Unsupervised Physics-Inspired Shear Wave Speed Estimation in Ultrasound Elastography

Shear wave elastography (SWE) is a promising tool to quantify tissue stiffness variations with increasing applications in tissue characterization. In SWE, the tissue is excited by an acoustic radiation force pulse sequence induced by an ultrasound probe. The generated shear waves propagate laterally away from the push location. The shear wave speed (SWS) can be measured to estimate elasticity, which is a physical property that can be used to characterize the tissue. SWS estimation requires two steps: speckle tracking from radiofrequency (RF)/IQ data to obtain particle displacement or velocity, and SWS estimation from the estimated velocity, which aims to find the speed of wave propagating in the lateral direction. The SWS can be calculated by comparing the velocity-time profiles at two locations separated by a few millimeters. In the supervised deep learning methods of SWS estimation, simulation data generated by finite element analysis is employed to train the network. However, the computational cost and complexity of modeling the wave propagation contribute to the limited practicality of supervised methods. In this paper, we present an unsupervised physics-inspired learning method for SWS estimation using equations governing the wave propagation in a viscoelastic medium. The proposed method does not require any finite element simulated data, and training data is synthetically generated using forward modeling of the wave propagation equation. Furthermore, unlabeled experimental data is utilized to train/fine-tune the network. We validated the proposed method using experimental data imaged by different machines and data created by placing pork fat on top of a phantom. The findings validate that the suggested approach can demonstrate comparable (or superior) performance compared to the traditional cross-correlation method.