Physics-Based Learning of the Wave Speed Landscape in Complex Media

TL;DR

This paper introduces a physics-based deep learning method to reconstruct large-scale wave velocity landscapes in complex media using reflection matrix data, overcoming limitations of traditional reflection imaging.

Contribution

It presents a novel matrix imaging approach that models wave propagation as a trainable multi-layer network, enabling large-scale velocity mapping from reflection data.

Findings

Validated on tissue-mimicking phantoms and human breast tissues

Demonstrated potential for tumor detection and characterization

Applicable to various wave types and media

Abstract

Wave velocity is a key parameter for imaging complex media, but in vivo measurements are typically limited to reflection geometries, where only backscattered waves from short-scale heterogeneities are accessible. As a result, conventional reflection imaging fails to recover large-scale variations of the wave velocity landscape. Here we show that matrix imaging overcomes this limitation by exploiting the quality of wave focusing as an intrinsic guide star. We model wave propagation as a trainable multi-layer network that leverages optimization and deep learning tools to infer the wave velocity distribution. We validate this approach through ultrasound experiments on tissue-mimicking phantoms and human breast tissues, demonstrating its potential for tumour detection and characterization. Our method is broadly applicable to any kind of waves and media for which a reflection matrix can be measured.