# Deep learning in ultrasound imaging

arXiv: https://arxiv.org/abs/1907.02994 · HTML: https://mlsanity.com/paper/1907.02994/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 30 of 40)

- [A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis](https://mlsanity.com/paper/2201.12260/) (2022)
- [Dehazing Ultrasound using Diffusion Models](https://mlsanity.com/paper/2307.11204/) (2023)
- [Rethinking Ultrasound Augmentation: A Physics-Inspired Approach](https://mlsanity.com/paper/2105.02188/) (2021)
- [Deep Ultrasound Denoising Using Diffusion Probabilistic Models](https://mlsanity.com/paper/2306.07440/) (2023)
- [Ultrasound Image Reconstruction with Denoising Diffusion Restoration Models](https://mlsanity.com/paper/2307.15990/) (2023)
- [Deep Unrolled Recovery in Sparse Biological Imaging](https://mlsanity.com/paper/2109.14025/) (2021)
- [Learned super resolution ultrasound for improved breast lesion characterization](https://mlsanity.com/paper/2107.05270/) (2021)
- [Deep Algorithm Unrolling for Biomedical Imaging](https://mlsanity.com/paper/2108.06637/) (2021)
- [Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models](https://mlsanity.com/paper/2408.10987/) (2024)
- [A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images](https://mlsanity.com/paper/2101.09122/) (2021)
- [AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers](https://mlsanity.com/paper/2309.14492/) (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 Learning for Ultrasound Speed-of-Sound Reconstruction: Impacts of Training Data Diversity on Stability and Robustness](https://mlsanity.com/paper/2202.01208/) (2022)
- [Ultrasound Imaging based on the Variance of a Diffusion Restoration Model](https://mlsanity.com/paper/2403.15316/) (2024)
- [Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing](https://mlsanity.com/paper/2106.03686/) (2021)
- [Ultrasound Signal Processing: From Models to Deep Learning](https://mlsanity.com/paper/2204.04466/) (2022)
- [Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory](https://mlsanity.com/paper/2209.06952/) (2022)
- [Deep Unrolling for Nonconvex Robust Principal Component Analysis](https://mlsanity.com/paper/2307.05893/) (2023)
- [BrainVoxGen: Deep learning framework for synthesis of Ultrasound to MRI](https://mlsanity.com/paper/2310.08608/) (2023)
- [Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty](https://mlsanity.com/paper/2310.20618/) (2023)
- [High Volume Rate 3D Ultrasound Reconstruction with Diffusion Models](https://mlsanity.com/paper/2505.22090/) (2025)
- [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)
- [Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation](https://mlsanity.com/paper/2202.05204/) (2022)
- [Image translation of Ultrasound to Pseudo Anatomical Display by CycleGAN](https://mlsanity.com/paper/2202.08053/) (2022)
- [Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity](https://mlsanity.com/paper/2305.19069/) (2023)
- [Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation](https://mlsanity.com/paper/2311.11176/) (2023)
- [Optimization of array encoding for ultrasound imaging](https://mlsanity.com/paper/2403.00289/) (2024)

## Abstract (arXiv)

We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g. sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.