EchoNet-Quality: Denoising Echocardiograms via Deep Generative Modeling of Ultrasound Noise

원문 초록

Echocardiography (echo), or cardiac ultrasound, is the most widely used imaging modality for cardiac form and function due to its relatively low cost, rapid acquisition time, and non-invasive nature. However, ultrasound acquisitions are often limited by artifacts and noise that hinder diagnostic interpretation in clinical settings. Existing methodologies for denoising echos consist solely of traditional filtering-based algorithms or deep learning methods developed on radio-frequency (RF) signals which prevents clinical applicability and scalability. To address these limitations, we introduce the first deep generative model capable of simulating ultrasound noise developed on B-mode data. Using this generative model, we develop a synthetic dataset of paired clean and noisy echo images to train a downstream model for real-world image denoising and demonstrate state-of-the-art performance in both internal and external experiments. In both held-out test sets, our method results in echo images with higher gCNR in comparison to noisy image counterparts and images derived from a comparable method which is consistent with provided visual comparisons. Our experiments showcase the potential of our method for future clinical use to improve the quality of echo acquisitions. To encourage further research into the field, we release our source code and model weights at https://github.com/echonet/image_quality.