# Deep Learning-based Universal Beamformer for Ultrasound Imaging

arXiv: https://arxiv.org/abs/1904.02843 · HTML: https://mlsanity.com/paper/1904.02843/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Learning for Ultrasound Beamforming](https://mlsanity.com/paper/2109.11431/) (2021)
- [Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning](https://mlsanity.com/paper/2202.08262/) (2022)
- [Blind Ultrasound Image Enhancement via Self-Supervised Physics-Guided Degradation Modeling](https://mlsanity.com/paper/2601.21856/) (2026)
- [Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN](https://mlsanity.com/paper/2112.02896/) (2021)

## Abstract (arXiv)

In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented using a hardware- or software-based delay-and-sum (DAS) beamformer, the performance of DAS decreases rapidly in situations where data acquisition is not ideal. Herein, for the first time, we demonstrate that a single data-driven adaptive beamformer designed as a deep neural network can generate high quality images robustly for various detector channel configurations and subsampling rates. The proposed deep beamformer is evaluated for two distinct acquisition schemes: focused ultrasound imaging and planewave imaging. Experimental results showed that the proposed deep beamformer exhibit significant performance gain for both focused and planar imaging schemes, in terms of contrast-to-noise ratio and structural similarity.