Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging

Exploring foci of: arXiv (Cornell University) Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging July 2025 • Lijie Huang, Jingyi Yin, Jingke Zhang, U‐Wai Lok, Ryan M. DeRuiter, Jieyang Jin, Kate M Knoll, Kendra E. Petersen, James D. Krier, Xiangyang Zhu, Gin… Ultrasound microvascular imaging (UMI) is often hindered by low signal-to-noise ratio (SNR), especially in contrast-free or deep tissue scenarios, which impairs subsequent vascular quantification and reliable disease diagnosis. To address this challenge, we propose Half-Angle-to-Half-Angle (HA2HA), a self-supervised denoising framework specifically designed for UMI. HA2HA constructs training pairs from complementary angular subsets of beamformed radio-frequency (RF) blood flow data, across which vascular signals r… Open Article Page

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