值分解算法相同,对比度噪声比降低了2.05 dB,执行时间减少了90.41%。随机采样奇异值分解算法能够提高运算效率,更有利于高帧率超声微血管的实时成像。