  Deep learning methods can be found in many medical imaging applications.
Recently, those methods were applied directly to the RF ultrasound
multi-channel data to enhance the quality of the reconstructed images. In this
paper, we apply a deep neural network to medical ultrasound imaging in the
beamforming stage. Specifically, we train the network using simulated
multi-channel data from two arrays with different sizes, using a variety of
direction of arrival (DOA) angles, and test its generalization performance on
real cardiac data. We demonstrate that our method can be used to improve image
quality over standard methods, both in terms of resolution and contrast.
Alternatively, it can be used to reduce the number of elements in the array,
while maintaining the image quality. The utility of our method is demonstrated
on both simulated and real data.
