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Researcher: Veronica Corona, Angelica I. Aviles-Rivero, Noémie Debroux, Carola-Bibiane Schönlieb

This project 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 L2 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.

Related Publications 

Multi-tasking to Correct: Motion-Compensated MRI via Joint Reconstruction and Registration
V Corona, AI Aviles-Rivero, N Debroux, M Graves, C Le Guyader, CB Schönlieb, G Williams – SCALE SPACE AND VARIATIONAL METHODS IN COMPUTER VISION, SSVM 2019 (2019) 11603 LNCS, 263
Motion Correction Resolved for MRI via Multi-Tasking: A Simultaneous Reconstruction and Registration Approach
V Corona, N Debroux, AI Aviles-Rivero, G Williams, M Graves, C Le Guyader, C-B Schönlieb