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Maxwell parallel imaging.

Matteo Alessandro Francavilla1, Stamatios Lefkimmiatis1, Jorge F Villena1

  • 1Q Bio Inc., San Carlos, CA, USA.

Magnetic Resonance in Medicine
|March 18, 2021
PubMed
Summary
This summary is machine-generated.

A new Maxwell parallel imaging (MPI) framework uses Maxwell equations for sensitivity map estimation and constrained optimization for parameter-free image reconstruction, enabling accurate and efficient accelerated MRI scans.

Keywords:
Maxwell regularizationconstrained optimizationelectromagnetic basisparallel imagingtensor decomposition

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Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging
  • Computational Physics

Background:

  • Parallel imaging (PI) reconstruction from accelerated data is ill-posed.
  • Existing methods rely on image and sensitivity map (SM) characteristics for regularization.

Purpose of the Study:

  • Develop a general framework for PI using Maxwell regularization for SM estimation.
  • Implement parameter-free image reconstruction via constrained optimization.

Main Methods:

  • Utilize Maxwell equations to define a subspace for SMs.
  • Employ constrained optimization for image reconstruction post-SM estimation.
  • The Maxwell parallel imaging (MPI) method is applicable to 2D/3D, Cartesian/radial trajectories, and requires minimal calibration.

Main Results:

  • MPI demonstrates effectiveness across various undersampling schemes (radial, Poisson-disc, Cartesian).
  • MPI performance is comparable to state-of-the-art PI methods.
  • Tensor decomposition reduces memory footprint for 3D reconstructions.

Conclusions:

  • MPI offers a physics-inspired, optimization-based approach for PI.
  • The framework enables accurate and efficient image reconstruction from accelerated scans.