  Ultrasound (US) imaging is based on the time-reversal principle, in which
individual channel RF measurements are back-propagated and accumulated to form
an image after applying specific delays. While this time reversal is usually
implemented as a delay-and-sum (DAS) beamformer, the image quality quickly
degrades as the number of measurement channels decreases. To address this
problem, various types of adaptive beamforming techniques have been proposed
using predefined models of the signals. However, the performance of these
adaptive beamforming approaches degrade when the underlying model is not
sufficiently accurate. Here, we demonstrate for the first time that a single
universal deep beamformer trained using a purely data-driven way can generate
significantly improved images over widely varying aperture and channel
subsampling patterns. In particular, we design an end-to-end deep learning
framework that can directly process sub-sampled RF data acquired at different
subsampling rate and detector configuration to generate high quality ultrasound
images using a single beamformer. Experimental results using B-mode focused
ultrasound confirm the efficacy of the proposed methods.
