超声定位显微镜 (ULM) 有助于以数十微米的分辨率对微血管进行结构和血流动力学成像。在 ULM 中，有效微泡信号的提取对于图像质量至关重要。奇异值分解 (SVD) 是目前 ULM 中最流行的微泡信号提取方法。大多数现有的 ULM 研究采用经验值的固定 SVD 滤波器阈值，这将导致由于血液信号分离不充分而导致成像质量下降。在本研究中，我们提出了一种基于冠层密度聚类的自适应非阈值 SVD 滤波器，称为 DCC-SVD。该滤波器根据时空特征的密度自动对 SVD 的组件进行分类，从而无需选择参数。在体外管模型中，DCC-SVD 证明了其在不同微泡浓度和流速下自适应分离血液和气泡信号的能力。我们使用浓度变量体内大鼠脑数据以及开源大鼠肾脏和小鼠肿瘤数据集。所提出的 DCC-SVD 将全局空间分辨率提高了约 4 μm，从 30.39 μm 提高到 26.02 μm。它还捕获了其他方法获得的图像中不存在的血管结构，并产生了更平滑的血管强度轮廓，使其成为 ULM 成像的一种有前景的时空滤波器。版权所有 © 2024 Elsevier B.V. 保留所有权利。

Ultrasound Localization Microscopy (ULM) facilitates structural and hemodynamic imaging of microvessels with a resolution of tens of micrometers. In ULM, the extraction of effective microbubble signals is crucial for image quality. Singular Value Decomposition (SVD) is currently the most prevalent method for microbubble signal extraction in ULM. Most existing ULM studies employ a fixed SVD filter threshold using empirical values which will lead to imaging quality degradation due to the insufficient separation of blood signals. In this study, we propose an adaptive and non-threshold SVD filter based on canopy-density clustering, termed DCC-SVD. This filter automatically classifies the components of the SVD based on the density of their spatiotemporal features, eliminating the need for parameter selection. In in vitro tube phantom, DCC-SVD demonstrated its ability to adaptive separation of blood and bubble signal at varying microbubble concentrations and flow rates. We compared the proposed DCC-SVD method with the Block-match 3D (BM3D) filter and a classical adaptive method called spatial similarity matrix (SSM), using concentration-variable in vivo rat brain data, as well as open-source rat kidney and mouse tumor datasets. The proposed DCC-SVD improved the global spatial resolution by approximately 4 μm from 30.39 μm to 26.02 μm. It also captured vessel structure absent in images obtained by other methods and yielded a smoother vessel intensity profile, making it a promising spatiotemporal filter for ULM imaging.Copyright © 2024 Elsevier B.V. All rights reserved.