  Medical ultrasound (US) is a widespread imaging modality owing its popularity
to cost efficiency, portability, speed, and lack of harmful ionizing radiation.
In this paper, we demonstrate that replacing the traditional ultrasound
processing pipeline with a data-driven, learnable counterpart leads to
significant improvement in image quality. Moreover, we demonstrate that greater
improvement can be achieved through a learning-based design of the transmitted
beam patterns simultaneously with learning an image reconstruction pipeline. We
evaluate our method on an in-vivo first-harmonic cardiac ultrasound dataset
acquired from volunteers and demonstrate the significance of the learned
pipeline and transmit beam patterns on the image quality when compared to
standard transmit and receive beamformers used in high frame-rate US imaging.
We believe that the presented methodology provides a fundamentally different
perspective on the classical problem of ultrasound beam pattern design.
