Wang Lutao, Xiao Jun, Chai Hua
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2015 Aug;32(4):773-8.
The successful suppression of clutter arising from stationary or slowly moving tissue is one of the key issues in medical ultrasound color blood imaging. Remaining clutter may cause bias in the mean blood frequency estimation and results in a potentially misleading description of blood-flow. In this paper, based on the principle of general wall-filter, the design process of three classes of filters, infinitely impulse response with projection initialization (Prj-IIR), polynomials regression (Pol-Reg), and eigen-based filters are previewed and analyzed. The performance of the filters was assessed by calculating the bias and variance of a mean blood velocity using a standard autocorrelation estimator. Simulation results show that the performance of Pol-Reg filter is similar to Prj-IIR filters. Both of them can offer accurate estimation of mean blood flow speed under steady clutter conditions, and the clutter rejection ability can be enhanced by increasing the ensemble size of Doppler vector. Eigen-based filters can effectively remove the non-stationary clutter component, and further improve the estimation accuracy for low speed blood flow signals. There is also no significant increase in computation complexity for eigen-based filters when the ensemble size is less than 10.
成功抑制来自静止或缓慢移动组织的杂波是医学超声彩色血流成像中的关键问题之一。残留的杂波可能会导致平均血流频率估计出现偏差,并可能对血流情况产生误导性描述。本文基于通用壁滤波器原理,对三类滤波器(带投影初始化的无限脉冲响应滤波器(Prj-IIR)、多项式回归滤波器(Pol-Reg)和基于特征值的滤波器)的设计过程进行了介绍和分析。通过使用标准自相关估计器计算平均血流速度的偏差和方差来评估滤波器的性能。仿真结果表明,Pol-Reg滤波器的性能与Prj-IIR滤波器相似。在稳定杂波条件下,它们都能提供准确的平均血流速度估计,并且可以通过增加多普勒矢量的集合大小来增强杂波抑制能力。基于特征值的滤波器可以有效去除非平稳杂波分量,并进一步提高低速血流信号的估计精度。当集合大小小于10时,基于特征值的滤波器的计算复杂度也不会显著增加。