  Classical model-based imaging methods for ultrasound elasticity inverse
problem require prior constraints about the underlying elasticity patterns,
while finding the appropriate hand-crafted prior for each tissue type is a
challenge. In contrast, standard data-driven methods count solely on supervised
learning on the training data pairs leading to massive network parameters for
unnecessary physical model relearning which might not be consistent with the
governing physical models of the imaging system. Fusing the physical forward
model and noise statistics with data-adaptive priors leads to a united
reconstruction framework that guarantees the learned reconstruction agrees with
the physical models while coping with the limited training data. In this paper,
we propose a new methodology for estimating the elasticity image by solving a
regularized optimization problem which benefits from the physics-based modeling
via a data-fidelity term and adversarially learned priors via a regularization
term. In this method, the regularizer is trained based on the Wasserstein
Generative Adversarial Network (WGAN) objective function which tries to
distinguish the distribution of clean and noisy images. Leveraging such an
adversarial regularizer for parameterizing the distribution of latent images
and using gradient descent (GD) for solving the corresponding regularized
optimization task leads to stability and convergence of the reconstruction
compared to pixel-wise supervised learning schemes. Our simulation results
verify the effectiveness and robustness of the proposed methodology with
limited training datasets.
