Vessel diseases are often accompanied by abnormalities related to vascular shape and size. Therefore, a clear visualization of vasculature is of high clinical significance. Ultrasound color flow imaging (CFI) is one of the prominent techniques for flow visualization. However, clutter signals originating from slow-moving tissue are one of the main obstacles to obtain a clear view of the vascular network. Enhancement of the vasculature by suppressing the clutters is a significant and irreplaceable step for many applications of ultrasound CFI. Currently, this task is often performed by singular value decomposition (SVD) of the data matrix. This approach exhibits two well-known limitations. First, the performance of SVD is sensitive to the proper manual selection of the ranks corresponding to clutter and blood subspaces. Second, SVD is prone to failure in the presence of large random noise in the dataset. A potential solution to these issues is using decomposition into low-rank and sparse matrices (DLSM) framework. SVD is one of the algorithms for solving the minimization problem under the DLSM framework. Many other algorithms under DLSM avoid full SVD and use approximated SVD or SVD-free ideas which may have better performance with higher robustness and less computing time. In practice, these models separate blood from clutter based on the assumption that steady clutter represents a low-rank structure and that the moving blood component is sparse. In this paper, we present a comprehensive review of ultrasound clutter suppression techniques and exploit the feasibility of low-rank and sparse decomposition schemes in ultrasound clutter suppression. We conduct this review study by adapting 106 DLSM algorithms and validating them against simulation, phantom, and in vivo rat datasets. Two conventional quality metrics, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), are used for performance evaluation. In addition, computation times required by different algorithms for generating clutter suppressed images are reported. Our extensive analysis shows that the DLSM framework can be successfully applied to ultrasound clutter suppression.

中文翻译：

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超声中的杂波抑制：性能评估以及低秩和稀疏矩阵分解方法的回顾。

血管疾病通常伴有与血管形状和大小有关的异常。因此，清晰可视化的脉管系统具有很高的临床意义。超声彩色流成像（CFI）是流可视化的重要技术之一。然而，源自缓慢运动的组织的杂波信号是获得清晰可见的血管网络的主要障碍之一。通过抑制杂波来增强脉管系统对于超声CFI的许多应用而言是重要且不可替代的步骤。当前，通常通过数据矩阵的奇异值分解（SVD）来执行此任务。这种方法表现出两个众所周知的局限性。首先，SVD的性能对正确选择与杂波和血液子空间相对应的等级很敏感。第二，在数据集中存在大量随机噪声的情况下，SVD容易失效。这些问题的潜在解决方案是使用分解为低秩和稀疏矩阵（DLSM）框架。SVD是解决DLSM框架下最小化问题的算法之一。DLSM下的许多其他算法都避免使用完整的SVD，并使用近似的SVD或无SVD的构想，这些构想可能具有更好的性能，更高的鲁棒性和更少的计算时间。在实践中，这些模型基于以下假设：稳定的杂波代表低等级结构，并且运动的血液成分稀疏，从而将血液与杂波分开。在本文中，我们对超声杂波抑制技术进行了全面的综述，并探讨了低秩和稀疏分解方案在超声杂波抑制中的可行性。我们通过改编106种DLSM算法并针对仿真，幻像和体内大鼠数据集对它们进行验证来进行本综述研究。性能评估使用了两个常规质量指标，即信噪比（SNR）和对比噪声比（CNR）。另外，报告了用于生成杂波抑制图像的不同算法所需的计算时间。我们的广泛分析表明，DLSM框架可以成功地应用于超声杂波抑制。报告了不同算法生成杂波抑制图像所需的计算时间。我们的广泛分析表明，DLSM框架可以成功地应用于超声杂波抑制。报告了不同算法生成杂波抑制图像所需的计算时间。我们的广泛分析表明，DLSM框架可以成功地应用于超声杂波抑制。