  In portable, three dimensional, and ultra-fast ultrasound imaging systems,
there is an increasing demand for the reconstruction of high quality images
from a limited number of radio-frequency (RF) measurements due to receiver (Rx)
or transmit (Xmit) event sub-sampling. However, due to the presence of side
lobe artifacts from RF sub-sampling, the standard beamformer often produces
blurry images with less contrast, which are unsuitable for diagnostic purposes.
Existing compressed sensing approaches often require either hardware changes or
computationally expensive algorithms, but their quality improvements are
limited. To address this problem, here we propose a novel deep learning
approach that directly interpolates the missing RF data by utilizing redundancy
in the Rx-Xmit plane. Our extensive experimental results using sub-sampled RF
data from a multi-line acquisition B-mode system confirm that the proposed
method can effectively reduce the data rate without sacrificing image quality.
