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In ultrasound (US) imaging, various types of adaptive beamforming techniqueshave been investigated to improve the resolution and contrast-to-noise ratio ofthe delay and sum (DAS) beamformers. Unfortunately, the performance of theseadaptive beamforming approaches degrade when the underlying model is notsufficiently accurate and the number of channels decreases. To address thisproblem, here we propose a deep learning-based beamformer to generatesignificantly improved images over widely varying measurement conditions andchannel subsampling patterns. In particular, our deep neural network isdesigned to directly process full or sub-sampled radio-frequency (RF) dataacquired at various subsampling rates and detector configurations so that itcan generate high quality ultrasound images using a single beamformer. Theorigin of such input-dependent adaptivity is also theoretically analyzed.Experimental results using B-mode focused ultrasound confirm the efficacy ofthe proposed methods.

Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound