  Ultrafast ultrasound (US) revolutionized biomedical imaging with its
capability of acquiring full-view frames at over 1 kHz, unlocking breakthrough
modalities such as shear-wave elastography and functional US neuroimaging. Yet,
it suffers from strong diffraction artifacts, mainly caused by grating lobes,
side lobes, or edge waves. Multiple acquisitions are typically required to
obtain a sufficient image quality, at the cost of a reduced frame rate. To
answer the increasing demand for high-quality imaging from single unfocused
acquisitions, we propose a two-step convolutional neural network (CNN)-based
image reconstruction method, compatible with real-time imaging. A low-quality
estimate is obtained by means of a backprojection-based operation, akin to
conventional delay-and-sum beamforming, from which a high-quality image is
restored using a residual CNN with multiscale and multichannel filtering
properties, trained specifically to remove the diffraction artifacts inherent
to ultrafast US imaging. To account for both the high dynamic range and the
oscillating properties of radio frequency US images, we introduce the mean
signed logarithmic absolute error (MSLAE) as a training loss function.
Experiments were conducted with a linear transducer array, in single plane-wave
(PW) imaging. Trainings were performed on a simulated dataset, crafted to
contain a wide diversity of structures and echogenicities. Extensive numerical
evaluations demonstrate that the proposed approach can reconstruct images from
single PWs with a quality similar to that of gold-standard synthetic aperture
imaging, on a dynamic range in excess of 60 dB. In vitro and in vivo
experiments show that trainings carried out on simulated data perform well in
experimental settings.
