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.

使用奇异值分解进行滤波可以在超高帧率超声流动成像中去除杂音、流量和杂质，但是对于病变组织的运动、靠近管壁的微血管流动等情况，这一假设并不成立，可能会导致图像伪影的存在。因此，了解 SVD 滤波引起的伪影的原因对于图像的解释是必要的。