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Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution

Abstract : Motion degradation is a central problem in Magnetic Resonance Imaging (MRI). This work addresses the problem of how to obtain higher quality, super-resolved motion-free, reconstructions from highly undersampled MRI data. In this work, we present for the first time a variational multi-task framework that allows joining three relevant tasks in MRI: reconstruction, registration and super-resolution. Our framework takes a set of multiple undersampled MR acquisitions corrupted by motion into a novel multi-task optimisation model, which is composed of an L 2 fidelity term that allows sharing representation between tasks, super-resolution foundations and hyperelastic deformations to model biological tissue behaviors. We demonstrate that this combination yields to significant improvements over sequential models and other bi-task methods. Our results exhibit fine details and compensate for motion producing sharp and highly textured images compared to state of the art methods.
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https://hal.uca.fr/hal-03164215
Contributor : Noémie DEBROUX Connect in order to contact the contributor
Submitted on : Tuesday, March 9, 2021 - 5:20:36 PM
Last modification on : Friday, April 1, 2022 - 3:52:25 AM
Long-term archiving on: : Thursday, June 10, 2021 - 7:32:45 PM

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Veronica Corona, Angelica I Aviles-Rivero, Noémie Debroux, Carole Le Guyader, Carola-Bibiane Schönlieb. Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution. Medical Image Analysis, Elsevier, 2021, 68, pp.101941. ⟨10.1016/j.media.2020.101941⟩. ⟨hal-03164215⟩

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