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Ultrasound elasticity images which enable the visualization of quantitativemaps of tissue stiffness can be reconstructed by solving an inverse problem.Classical model-based approaches for ultrasound elastography use deterministicfinite element methods (FEMs) to incorporate the governing physical lawsresulting in poor performance in noisy conditions. Moreover, these approachesutilize fixed regularizers for various tissue patterns while appropriatedata-adaptive priors might be required for capturing the complex spatialelasticity distribution. In this regard, we propose a joint model-based andlearning-based framework for estimating the elasticity distribution by solvinga regularized optimization problem. We present an integrated objective functioncomposed of a statistical physics-based forward model and a data-drivenregularizer to leverage deep neural networks for learning the underlyingelasticity prior. This constrained optimization problem is solved using thegradient descent (GD) method and the gradient of regularizer is simply replacedby the residual of the trained denoiser network for having an explicitobjective function with reduced computation time.

Combining physics-based modeling and deep learning for ultrasound elastography