Ultrasound shear wave elastography (SWE) is a noninvasive technique for characterizing the mechanical properties of soft tissues. Although early SWE methods assumed purely elastic behavior, most biological tissues are now recognized as viscoelastic, motivating the development of advanced reconstruction strategies. Existing approaches to viscoelastic imaging include physics-based modeling, inverse problem formulations, and numerical methods for estimating elasticity, viscosity, and frequency-dependent moduli. More recently, deep learning, particularly hybrid frameworks that incorporate biomechanical priors with data-driven models trained on synthetic and clinical data, has emerged as a promising direction for SWE reconstruction. Challenges remain in noise sensitivity, modeling assumptions, computational cost, and cross-platform standardization. Addressing these limitations is essential for translating viscoelastic biomarkers into routine clinical practice. These developments position SWE to evolve beyond stiffness mapping toward comprehensive viscoelastic biomarkers, with the potential to improve disease characterization, monitoring, and clinical decision-making.
Sahshong et al. (2026) studied this question.