Deep Ultrasound Denoising Without Clean Data

TL;DR

This paper introduces a deep learning method for ultrasound image denoising that does not require clean training data, leveraging noisy pairs to effectively reduce noise in deep tissue regions.

Contribution

The proposed approach trains on noisy pairs without clean targets, converging to the clean image as the average of the pairs, which is novel in ultrasound denoising.

Findings

Effective noise reduction on real phantom data.

Improved image quality in deep tissue regions.

No need for clean training data.

Abstract

On one hand, the transmitted ultrasound beam gets attenuated as propagates through the tissue. On the other hand, the received Radio-Frequency (RF) data contains an additive Gaussian noise which is brought about by the acquisition card and the sensor noise. These two factors lead to a decreasing Signal to Noise Ratio (SNR) in the RF data with depth, effectively rendering deep regions of B-Mode images highly unreliable. There are three common approaches to mitigate this problem. First, increasing the power of transmitted beam which is limited by safety threshold. Averaging consecutive frames is the second option which not only reduces the framerate but also is not applicable for moving targets. And third, reducing the transmission frequency, which deteriorates spatial resolution. Many deep denoising techniques have been developed, but they often require clean data for training the model, which is usually only available in simulated images. Herein, a deep noise reduction approach is proposed which does not need clean training target. The model is constructed between noisy input-output pairs, and the training process interestingly converges to the clean image that is the average of noisy pairs. Experimental results on real phantom as well as ex vivo data confirm the efficacy of the proposed method for noise cancellation.