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Multiscale coherence regularization reconstruction using a nonlocal operator for fast variable-density spiral

Sheng Fang1, Lyu Li2, Wenchuan Wu2

  • 1Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University.

Magnetic Resonance Imaging
|April 25, 2016
PubMed
Summary
This summary is machine-generated.

A new algorithm, mCORNOL, effectively suppresses aliasing artifacts in accelerated MR imaging. This method improves image quality by sequentially addressing large-scale artifacts and preserving fine structures in variable-density spiral acquisitions.

Keywords:
Multiscale CORNOLNonlinear reconstructionParallel imagingStructure preservationVariable-density spiral

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Image Reconstruction
  • Medical Physics

Background:

  • Undersampled variable-density spiral (VDS) trajectories in MRI accelerate data acquisition.
  • Nonlinear reconstruction methods can reduce aliasing artifacts but may compromise image quality.
  • Balancing artifact suppression and fine-structure preservation remains a challenge.

Purpose of the Study:

  • To introduce a novel sequential, multiscale coherence regularization algorithm using a nonlocal operator (mCORNOL).
  • To address the conflict between large-scale aliasing artifact suppression and fine-scale structure preservation in undersampled VDS MRI.
  • To enhance the overall image quality of accelerated VDS MRI reconstructions.

Main Methods:

  • mCORNOL leverages nonlocal operators for scale-controlled image structure measurement.
  • The algorithm adjusts smoothing constraint scales, starting large to remove artifacts and progressively reducing to preserve structures.
  • Reconstruction iteratively balances artifact removal and structure fidelity.

Main Results:

  • Numerical simulations and in vivo experiments validated mCORNOL's performance.
  • mCORNOL demonstrated superior aliasing artifact suppression compared to SENSE, Total Variation, and original CORNOL.
  • Enhanced preservation of image structures was observed at high reduction factors.

Conclusions:

  • mCORNOL reconstruction offers an effective solution for improving image quality in undersampled VDS MRI.
  • The sequential, multiscale approach successfully decouples artifact removal and structure preservation.
  • This method holds promise for accelerating MRI acquisition without sacrificing diagnostic image quality.