  Ultrasound B-Mode images are created from data obtained from each element in
the transducer array in a process called beamforming. The beamforming goal is
to enhance signals from specified spatial locations, while reducing signal from
all other locations. On clinical systems, beamforming is accomplished with the
delay-and-sum (DAS) algorithm. DAS is efficient but fails in patients with high
noise levels, so various adaptive beamformers have been proposed. Recently,
deep learning methods have been developed for this task. With deep learning
methods, beamforming is typically framed as a regression problem, where clean,
ground-truth data is known, and usually simulated. For in vivo data, however,
it is extremely difficult to collect ground truth information, and deep
networks trained on simulated data underperform when applied to in vivo data,
due to domain shift between simulated and in vivo data. In this work, we show
how to correct for domain shift by learning deep network beamformers that
leverage both simulated data, and unlabeled in vivo data, via a novel domain
adaption scheme. A challenge in our scenario is that domain shift exists both
for noisy input, and clean output. We address this challenge by extending
cycle-consistent generative adversarial networks, where we leverage maps
between synthetic simulation and real in vivo domains to ensure that the
learned beamformers capture the distribution of both noisy and clean in vivo
data. We obtain consistent in vivo image quality improvements compared to
existing beamforming techniques, when applying our approach to simulated
anechoic cysts and in vivo liver data.
