MRI suffers from incomplete information and localized deformations due to a manifold of factors. For example, metallic hip and knee replacements yield local deformities in the resultant scans. Other factors include, selective reconstruction of data and limited fields of views. In this work, we propose a new deep generative framework, referred to as IPA-MedGAN, for the inpainting of missing or complete information in brain MR. This framework aims to enhance the performance of further post-processing tasks, such as PET-MRI attenuation correction, segmentation or classification. Quantitative and qualitative comparisons were performed to illustrate the performance of the proposed framework.
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