Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging
This provides a label-free and generalizable solution for enhancing vascular imaging in clinical ultrasound, addressing noise issues in both contrast-free and contrast-enhanced scenarios.
The paper tackled the problem of low signal-to-noise ratio in ultrasound microvascular imaging, which impairs vascular quantification and disease diagnosis, by proposing a self-supervised denoising framework called HA2HA that achieved improvements exceeding 15 dB in contrast-to-noise ratio and signal-to-noise ratio.
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.