mlsanityData to 14 Oct 2026

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

arXiv 2210.06330 ↗First posted 12 Oct 2022eess.IV cs.CV cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 26 of its 76 references that are in the map. First posted on arXiv 12 Oct 2022.

Builds on

26 of 76 references.

References · 26

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14 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 2.

5 papers.

  1. 2024Learning Image Priors through Patch-based Diffusion Models for Solving Inverse Problemscited by 20
  2. 2024DiffusionBlend: Learning 3D Image Prior through Position-aware Diffusion Score Blending for 3D Computed Tomography Reconstructioncited by 13
  3. 2022Accelerated Motion Correction with Deep Generative Diffusion Modelscited by 12
  4. 2023PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstructioncited by 7
  5. 2023Learning Task-Specific Strategies for Accelerated MRIcited by 3