Problem
Research questions and friction points this paper is trying to address.
Motion-corrected quantitative R2* mapping
Unified deep unfolding framework
Self-supervised learning for MRI artifact reduction
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping
🤖 AI Summary
Conventional quantitative MRI (qMRI) struggles to jointly suppress motion and magnetic field inhomogeneity artifacts under highly accelerated acquisitions, degrading R2* quantification accuracy. To address this, we propose the first end-to-end interpretable deep-unfolding framework that unifies motion modeling, field-map correction, and biophysical signal modeling directly within a k-space-domain reconstruction architecture—enabling artifact-free R2* map estimation from undersampled multi-echo gradient-recalled echo (mGRE) raw data. Crucially, we introduce a novel self-supervised training paradigm that requires no prior parameter estimation, eliminating dependence on ground-truth R2* maps or field-map labels. Evaluated on real accelerated mGRE data, our method significantly reduces R2* quantification error compared to conventional two-step approaches, while demonstrating superior robustness and cross-scanner generalizability.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep Unfolding Framework
Motion-Artifact Reduction
Self-Supervised Learning Scheme
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