  Existing physical model-based imaging methods for ultrasound elasticity
reconstruction utilize fixed variational regularizers that may not be
appropriate for the application of interest or may not capture complex spatial
prior information about the underlying tissues. On the other hand, end-to-end
learning-based methods count solely on the training data, not taking advantage
of the governing physical laws of the imaging system. Integrating
learning-based priors with physical forward models for ultrasound elasticity
imaging, we present a joint reconstruction framework which guarantees that
learning driven reconstructions are consistent with the underlying physics. For
solving the elasticity inverse problem as a regularized optimization problem,
we propose a plug-and-play (PnP) reconstruction approach in which each
iteration of the elasticity image estimation process involves separate updates
incorporating data fidelity and learning-based regularization. In this
methodology, the data fidelity term is developed using a statistical linear
algebraic model of quasi-static equilibrium equation revealing the relationship
of the observed displacement fields \cmmnt{measured deformation data} to the
unobserved elastic modulus. The regularizer comprises a convolutional neural
network (CNN) based denoiser that captures the learned prior structure of the
underlying tissues. Preliminary simulation results demonstrate the robustness
and effectiveness of the proposed approach with limited training datasets and
noisy displacement measurements.
