  In the recent past, there have been several efforts in accelerating
computationally heavy beamforming algorithms such as minimum variance
distortionless response (MVDR) beamforming to achieve real-time performance
comparable to the popular delay and sum (DAS) beamforming. This has been
achieved using a variety of neural network architectures ranging from fully
connected neural networks (FCNNs), convolutional neural networks (CNNs) and
general adversarial networks (GANs). However most of these approaches are
working with optimizations considering image level losses and hence require a
significant amount of dataset to ensure that the process of beamforming is
learned. In this work, a patch level U-Net based neural network is proposed,
where the delay compensated radio frequency (RF) patch for a fixed region in
space (e.g. 32x32) is transformed through a U-Net architecture and multiplied
with DAS apodization weights and optimized for similarity with MVDR image of
the patch. Instead of framing the beamforming problem as a regression problem
to estimate the apodization weights, the proposed approach treats the
non-linear transformation of the RF data space that can account for the data
driven weight adaptation done by the MVDR approach in the parameters of the
network. In this way, it is also observed that by restricting the input to a
patch the model will learn the beamforming pipeline as an image non-linear
transformation problem.
