# A Deep Learning Framework for Single-Sided Sound Speed Inversion in Medical Ultrasound

arXiv: https://arxiv.org/abs/1810.00322 · HTML: https://mlsanity.com/paper/1810.00322/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2018.

## Built on by (most cited first, 10 of 10)

- [Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning](https://mlsanity.com/paper/2302.03064/) (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)
- [Phase Aberration Correction without Reference Data: An Adaptive Mixed Loss Deep Learning Approach](https://mlsanity.com/paper/2303.05747/) (2023)
- [Learning-based sound speed estimation and aberration correction in linear-array photoacoustic imaging](https://mlsanity.com/paper/2306.11034/) (2023)
- [AutoSpeed: A Linked Autoencoder Approach for Pulse-Echo Speed-of-Sound Imaging for Medical Ultrasound](https://mlsanity.com/paper/2207.02392/) (2022)
- [Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging](https://mlsanity.com/paper/2410.08329/) (2024)
- [Uncertainty Estimation for Trust Attribution to Speed-of-Sound Reconstruction with Variational Networks](https://mlsanity.com/paper/2504.11307/) (2025)
- [DA-UCT: Self-Supervised Domain-Adaptive Ultrasound Computed Tomography for Rapid Musculoskeletal Sound Speed Reconstruction](https://mlsanity.com/paper/2605.25024/) (2026)
- [IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data](https://mlsanity.com/paper/2607.22351/) (2026)

## Abstract (arXiv)

Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to high-end ultrasound hardware due to high power requirements; are extremely sensitive to patient and sonographer motion, and generally, suffer from low frame rates. Motivated by research and theory showing that longitudinal wave sound speed carries similar diagnostic abilities to shear wave imaging, we present an alternative approach using single sided pressure-wave sound speed measurements from channel data. Methods: In this paper, we present a single-sided sound speed inversion solution using a fully convolutional deep neural network. We use simulations for training, allowing the generation of limitless ground truth data. Results: We show that it is possible to invert for longitudinal sound speed in soft tissue at high frame rates. We validate the method on simulated data. We present highly encouraging results on limited real data. Conclusion: Sound speed inversion on channel data has significant potential, made possible in real time with deep learning technologies. Significance: Specialized shear wave ultrasound systems remain inaccessible in many locations. longitudinal sound speed and deep learning technologies enable an alternative approach to diagnosis based on tissue elasticity. High frame rates are possible.