Self-supervised Deep Learning for Denoising in Ultrasound Microvascular Imaging

TL;DR

This paper introduces HA2HA, a self-supervised deep learning framework that significantly improves ultrasound microvascular image quality by denoising RF data, enhancing vascular visualization without requiring labeled data.

Contribution

The novel HA2HA method constructs training pairs from angular subsets of RF data, enabling effective denoising in UMI without supervision, applicable across various contrast conditions.

Findings

Over 15 dB improvement in CNR and SNR.

Enhanced microvascular flow visualization in human liver.

Effective denoising in both power and color Doppler imaging.

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.