# Ultrasound Signal Processing: From Models to Deep Learning

arXiv: https://arxiv.org/abs/2204.04466 · HTML: https://mlsanity.com/paper/2204.04466/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 127 references that are in the map.
- First posted on arXiv 9 Apr 2022.

## Builds on (most cited first, 28 of 28)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound](https://mlsanity.com/paper/1811.08252/) (2018)
- [Deep learning in ultrasound imaging](https://mlsanity.com/paper/1907.02994/) (2019)
- [The AI Index 2021 Annual Report](https://mlsanity.com/paper/2103.06312/) (2021)
- [Adaptive Ultrasound Beamforming using Deep Learning](https://mlsanity.com/paper/1909.10342/) (2019)
- [Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging](https://mlsanity.com/paper/1908.05764/) (2019)
- [Super-resolution Ultrasound Localization Microscopy through Deep Learning](https://mlsanity.com/paper/1804.07661/) (2018)
- [Adaptive and Compressive Beamforming Using Deep Learning for Medical Ultrasound](https://mlsanity.com/paper/1907.10257/) (2019)
- [A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound](https://mlsanity.com/paper/1810.00322/) (2018)
- [Towards CT-quality Ultrasound Imaging using Deep Learning](https://mlsanity.com/paper/1710.06304/) (2017)
- [Learned super resolution ultrasound for improved breast lesion characterization](https://mlsanity.com/paper/2107.05270/) (2021)
- [Image Reconstruction via Variational Network for Real-Time Hand-Held Sound-Speed Imaging](https://mlsanity.com/paper/1807.07416/) (2018)
- [Training Variational Networks with Multi-Domain Simulations: Speed-of-Sound Image Reconstruction](https://mlsanity.com/paper/2006.14395/) (2020)
- [Deep-Learning Based Adaptive Ultrasound Imaging from Sub-Nyquist Channel Data](https://mlsanity.com/paper/2008.02628/) (2020)
- [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)
- [Beamforming through regularized inverse problems in ultrasound medical imaging](https://mlsanity.com/paper/1507.08184/) (2015)
- [Ultrasound Aberration Correction based on Local Speed-of-Sound Map Estimation](https://mlsanity.com/paper/1909.10254/) (2019)
- [Sparse Array Design via Fractal Geometries](https://mlsanity.com/paper/2001.01217/) (2020)
- [Deep neural networks for non-linear model-based ultrasound reconstruction](https://mlsanity.com/paper/1807.01224/) (2018)
- [Distortion matrix approach for ultrasound imaging of random scattering media](https://mlsanity.com/paper/1912.05417/) (2019)
- [3D B-mode ultrasound speckle reduction using deep learning for 3D registration applications](https://mlsanity.com/paper/2008.01147/) (2020)
- [Deep Learning for Ultrasound Beamforming](https://mlsanity.com/paper/2109.11431/) (2021)
- [Deep Proximal Learning for High-Resolution Plane Wave Compounding](https://mlsanity.com/paper/2112.12410/) (2021)

## Built on by (most cited first, 2 of 2)

- [Dehazing Ultrasound using Diffusion Models](https://mlsanity.com/paper/2307.11204/) (2023)
- [IRSDE-Despeckle: A Physics-Grounded Diffusion Model for Generalizable Ultrasound Despeckling](https://mlsanity.com/paper/2602.22717/) (2026)

## Abstract (arXiv)

Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Conventionally, reconstruction algorithms where derived from physical principles. These algorithms rely on assumptions and approximations of the underlying measurement model, limiting image quality in settings were these assumptions break down. Conversely, more sophisticated solutions based on statistical modelling, careful parameter tuning, or through increased model complexity, can be sensitive to different environments. Recently, deep learning based methods, which are optimized in a data-driven fashion, have gained popularity. These model-agnostic techniques often rely on generic model structures, and require vast training data to converge to a robust solution. A relatively new paradigm combines the power of the two: leveraging data-driven deep learning, as well as exploiting domain knowledge. These model-based solutions yield high robustness, and require less parameters and training data than conventional neural networks. In this work we provide an overview of these techniques from recent literature, and discuss a wide variety of ultrasound applications. We aim to inspire the reader to further research in this area, and to address the opportunities within the field of ultrasound signal processing. We conclude with a future perspective on model-based deep learning techniques for medical ultrasound.