  Traditional beamforming of medical ultrasound images relies on sampling rates
significantly higher than the actual Nyquist rate of the received signals. This
results in large amounts of data to store and process, imposing hardware and
software challenges on the development of ultrasound machinery and algorithms,
and impacting the resulting performance. In light of the capabilities
demonstrated by deep learning methods over the past years across a variety of
fields, including medical imaging, it is natural to consider their ability to
recover high-quality ultrasound images from partial data. Here, we propose an
approach for deep-learning based reconstruction of B-mode images from
temporally and spatially sub-sampled channel data. We begin by considering
sub-Nyquist sampled data, time-aligned in the frequency domain and transformed
back to the time domain. The data is further sampled spatially, so that only a
subset of the received signals is acquired. The partial data is used to train
an encoder-decoder convolutional neural network, using as targets
minimum-variance (MV) beamformed signals that were generated from the original,
fully-sampled data. Our approach yields high-quality B-mode images, with higher
resolution than previously proposed reconstruction approaches (NESTA) from
compressed data as well as delay-and-sum beamforming (DAS) of the fully-sampled
data. In terms of contrast to noise ratio, our results are comparable to MV
beamforming of the fully-sampled data, thus enabling better and more efficient
imaging than is mostly used in clinical practice today.
