Optimal MRI Undersampling Patterns for Pathology Localization

We investigate MRI acceleration strategies for the benefit of downstream image analysis tasks. Specifically, we propose to optimize the k-space undersampling patterns according to how well a sought-after pathology could be segmented or localized in the reconstructed images. We study the effect of the proposed paradigm on the segmentation task using two classical labeled medical datasets, and on the task of pathology visualization within the bounding boxes, using the recently released fastMRl+ annotations. We demonstrate a noticeable improvement of the target metrics when the sampling pattern is optimized, e.g., for the segmentation problem at x16 acceleration, we report up to 12% improvement in Dice score over the other undersampling strategies.