  Beamforming is an essential step in the ultrasound image formation pipeline
and has recently attracted growing interest. An important goal of beamforming
is to increase the image spatial resolution, or in other words to narrow down
the system point spread function. In parallel to beamforming approaches,
deconvolution methods have also been explored in ultrasound imaging to mitigate
the adverse effects of PSF. Unfortunately, these two steps have only been
considered separately in a sequential approach. Herein, a novel framework for
unifying beamforming and deconvolution in ultrasound image reconstruction is
introduced. More specifically, the proposed formulation is a regularized
inverse problem including two linear models for beamforming and deconvolution
plus additional sparsity constraint. We take advantage of the alternating
direction method of multipliers algorithm to find the solution of the joint
optimization problem. The performance evaluation is presented on a set of
publicly available simulations, real phantoms, and in vivo data. Furthermore,
the superiority of the proposed approach in comparison with the sequential
approach as well as each of the other beamforming and deconvolution approaches
alone is also shown. Results demonstrate that our approach combines the
advantages of both methods and offers ultrasound images with superior
resolution and contrast.
