Abstract
Quantitative viscoelasticity imaging via shear wave elastography (SWE) remains challenging due to complex wave physics and limitations of conventional reconstruction methods. To address this, we present SW-VEI-Net, a physics-informed neural network (PINN) that simultaneously reconstructs the shear elastic modulus and viscous modulus by integrating viscoelastic wave equations into a dual-network architecture. The framework employs a dual-loss function to balance data fidelity and physics-based regularization, significantly reducing reliance on empirical data while improving interpretability. Extensive validation on tissue-mimicking phantoms, rat liver fibrosis model, and clinical cases demonstrates that SW-VEI-Net outperforms state-of-the-art SWE methods. Compared to SWENet (a PINN-based method using a linear elastic model), SW-VEI-Net not only enables simultaneous assessment of shear elastic and viscous moduli, but also achieves higher accuracy in shear elastic modulus reconstruction. Furthermore, when benchmarked against the dispersion fitting (DF) method (based on a viscoelastic model), SW-VEI-Net produces comparable viscoelastic parameter maps while exhibiting enhanced robustness and consistency. For liver fibrosis staging, SW-VEI-Net achieves AUC values of 0.85 ($\geq$F2) and 0.91 ($=$F4) based on elastic modulus classification, surpassing both SWENet (0.84, 0.85) and DF (0.78, 0.88). Additional validation in healthy volunteers shows strong agreement with a commercial ultrasound system. By synergizing deep learning with fundamental wave physics, this study represents a significant advancement in SWE, offering substantial clinical potential for early detection of hepatic fibrosis and malignant lesions through precise viscoelastic biomarker mapping.