Using domain adaptive deep neural networks to improve transthoracic echocardiography

Abstract

We previously showed that domain adaptive deep neural networks (DNNs) can outperform delay-and-sum (DAS) beamforming in the context of abdominal imaging. We hypothesize the ability of our domain adaptive DNN framework to be applied to transthoracic echocardiography (TTE). We also propose architectural improvements, such as leveraging an encoder-decoder structure and skip connections, to further improve ultrasound image quality for echocardiography tasks such as the detection of thrombi in the left atrial appendage (LAA). DNN training data utilized simulated and in vivo cardiac data. Simulated anechoic and hypoechoic cysts with various amounts of clutter were generated through Field II and in vivo data was collected by scanning patients at Vanderbilt University Medical Center. Fundamental frequency TTE data from five separate cases were processed with DAS, ADMIRE, the baseline model, and multiple models with modified architectures. We found that even when varying the amount of training data, the DNNs consistently achieved higher generalized contrast-to-noise (gCNR) and contrast ratio (CR) but lower contrast-to-noise ratio when compared to DAS. The best-performing beamformer was one DNN with our architectural improvements, achieving higher average gCNR and CR values of .907 and 48.30 dB compared to the baseline DNN values of .788 and 39.45 dB, and DAS values of .717 and 14.08 dB. Our results demonstrate that our domain adaptive DNN can effectively be applied in the context of transthoracic cardiology, and an encoder-decoder architecture with skip connections can result in even more improvements. Further advancements may improve image quality even more.