# Variational Multi-Task MRI Reconstruction: Joint Reconstruction, Registration and Super-Resolution

## TL;DR

This paper introduces a novel variational multi-task framework for MRI that jointly addresses reconstruction, registration, and super-resolution, significantly improving image quality from undersampled, motion-corrupted data.

## Contribution

It presents the first integrated multi-task optimization model combining reconstruction, registration, and super-resolution for MRI, outperforming sequential and bi-task approaches.

## Key findings

- Enhanced image sharpness and detail recovery.
- Significant motion artifact reduction.
- Outperforms state-of-the-art methods.

## 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.