  Medical image reconstruction is typically an ill-posed inverse problem. In
order to address such ill-posed problems, the prior distribution of the sought
after object property is usually incorporated by means of some
sparsity-promoting regularization. Recently, prior distributions for images
estimated using generative adversarial networks (GANs) have shown great promise
in regularizing some of these image reconstruction problems. In this work, we
apply an image-adaptive GAN-based reconstruction method (IAGAN) to reconstruct
high fidelity images from incomplete medical imaging data. It is observed that
the IAGAN method can potentially recover fine structures in the object that are
relevant for medical diagnosis but may be oversmoothed in reconstructions with
traditional sparsity-promoting regularization.
