ArticleBiomedical signal processing and control2026
Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging.
Article in Biomedical signal processing and control, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Ultrasound microvascular imaging (UMI) has emerged as a powerful, noninvasive modality for visualizing the microvasculature, offering valuable insights into underlying pathological and physiological processes. However, it is often hindered by low signal-to-noise ratio (SNR) of unfocused plane wave imaging, especially in contrast-free or deep tissue scenarios, which may impair 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 remain consistent while noise varies. HA2HA was trained using
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