SWENet: A Physics-Informed Deep Neural Network (PINN) for Shear Wave Elastography

• Published in: IEEE transactions on medical imaging 2024-04, Vol.43 (4), p.1434-1448
• Main Authors: Yin, Ziying, Li, Guo-Yang, Zhang, Zhaoyi, Zheng, Yang, Cao, Yanping
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Biological neural networks; Biological tissues; Brain; deep learning; Elastic properties; Elasticity Imaging Techniques - methods; Elastography; Heterogeneity; Inclusions; inverse problem; Magnetic resonance; Mathematical models; Mechanical properties; Neural networks; Neural Networks, Computer; phantom and ex vivo experiments; Phantoms; Physics; physics-informed neural network; Shear modulus; Shear wave elastography; Solid tumors; Spatial variations; Training; tumor biomechanics; Ultrasonography; Wave diffraction
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2023.3338178

Shear wave elastography (SWE) enables the measurement of elastic properties of soft materials in a non-invasive manner and finds broad applications in various disciplines. The state-of-the-art SWE methods rely on the measurement of local shear wave speeds to infer material parameters and suffer from wave diffraction when applied to soft materials with strong heterogeneity. In the present study, we overcome this challenge by proposing a physics-informed neural network (PINN)-based SWE (SWENet) method. The spatial variation of elastic properties of inhomogeneous materials has been introduced in the governing equations, which are encoded in SWENet as loss functions. Snapshots of wave motions have been used to train neural networks, and during this course, the elastic properties within a region of interest illuminated by shear waves are inferred simultaneously. We performed finite element simulations, tissue-mimicking phantom experiments, and ex vivo experiments 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 excellent accuracy. The advantages of the SWENet over conventional SWE methods consist of using more features of the wave motions and enabling seamless integration of multi-source data in the inverse analysis. Given the advantages of SWENet, it may find broad applications where full wave fields get involved to infer heterogeneous mechanical properties, such as identifying small solid tumors with ultrasound SWE, and differentiating gray and white matters of the brain with magnetic resonance elastography.