  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.
