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Equation of Motion: General Plane motion01:22

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Image reconstruction algorithm for motion insensitive MR Fingerprinting (MRF): MORF.

Bhairav Bipin Mehta1, Dan Ma1, Eric Yann Pierre2

  • 1Department of Radiology, Case Western Reserve University, Cleveland, Ohio.

Magnetic Resonance in Medicine
|May 8, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces MOtion insensitive MR fingerprinting (MORF) to improve MRI robustness against subject motion. MORF effectively reduces motion artifacts, closely matching non-motion data even with significant motion corruption.

Keywords:
MR fingerprintingmotion compensationmotion estimationpattern recognitionquantitative imagingrelaxation time

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Biology

Background:

  • Magnetic Resonance Fingerprinting (MRF) is a powerful technique for quantitative MRI.
  • Subject motion during MRF acquisition can significantly degrade image quality and accuracy.
  • Existing methods struggle to fully compensate for motion-induced artifacts.

Purpose of the Study:

  • To develop a novel algorithm that enhances the robustness of MRF against subject motion.
  • To improve the reliability and accuracy of MRF in clinical settings where motion is common.

Main Methods:

  • Developed MOtion insensitive MRF (MORF), an iterative reconstruction algorithm incorporating retrospective motion correction.
  • MORF employs pattern recognition, motion-corrupted frame identification, registration-based motion estimation, and motion-compensated data consistency verification.
  • Validated MORF using in vivo 2D brain MRF data with both retrospective and prospective motion corruption.

Main Results:

  • MORF significantly reduced motion artifacts in MRF datasets corrupted by in-plane motion.
  • Results from MORF closely resembled those obtained from non-motion data, even with up to ~54% motion-corrupted data.
  • MORF outperformed the iterative multi-scale (IMS) algorithm in handling motion artifacts.

Conclusions:

  • MORF enhances MRF's insensitivity to rigid-body motion during acquisition.
  • The algorithm improves the practical applicability of MRF in scenarios prone to patient movement.
  • MORF offers a promising solution for motion-corrupted MRF data, preserving quantitative accuracy.