Application-specific pulse-echo ultrasound image reconstruction using neural networks

Abstract

Deep neural networks (DNNs) have recently emerged as powerful function approximators that can learn to transform given inputs into desired outputs when provided with enough training samples. Ultrasound B-mode images are traditionally formed using the delay-and-sum beamformer to display the echogenicity (i.e., backscattering strength) of the medium. Advanced beamformers are often designed to improve indirect metrics of image quality such as lesion detectability and point target resolution. Here, we describe how DNNs can be trained directly to minimize errors in the output images as compared to the ground truth targets and present recent work with DNNs for two specific applications. A simple convolutional DNN was trained to accurately estimate echogenicity for B-mode imaging using Field II simulations; the DNN produced speckle-reduced B-mode images of data acquired in simulations, phantoms, and in vivo. Similarly, a similar DNN was trained to detect targeted microbubbles for ultrasound molecular imaging; the DNN nondestructively detected microbubbles with similar performance to a state-of-the-art destructive imaging technique while enabling real-time molecular imaging.