CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping

TL;DR

CoRRECT is a deep unfolding framework that jointly corrects motion and field inhomogeneities in quantitative MRI, producing high-quality R2* maps from accelerated data without pre-computed correction parameters.

Contribution

This work introduces CoRRECT, a novel end-to-end neural network framework that integrates physical models and self-supervised learning for artifact correction in qMRI.

Findings

Successfully recovers artifact-free R2* maps from accelerated MRI data

Outperforms traditional methods in motion and inhomogeneity correction

Operates without pre-computed correction parameters

Abstract

Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters. Traditional qMRI methods usually deal separately with artifacts arising from accelerated data acquisition, involuntary physical motion, and magnetic-field inhomogeneities, leading to suboptimal end-to-end performance. This paper presents CoRRECT, a unified deep unfolding (DU) framework for qMRI consisting of a model-based end-to-end neural network, a method for motion-artifact reduction, and a self-supervised learning scheme. The network is trained to produce R2* maps whose k-space data matches the real data by also accounting for motion and field inhomogeneities. When deployed, CoRRECT only uses the k-space data without any pre-computed parameters for motion or inhomogeneity correction. Our results on experimentally collected multi-Gradient-Recalled Echo (mGRE) MRI data show that CoRRECT recovers motion and inhomogeneity artifact-free R2* maps in highly accelerated acquisition settings. This work opens the door to DU methods that can integrate physical measurement models, biophysical signal models, and learned prior models for high-quality qMRI.