# Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound

arXiv: https://arxiv.org/abs/1907.10257 · HTML: https://mlsanity.com/paper/1907.10257/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2019.

## Built on by (most cited first, 12 of 12)

- [Roadmap on Signal Processing for Next Generation Measurement Systems](https://mlsanity.com/paper/2111.02493/) (2021)
- [Deep Coherence Learning: An Unsupervised Deep Beamformer for High Quality Single Plane Wave Imaging in Medical Ultrasound](https://mlsanity.com/paper/2311.11169/) (2023)
- [Deep Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustness](https://mlsanity.com/paper/2202.01208/) (2022)
- [Ultrasound Signal Processing: From Models to Deep Learning](https://mlsanity.com/paper/2204.04466/) (2022)
- [Single Plane-Wave Imaging using Physics-Based Deep Learning](https://mlsanity.com/paper/2109.03661/) (2021)
- [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)
- [NOIR: Neural Operator mapping for Implicit Representations](https://mlsanity.com/paper/2603.13118/) (2026)
- [Deep Learning for Multi-View Ultrasonic Image Fusion](https://mlsanity.com/paper/2109.03616/) (2021)
- [Patch Based Transformation for Minimum Variance Beamformer Image Approximation Using Delay and Sum Pipeline](https://mlsanity.com/paper/2110.10220/) (2021)
- [Tunable Image Quality Control of 3-D Ultrasound using Switchable CycleGAN](https://mlsanity.com/paper/2112.02896/) (2021)
- [Physics-Guided Multi-Objective Deep Learning for Ultrasound RF Data Interpolation in Resource-Constrained Imaging](https://mlsanity.com/paper/2609.28775/) (2026)

## Abstract (arXiv)

In ultrasound (US) imaging, various types of adaptive beamforming techniques have been investigated to improve the resolution and contrast-to-noise ratio of the delay and sum (DAS) beamformers. Unfortunately, the performance of these adaptive beamforming approaches degrade when the underlying model is not sufficiently accurate and the number of channels decreases. To address this problem, here we propose a deep learning-based beamformer to generate significantly improved images over widely varying measurement conditions and channel subsampling patterns. In particular, our deep neural network is designed to directly process full or sub-sampled radio-frequency (RF) data acquired at various subsampling rates and detector configurations so that it can generate high quality ultrasound images using a single beamformer. The origin of such input-dependent adaptivity is also theoretically analyzed. Experimental results using B-mode focused ultrasound confirm the efficacy of the proposed methods.