Multi-tasking to Correct: Motion-Compensated MRI via Joint Reconstruction and Registration
For MRI researchers and clinicians, this method improves motion-correction in multi-acquisition MRI, but the improvement is incremental over existing joint approaches.
This work proposes a novel variational model for joint reconstruction and registration to correct motion in MRI, achieving higher quality reconstructions and better estimates of breathing dynamics compared to sequential approaches.
This work addresses a central topic in Magnetic Resonance Imaging (MRI) which is the motion-correction problem in a joint reconstruction and registration framework. From a set of multiple MR acquisitions corrupted by motion, we aim at - jointly - reconstructing a single motion-free corrected image and retrieving the physiological dynamics through the deformation maps. To this purpose, we propose a novel variational model. First, we introduce an $L^2$ fidelity term, which intertwines reconstruction and registration along with the weighted total variation. Second, we introduce an additional regulariser which is based on the hyperelasticity principles to allow large and smooth deformations. We demonstrate through numerical results that this combination creates synergies in our complex variational approach resulting in higher quality reconstructions and a good estimate of the breathing dynamics. We also show that our joint model outperforms in terms of contrast, detail and blurring artefacts, a sequential approach.