SWENet: a physics-informed deep neural network (PINN) for shear wave elastography

TL;DR

SWENet is a physics-informed neural network approach that improves the accuracy of shear wave elastography in inhomogeneous soft tissues by leveraging wave motion features and multi-source data integration.

Contribution

The paper introduces SWENet, a novel PINN-based SWE method that accurately infers spatial elastic properties in inhomogeneous materials, surpassing traditional techniques.

Findings

Accurately identified shear moduli in soft composites with millimeter-sized inclusions.

Demonstrated improved resolution and accuracy over conventional SWE methods.

Validated effectiveness through simulations and phantom experiments.

Abstract

Shear wave elastography (SWE) enables the measurement of elastic properties of soft materials, including soft tissues, in a non-invasive manner and finds broad applications in a variety of disciplines. The state-of-the-art SWE methods commercialized in various instruments rely on the measurement of shear wave velocities to infer material parameters and have relatively low resolution and accuracy for inhomogeneous soft materials due to the complexity of wave fields. In the present study, we overcome this challenge by proposing a physics-informed neural network (PINN)-based SWE (SWENet) method considering the merits of PINN in solving an inverse problem. The spatial variation of elastic properties of inhomogeneous materials has been defined in governing equations, which are encoded in PINN as loss functions. Snapshots of wave motion inside a local region have been used to train the neural networks, and during this course, the spatial distribution of elastic properties is inferred simultaneously. Both finite element simulations and tissue-mimicking phantom experiments have been performed to validate the method. Our results show that the shear moduli of soft composites consisting of matrix and inclusions of several millimeters in cross-section dimensions with either regular or irregular geometries can be identified with good accuracy. The advantages of the SWENet over conventional SWE methods consist of using more features of the wave motion in inhomogeneous soft materials and enabling seamless integration of multi-source data in the inverse analysis. Given the advantages of the reported method, it may find applications including but not limited to mechanical characterization of artificial soft biomaterials, imaging elastic properties of nerves in vivo, and differentiating small malignant tumors from benign ones by quantitatively measuring their distinct stiffnesses.