Improving Image Quality of Single Plane Wave Ultrasound via Deep Learning Based Channel Compounding
Sven Rothlübbers, Hannah Strohm, Klaus Eickel, Jürgen Jenne, Vincent Kuhlen, David Sinden, Matthias Günther
Abstract
The emergence of data driven approaches such as Deep Learning has led to novel application of various aspects of science and engineering. It has recently entered the field of ultrasound image beamforming. In this work we investigate neural networks tailored to create images of the quality of multiple compounded plane wave excitations from the data of the central angle (0°) excitation only. The proposed network is used to produce pixel-wise weights to weigh a standard delay-and-sum image from all channel data available to a pixel. It is found to produce higher quality images than the classical reference reconstruction from the 0° angle data.