  Diagnostic imaging plays a critical role in healthcare, serving as a
fundamental asset for timely diagnosis, disease staging and management as well
as for treatment choice, planning, guidance, and follow-up. Among the
diagnostic imaging options, ultrasound imaging is uniquely positioned, being a
highly cost-effective modality that offers the clinician an unmatched and
invaluable level of interaction, enabled by its real-time nature. Ultrasound
probes are becoming increasingly compact and portable, with the market demand
for low-cost pocket-sized and (in-body) miniaturized devices expanding. At the
same time, there is a strong trend towards 3D imaging and the use of
high-frame-rate imaging schemes; both accompanied by dramatically increasing
data rates that pose a heavy burden on the probe-system communication and
subsequent image reconstruction algorithms.
  With the demand for high-quality image reconstruction and signal extraction
from less (e.g unfocused or parallel) transmissions that facilitate fast
imaging, and a push towards compact probes, modern ultrasound imaging leans
heavily on innovations in powerful digital receive channel processing.
Beamforming, the process of mapping received ultrasound echoes to the spatial
image domain, naturally lies at the heart of the ultrasound image formation
chain. In this chapter on Deep Learning for Ultrasound Beamforming, we discuss
why and when deep learning methods can play a compelling role in the digital
beamforming pipeline, and then show how these data-driven systems can be
leveraged for improved ultrasound image reconstruction.
