管壁和血流多普勒信号的分离研究

根据超声多普勒血流信号和血管壁搏动信号的统计特性,提出了一种基于主元分析的非线性滤波方法。并用于计算机仿真的超声多普勒信号和实际采集的人体颈总动脉多普勒信号的处理,同时与传统的高通滤波器方法进行了比较。实验中得到了与实际更接近的平均流速估计,相对误差仅为5.62%,与滤波法的相对误差12.83%相比,准确性得到了一定的提高。结果表明:基于主元分析的非线性滤波方法能在较大的管壁血流功率比范围内滤除管壁搏动信号,同时保留低频血流信号成分,因而可用于超声多普勒系统中管壁和血流信号成分的分离,分离后的这些低频的血流成分信号能使以后的参数提取和信号分析工作具有更高的精度和可靠性。

Study on separation methods for wall clutter and Doppler blood signals

A nonlinear filtering method based on principle component analysis (PCA) was proposed according to the statistical characteristics of the Doppler ultrasound blood flow signal and wall thump signal. This method was applied to computer-simulated and clinically collected carotid Doppler signals, and compared with the conventional high-pass filtering method. The mean blood velocity was estimated using the proposed method with a relative error of 5.62%, which demonstrated improved accuracy compared to 12.83% by using the high-pass filtering method. The experiment results indicate that the PCA-based nonlinear filtering method can remove wall thump signal under a broad wall-to-blood ratio (WBR) range while preserving low frequency blood component, thus it can be applied to the separation of the blood and wall components in Doppler ultrasound systems, and facilitate better accuracy and reliability of the further parameter extraction and signal analysis.