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 L2 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.
@article{arxiv.1908.05911,
title = {Variational Multi-Task MRI Reconstruction: Joint Reconstruction, Registration and Super-Resolution},
author = {Veronica Corona and Angelica I. Aviles-Rivero and Noémie Debroux and Carole Le Guyader and Carola-Bibiane Schönlieb},
journal= {arXiv preprint arXiv:1908.05911},
year = {2019}
}