Filtering based on Singular Value Decomposition (SVD) provides substantial separation of clutter, flow and noise in high frame rate ultrasound flow imaging. The use of SVD as a clutter filter has greatly improved techniques such as vector flow imaging, functional ultrasound and super-resolution ultrasound localization microscopy. The removal of clutter and noise relies on the assumption that tissue, flow and noise are each represented by different subsets of singular values, so that their signals are uncorrelated and lay on orthogonal sub-spaces. This assumption fails in the presence of tissue motion, for near-wall or microvascular flow, and can be influenced by an incorrect choice of singular value thresholds. Consequently, separation of flow, clutter and noise is imperfect, which can lead to image artefacts not present in the original data. Temporal and spatial fluctuation in intensity are the commonest artefacts, which vary in appearance and strengths. Ghosting and splitting artefacts are observed in the microvasculature where the flow signal is sparsely distributed. Singular value threshold selection, tissue motion, frame rate, flow signal amplitude and acquisition length affect the prevalence of these artefacts. Understanding what causes artefacts due to SVD clutter and noise removal is necessary for their interpretation.

翻译：基于奇异值分解的滤波方法在高帧率超声血流成像中实现了对杂波、血流和噪声的有效分离。将SVD用作杂波滤波器极大推动了矢量血流成像、功能超声成像及超分辨率超声定位显微成像等技术的发展。该方法的杂波与噪声去除基于以下假设：组织、血流和噪声分别由不同的奇异值子集表征，其信号互不相关且位于正交子空间中。然而在组织运动存在的情况下，该假设对近壁或微血管血流会失效，且可能受到奇异值阈值选取不当的影响。因此，血流、杂波与噪声的分离存在不完善性，可能导致原始数据中不存在的图像伪影。时空强度波动是最常见的伪影类型，其外观与强度各异。在血流信号稀疏分布的微血管系统中可观察到重影和分裂伪影。奇异值阈值选择、组织运动、帧率、血流信号幅度及采集时长均会影响这些伪影的普遍性。理解SVD杂波与噪声去除产生伪影的成因，对于正确解读图像具有重要意义。