Enhanced SVD filter based on singular vector subspace denoising improves ultrafast ultrasound microvascular imaging performance

作者：Yu Xia, Jiabin Zhang, Daichao Chen, Jingyi Yin, Hao Yu, Jue Zhang · 发表于：Physics in Medicine and Biology · 年份：2025 · DOI：10.1088/1361-6560/ae085f · 被引用次数：3 · 研究领域：Photoacoustic and Ultrasonic Imaging、Ultrasound Imaging and Elastography、Ultrasound and Hyperthermia Applications

Abstract Objective . Ultrafast ultrasound imaging can significantly improve the ability of ultrasound for microvascular visualization. Clutter filtering through singular value decomposition (SVD)-based filter remains a pivotal step in Ultrafast ultrasound imaging. However, the current hard threshold-based SVD filter cannot completely separate blood flow from noise on the basis of filtering tissue clutter, resulting in low contrast in microvascular imaging. This paper proposes a novel enhanced SVD (eSVD) filter to enhance blood flow signals and suppress noise while filtering clutter. Approach . The proposed method innovatively partitions spatial singular vectors into multiple blood flow subspaces followed by subspace-specific weighted reconstruction to amplify blood signatures. Main results . We validate the effectiveness of the eSVD filter in contrast-free ultrafast power Doppler imaging (uPDI), contrast-enhanced uPDI, and ultrasound localization microscopy (ULM) imaging experiments. Qualitative and quantitative experimental results show that compared with the hard threshold-based SVD filter, our method can significantly improve the contrast between vessels and background, and highlight the details of microvessels. Compared with the adaptive SVD filter based on the spatial similarity matrix, our eSVD filter improves contrast-to-noise ratio by 8.36 dB, signal-to-noise ratio by 7.92 dB, and blood-to-clutter ratio by 15.47 dB in the uPDI of mouse contrast-free brain. In the ULM ...