Generative diffusion model for DSC MRI artifact correction

Abstract

Dynamic susceptibility contrast (DSC) MRI may suffer from artifacts due to long acquisition time. Past methods are limited in their performance and may change the contrast passage timing. In this work, we present a generative diffusion model that can restore signal loss and movement artifacts. We showed the generated DSC MRI images to have proper post-contrast vessel and grey matter structure with accurate contrast agent arrival/washout timing. The brain shape was also accurately generated as shown by the DICE score. This approach could provide a solution to restore a corrupted DSC MRI data while maintaining accurate contrast passage timing.