Neural networks as an applied tool for ultrasound beamforming and image reconstruction

Abstract

In recent years, the computer science community has made available its artificial intelligence tools to the public at-large. Freely available online courses, software libraries, and databases have made it easy to incorporate artificial intelligence, and specifically machine learning, into many research areas. In medical imaging, this has led to a proliferation of machine learning-based research methodologies in image reconstruction tasks, whether appropriate or not. In this lecture, we review machine learning as a tool for ultrasound beamforming. We discuss the selection of an appropriate problem to be solved with machine learning, survey common and popular implementations of neural networks, and describe best practices and common pitfalls. We visit a few misconceptions about machine learning in ultrasound imaging and examine the recent literature in the field of ultrasound beamforming and image reconstruction.