Physics Informed Deep Unfolded Full Waveform Inversion for Edema Detection

TL;DR

This paper introduces DUFWI, a deep learning-enhanced physics-based inversion method for real-time edema detection using ultrasound, outperforming traditional techniques in quality and efficiency.

Contribution

The paper presents DUFWI, a novel deep unfolded FWI approach that achieves rapid, high-quality SoS reconstruction for edema detection, combining physics fidelity with data-driven learning.

Findings

DUFWI outperforms classical FWI and MB-QRUS in reconstruction quality.

DUFWI achieves real-time SoS imaging with fewer iterations.

Validated on both simulated and hardware ultrasound data.

Abstract

Edema is a potential indicator of underlying pathological changes. However, its low-contrast signature is often masked in conventional B-mode imaging by strong scatterers, making reliable detection challenging. Ultrasound (US) provides a non-invasive, non-ionizing, and cost-efficient imaging option that is widely used. Conventional techniques, which rely on beamforming, often lack sufficient physical interpretability. Quantitative US (QUS) can estimate physical properties such as the speed of sound (SoS) and density by solving a physics-based inverse problem directly on the measured US wavefields, i.e., the raw per-element channel data (CD), to recover their spatial distribution. However, state-of-the-art physics-based inversion methods, including full waveform inversion (FWI) and model-based quantitative radar and US (MB-QRUS), are computationally intensive and susceptible to local minima, which constrains their clinical utility. We introduce deep unfolded FWI (DUFWI), a physics-faithful unfolded iterative inversion method that exhibits FWI-like refinement behavior while learning the update rule from data, requiring only a small number of iterations for real-time SoS reconstruction. Across both simulated datasets and hardware measurements acquired with a Verasonics US system, the DUFWI significantly outperforms classical FWI and MB-QRUS in reconstruction quality while maintaining high computational efficiency. These results demonstrate real-time edema diagnosis in both simulation and hardware experiments, with phantom-based validation using cylindrical rods, supporting practical deployment under typical US imaging setting.