  We consider deep learning strategies in ultrasound systems, from the
front-end to advanced applications. Our goal is to provide the reader with a
broad understanding of the possible impact of deep learning methodologies on
many aspects of ultrasound imaging. In particular, we discuss methods that lie
at the interface of signal acquisition and machine learning, exploiting both
data structure (e.g. sparsity in some domain) and data dimensionality (big
data) already at the raw radio-frequency channel stage. As some examples, we
outline efficient and effective deep learning solutions for adaptive
beamforming and adaptive spectral Doppler through artificial agents, learn
compressive encodings for color Doppler, and provide a framework for structured
signal recovery by learning fast approximations of iterative minimization
problems, with applications to clutter suppression and super-resolution
ultrasound. These emerging technologies may have considerable impact on
ultrasound imaging, showing promise across key components in the receive
processing chain.
