# Adaptive Ultrasound Beamforming using Deep Learning

arXiv: https://arxiv.org/abs/1909.10342 · HTML: https://mlsanity.com/paper/1909.10342/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2019.

## Built on by (most cited first, 18 of 18)

- [Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2307.15990/) (2023)
- [Deep Task-Based Analog-to-Digital Conversion](https://mlsanity.com/paper/2201.12634/) (2022)
- [SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation](https://mlsanity.com/paper/2306.02271/) (2023)
- [Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image](https://mlsanity.com/paper/2103.01263/) (2021)
- [Ultrasound Speckle Suppression and Denoising using MRI-derived Normalizing Flow Priors](https://mlsanity.com/paper/2112.13110/) (2021)
- [Accelerated Intravascular Ultrasound Imaging using Deep Reinforcement Learning](https://mlsanity.com/paper/2201.09522/) (2022)
- [Deep Coherence Learning: An Unsupervised Deep Beamformer for High Quality Single Plane Wave Imaging in Medical Ultrasound](https://mlsanity.com/paper/2311.11169/) (2023)
- [Fast Sampling generative model for Ultrasound image reconstruction](https://mlsanity.com/paper/2312.09510/) (2023)
- [Ultrasound Signal Processing: From Models to Deep Learning](https://mlsanity.com/paper/2204.04466/) (2022)
- [Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty](https://mlsanity.com/paper/2310.20618/) (2023)
- [Single PW takes a shortcut to compound PW in US imaging](https://mlsanity.com/paper/2312.09514/) (2023)
- [Ultrasound Image Enhancement with the Variance of Diffusion Models](https://mlsanity.com/paper/2409.11380/) (2024)
- [Near Field Localization via AI-Aided Subspace Methods](https://mlsanity.com/paper/2504.00599/) (2025)
- [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)
- [Tiny-VBF: Resource-Efficient Vision Transformer based Lightweight Beamformer for Ultrasound Single-Angle Plane Wave Imaging](https://mlsanity.com/paper/2311.12082/) (2023)
- [Experimental Validation of Ultrasound Beamforming with End-to-End Deep Learning for Single Plane Wave Imaging](https://mlsanity.com/paper/2404.14188/) (2024)
- [Null-Space Diffusion Restoration with Adaptive Uncertainty-Guided Fusion for Ultrasound Speckle Reduction](https://mlsanity.com/paper/2608.29820/) (2026)

## Abstract (arXiv)

Biomedical imaging is unequivocally dependent on the ability to reconstruct interpretable and high-quality images from acquired sensor data. This reconstruction process is pivotal across many applications, spanning from magnetic resonance imaging to ultrasound imaging. While advanced data-adaptive reconstruction methods can recover much higher image quality than traditional approaches, their implementation often poses a high computational burden. In ultrasound imaging, this burden is significant, especially when striving for low-cost systems, and has motivated the development of high-resolution and high-contrast adaptive beamforming methods. Here we show that deep neural networks that adopt the algorithmic structure and constraints of adaptive signal processing techniques can efficiently learn to perform fast high-quality ultrasound beamforming using very little training data. We apply our technique to two distinct ultrasound acquisition strategies (plane wave, and synthetic aperture), and demonstrate that high image quality can be maintained when measuring at low data-rates, using undersampled array designs. Beyond biomedical imaging, we expect that the proposed deep~learning based adaptive processing framework can benefit a variety of array and signal processing applications, in particular when data-efficiency and robustness are of importance.