  Medical ultrasound provides images which are the spatial map of the tissue
echogenicity. Unfortunately, an ultrasound image is a low-quality version of
the expected Tissue Reflectivity Function (TRF) mainly due to the non-ideal
Point Spread Function (PSF) of the imaging system. This paper presents a novel
beamforming approach based on deep learning to get closer to the ideal PSF in
Plane-Wave Imaging (PWI). The proposed approach is designed to reconstruct the
desired TRF from echo traces acquired by transducer elements using only a
single plane-wave transmission. In this approach, first, an ideal model for the
TRF is introduced by setting the imaging PSF as a sharp Gaussian function.
Then, a mapping function between the pre-beamformed Radio-Frequency (RF)
channel data and the proposed TRF is constructed using deep learning. Network
architecture contains multi-resolution decomposition and reconstruction using
wavelet transform for effective recovery of high-frequency content of the
desired TRF. Inspired by curriculum learning, we exploit step by step training
from coarse (mean square error) to fine ($\ell_{0.2}$) loss functions. The
proposed method is trained on a large number of simulation ultrasound data with
the ground-truth echogenicity map extracted from real photographic images. The
performance of the trained network is evaluated on the publicly available
simulation and \textit{in vivo} test data without any further fine-tuning.
Simulation test results confirm that the proposed method reconstructs images
with a high quality in terms of resolution and contrast, which are also
visually similar to the proposed ground-truth image. Furthermore, \textit{in
vivo} results show that the trained mapping function preserves its performance
in the new domain. Therefore, the proposed approach maintains high resolution,
contrast, and framerate simultaneously.
