  In ultrasound (US) imaging, individual channel RF measurements are
back-propagated and accumulated to form an image after applying specific
delays. While this time reversal is usually implemented using a hardware- or
software-based delay-and-sum (DAS) beamformer, the performance of DAS decreases
rapidly in situations where data acquisition is not ideal. Herein, for the
first time, we demonstrate that a single data-driven adaptive beamformer
designed as a deep neural network can generate high quality images robustly for
various detector channel configurations and subsampling rates. The proposed
deep beamformer is evaluated for two distinct acquisition schemes: focused
ultrasound imaging and planewave imaging. Experimental results showed that the
proposed deep beamformer exhibit significant performance gain for both focused
and planar imaging schemes, in terms of contrast-to-noise ratio and structural
similarity.
