Integrating Learning-Based Priors With Physics-Based Models in Ultrasound Elasticity Reconstruction

Ultrasound elastography images, which enable quantitative visualization of tissue stiffness, can be reconstructed by solving an inverse problem. Classical model-based methods are usually formulated in terms of constrained optimization problems. To stabilize the elasticity reconstructions, regularization techniques, such as Tikhonov method, are used with the cost of promoting smoothness and blurriness in the reconstructed images. Thus, incorporating a suitable regularizer is essential for reducing the elasticity reconstruction artifacts, while finding the most suitable one is challenging. In this work, we present a new statistical representation of the physical imaging model, which incorporates effective signal-dependent colored noise modeling. Moreover, we develop a learning-based integrated statistical framework, which combines a physical model with learning-based priors. We use a dataset of simulated phantoms with various elasticity distributions and geometric patterns to train a denoising regularizer as the learning-based prior. We use fixed-point approaches and variants of gradient descent for solving the integrated optimization task following learning-based plug-and-play (PnP) prior and regularization by denoising (RED) paradigms. Finally, we evaluate the performance of the proposed approaches in terms of relative mean square error (RMSE) with nearly 20% improvement for both piecewise smooth simulated phantoms and experimental phantoms compared with the classical model-based methods and 12% improvement for both spatially varying breast-mimicking simulated phantoms and an experimental breast phantom, demonstrating the potential clinical relevance of our work. Moreover, the qualitative comparisons of reconstructed images demonstrate the robust performance of the proposed methods even for complex elasticity structures that might be encountered in clinical settings.