Blind source separation (BSS) refers to a number of signal processing techniques that decompose a signal into several “source” signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review the existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound super-resolution, contrast quantification, and speckle tracking. For all applications, this was done in silico , in vitro , and in vivo . We found that the critical step in BSS filtering is the identification of components containing the desired signal and highlighted the value of a priori domain knowledge to define effective criteria for signal component selection.

中文翻译：

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用于超声成像中杂波和噪声抑制的盲源分离：针对不同应用的评论。

盲源分离（BSS）是指将信号分解为多个“源”信号的多种信号处理技术。近年来，BSS被越来越多地用于抑制超声成像中的杂波和噪声。尤其是，它具有基于独立性度量而不是其时间或空间频率内容来分离源的能力，使BSS成为用于数据的强大过滤工具，其中期望和不期望信号在频谱域中重叠。这项工作的目的是回顾现有的BSS方法及其在超声成像中的潜力。此外，我们在对比超声超分辨率，对比量化和斑点跟踪领域测试并比较了这些技术的有效性。对于所有应用程序，都已完成在计算机上 ， 体外 和 体内 。我们发现，BSS滤波的关键步骤是识别包含所需信号的分量，并突出显示了先验 领域知识，为信号成分选择定义有效标准。