Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution - Université Clermont Auvergne Accéder directement au contenu
Article Dans Une Revue Medical Image Analysis Année : 2021

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

Résumé

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.
Fichier principal
Vignette du fichier
1908.05911.pdf (9.01 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

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

Identifiants

Citer

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⟩
89 Consultations
65 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More