# Patch Based Transformation for Minimum Variance Beamformer Image Approximation Using Delay and Sum Pipeline

arXiv: https://arxiv.org/abs/2110.10220 · HTML: https://mlsanity.com/paper/2110.10220/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep learning in ultrasound imaging](https://mlsanity.com/paper/1907.02994/) (2019)
- [Adaptive Ultrasound Beamforming using Deep Learning](https://mlsanity.com/paper/1909.10342/) (2019)
- [So you think you can DAS? A viewpoint on delay-and-sum beamforming](https://mlsanity.com/paper/2007.11960/) (2020)
- [Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound](https://mlsanity.com/paper/1907.10257/) (2019)
- [Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning](https://mlsanity.com/paper/1712.06096/) (2017)
- [Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN](https://mlsanity.com/paper/2012.14142/) (2020)
- [Deep-Learning Based Adaptive Ultrasound Imaging from Sub-Nyquist Channel Data](https://mlsanity.com/paper/2008.02628/) (2020)
- [Complex Convolutional Neural Networks for Ultrafast Ultrasound Image Reconstruction from In-Phase/Quadrature Signal](https://mlsanity.com/paper/2009.11536/) (2020)
- [Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal](https://mlsanity.com/paper/2006.14773/) (2020)

## Abstract (arXiv)

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.