Deep Unfolding of Full Waveform Inversion for Quantitative Ultrasound Imaging

Abstract

This paper introduces a deep unfolding-based approach for Full Waveform Inversion (FWI) in quantitative ultrasound imaging. Our technique leverages trained deep neural networks to perform an optimized gradient step that achieves superior results and significantly reduces the number of iterations required for convergence—a crucial advantage for real-world applications. While a recently proposed deep unfolding approach, MB-QRUS, demonstrated higher efficiency than traditional FWI, our experiments on both the training dataset and out-of-distribution examples show that our method significantly outperforms classical FWI and MB-QRUS in reconstruction quality under noisy conditions, while maintaining a high level of efficiency. This work enhances the potential for real-time quantitative ultrasound imaging in clinical settings and suggests broader applicability of FWI across various domains.