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Joint coil sensitivity and motion correction in parallel MRI with a self-calibrating score-based diffusion model.

Lixuan Chen1, Xuanyu Tian2, Jiangjie Wu3

  • 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

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Summary

This study introduces JSMoCo, a new method for Magnetic Resonance Imaging (MRI) that jointly corrects motion and estimates coil sensitivity maps. This improves image quality and motion correction in fast MRI scans.

Keywords:
Diffusion modelsMRI reconstructionMotion correctionSensitivity Map estimation

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Biophysics

Background:

  • Magnetic Resonance Imaging (MRI) is crucial for diagnosis but suffers from long scan times and motion artifacts.
  • Existing motion correction methods often fail to address the impact of artifacts on coil sensitivity map (CSM) estimation during fast MRI reconstruction.
  • Errors in CSM estimation can propagate, significantly degrading the performance of motion correction algorithms.

Purpose of the Study:

  • To propose JSMoCo, a novel method for jointly estimating motion parameters and time-varying coil sensitivity maps for accelerated MRI reconstruction.
  • To address the ill-posed nature of joint estimation by utilizing score-based diffusion models as priors and incorporating MRI physical principles.
  • To improve the accuracy and robustness of motion correction in fast MRI by accounting for dynamic changes in coil sensitivities.

Main Methods:

  • Developed JSMoCo, a method for joint estimation of motion parameters and time-varying coil sensitivity maps (CSMs) in under-sampled MRI.
  • Leveraged score-based diffusion models as powerful priors to constrain the ill-posed inverse problem.
  • Parameterized rigid motion with trainable variables, modeled CSMs as polynomial functions, and employed a Gibbs sampler for system consistency.

Main Results:

  • JSMoCo successfully reconstructed high-quality MRI images from under-sampled k-space data in both simulated and in-vivo experiments.
  • Demonstrated robust motion correction by accurately estimating time-varying coil sensitivities.
  • Prevented error propagation from pre-estimated sensitivity maps to the final reconstructed images.

Conclusions:

  • JSMoCo offers a significant advancement in fast MRI reconstruction by simultaneously addressing motion artifacts and dynamic coil sensitivity variations.
  • The proposed method enhances image quality and motion correction accuracy, outperforming existing techniques.
  • The joint estimation framework, guided by diffusion models and physical constraints, provides a robust solution for accelerated MRI acquisition.