SSI-Net: A hybrid physics-constrained deep learning framework for quantitative ultrasound speed-of-sound reconstruction

By embedding an exact wave solver into a trainable architecture, SSI‑Net combines the representational capacity of deep learning with strict physical consistency. It provides a robust, efficient, and clinically translatable solution for quantitative SoS imaging, offering a promising tool for tissue characterization in diagnostic ultrasound.

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Publication Details

Journal
Physics in Medicine and Biology
Published
2026-03-17
DOI
https://doi.org/10.1088/1361-6560/ae5374
Primary Topic
Ultrasound Imaging and Elastography
Type
article
Field-Weighted Citation Impact
0.00
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article

SSI-Net: A hybrid physics-constrained deep learning framework for quantitative ultrasound speed-of-sound reconstruction

Yangjie Sheng, Zhangshuo Gao, Qian Jiang, Zheng Sun
Physics in Medicine and Biology
Ultrasound Imaging and Elastography
article

SSI-Net: A hybrid physics-constrained deep learning framework for quantitative ultrasound speed-of-sound reconstruction

Yangjie Sheng, Zhangshuo Gao, Qian Jiang, Zheng Sun
article en

Abstract

By embedding an exact wave solver into a trainable architecture, SSI‑Net combines the representational capacity of deep learning with strict physical consistency. It provides a robust, efficient, and clinically translatable solution for quantitative SoS imaging, offering a promising tool for tissue characterization in diagnostic ultrasound.

Physics in Medicine and Biology
North China Electric Power University (CN)
Openalex Percentile: Top 19%
Ultrasound Imaging and Elastography
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