CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping
This work addresses the challenge of improving end-to-end performance in quantitative MRI for medical imaging applications, though it appears incremental as it builds on existing deep unfolding methods.
The paper tackles the problem of suboptimal quantitative MRI (qMRI) due to separate handling of artifacts from accelerated acquisition, motion, and field inhomogeneities, presenting CoRRECT, a deep unfolding framework that recovers motion and inhomogeneity artifact-free R2* maps in highly accelerated settings.
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.