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Journal Articles Medical Image Analysis Year : 2021

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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Dates and versions

hal-03164215 , version 1 (09-03-2021)

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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, 2021, 68, pp.101941. ⟨10.1016/j.media.2020.101941⟩. ⟨hal-03164215⟩
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