Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
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 remain consistent while noise varies. HA2HA
was trained using in-vivo contrast-free pig kidney data and validated across
diverse datasets, including contrast-free and contrast-enhanced data from pig
kidneys, as well as human liver and kidney. An improvement exceeding 15 dB in
both contrast-to-noise ratio (CNR) and SNR was observed, indicating a
substantial enhancement in image quality. In addition to power Doppler imaging,
denoising directly in the RF domain is also beneficial for other downstream
processing such as color Doppler imaging (CDI). CDI results of human liver
derived from the HA2HA-denoised signals exhibited improved microvascular flow
visualization, with a suppressed noisy background. HA2HA offers a label-free,
generalizable, and clinically applicable solution for robust vascular imaging
in both contrast-free and contrast-enhanced UMI.
