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Related Experiment Video

Updated: Sep 10, 2025

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

Xiaojian Xu1, Weijie Gan1, Satya V V N Kothapalli2

  • 1Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA.

Journal of Mathematical Imaging and Vision
|August 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces CoRRECT, a unified deep unfolding framework for quantitative MRI (qMRI). CoRRECT effectively reduces artifacts from motion and magnetic field inhomogeneities in accelerated MRI scans, producing high-quality R2* maps.

Keywords:
Deep unfoldingGradient recalled echoImage reconstructionInverse problemsMotion correctionR2* mappingSelf-supervised deep learning

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Artificial Intelligence

Background:

  • Quantitative MRI (qMRI) quantifies biological tissue parameters but faces challenges with artifacts.
  • Traditional qMRI methods address artifacts like motion and magnetic field inhomogeneities separately, limiting performance.
  • Accelerated data acquisition in qMRI exacerbates artifact issues, necessitating advanced solutions.

Purpose of the Study:

  • To present CoRRECT, a unified deep unfolding framework for artifact reduction in qMRI.
  • To develop a model-based neural network that integrates motion and field inhomogeneity correction.
  • To enable high-quality qMRI with accelerated acquisition without pre-computed correction parameters.

Main Methods:

  • Developed a unified deep unfolding (DU) framework named CoRRECT.
  • Implemented a model-based, end-to-end neural network trained with self-supervised learning.
  • The network learns to correct for motion and field inhomogeneities directly from k-space data.

Main Results:

  • CoRRECT successfully recovers artifact-free R2* maps from accelerated multi-gradient recalled echo (mGRE) MRI data.
  • The framework accounts for motion and field inhomogeneities without requiring pre-computed correction parameters.
  • Demonstrated robust performance in highly accelerated acquisition settings.

Conclusions:

  • CoRRECT offers a unified approach to artifact correction in qMRI, improving image quality.
  • Deep unfolding methods can integrate physical, biophysical, and learned models for advanced qMRI.
  • This work paves the way for more efficient and accurate quantitative MRI techniques.