Medical image reconstruction with image-adaptive priors learned by use of generative adversarial networks
This work addresses medical imaging reconstruction for improved diagnostic accuracy, but it appears incremental as it builds on existing GAN-based methods with an adaptive approach.
The authors tackled the ill-posed inverse problem in medical image reconstruction by using an image-adaptive GAN-based method (IAGAN) to incorporate prior distributions, resulting in the recovery of fine structures relevant for diagnosis that are often oversmoothed with traditional regularization.
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.