Deep ultrasound denoising without clean data

Sobhan Goudarzi; Hassan Rivaz

doi:10.1117/12.2650041

10 April 2023 Deep ultrasound denoising without clean data

Sobhan Goudarzi, Hassan Rivaz

Proceedings Volume 12470, Medical Imaging 2023: Ultrasonic Imaging and Tomography; 124700Q (2023) https://doi.org/10.1117/12.2650041
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

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.

Citation Download Citation

Sobhan Goudarzi and Hassan Rivaz "Deep ultrasound denoising without clean data", Proc. SPIE 12470, Medical Imaging 2023: Ultrasonic Imaging and Tomography, 124700Q (10 April 2023); https://doi.org/10.1117/12.2650041

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