Training improvements for ultrasound beamforming with deep neural networks

Abstract

This paper investigates practical considerations of training ultrasound deep neural network (DNN) beamformers. First, we studied training DNNs using the combination of multiple point target responses instead of single point target responses. Next, we demonstrated the effect of different hyperparameter settings on ultrasound image quality for simulated scans. This study also showed that DNN beamforming was robust to electronic noise. Next, we showed that mean squared error validation loss was not a good predictor for image quality for simulation, phantom, and in vivo scans. As an alternative to validation loss for selecting DNN beamformers, we studied image quality in physical phantom and in vivo scans and demonstrated that DNN beamformer image quality in these settings was correlated to DNN beamformer image quality in simulated images. These findings suggest that simulated image quality can be used to select DNN beamformers. Finally, we studied the effect of dataset size on DNN beamformer image quality in simulation, physical phantom, and in vivo scans. We interpret the results in terms of recent work on the scaling of deep learning. Overall, the results in this paper show that DNN beamforming has significant potential for improving B-mode image quality.