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A modulated closed form solution for quantitative susceptibility mapping--a thorough evaluation and comparison to

Diana Khabipova1, Yves Wiaux2, Rolf Gruetter3

  • 1Laboratory for Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

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|December 3, 2014
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Summary

Quantitative susceptibility mapping (QSM) techniques were compared, finding the fast modulated closed-form solution (MCF) highly correlated with COSMOS. Both QSM and R2* maps effectively distinguish deep gray matter structures.

Keywords:
Effective transverse relaxationModulated closed form solution (MCF)Phase imaging Brain Tissue susceptibility magnetic resonance imaging (MRI)Quantitative susceptibility mapping

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Area of Science:

  • Medical Imaging
  • Neuroimaging
  • Magnetic Resonance Imaging

Background:

  • Quantitative susceptibility mapping (QSM) is crucial for analyzing magnetic susceptibility in biological tissues.
  • Understanding the performance and assumptions of different QSM techniques is essential for accurate neuroimaging.
  • High-resolution 7 T MRI data requires robust QSM reconstruction methods.

Purpose of the Study:

  • To comprehensively compare various QSM techniques, evaluating their performance and sensitivity to underlying assumptions.
  • To assess the optimal reconstruction parameters for QSM methods using both numerical phantoms and in-vivo human brain data.
  • To compare QSM-derived contrast with conventional gradient recalled echo (GRE) magnitude and R2* maps.

Main Methods:

  • Comparison of two iterative single orientation QSM methods (l2, l1TV norm minimization), COSMOS (multiple orientation), and a novel modulated closed-form solution (MCF).
  • Evaluation using a numerical phantom and high-resolution (0.65 mm isotropic) 7 T in-vivo brain data with a new coil combination method.
  • Systematic variation of regularization and prior-knowledge parameters to find optimal reconstructions, with and without ground truth.

Main Results:

  • Single orientation QSM methods demonstrated comparable performance.
  • The MCF method showed high correlation (0.95) with COSMOS, offering significantly faster computation.
  • L-curve method provided a good balance between artifact reduction and over-regularization; R2* and QSM maps showed similar ability to differentiate deep gray matter structures.

Conclusions:

  • The MCF method presents a computationally efficient and accurate alternative for QSM reconstruction.
  • Careful parameter selection is necessary to balance artifact reduction and avoid over-regularization in QSM.
  • QSM and R2* mapping offer complementary or comparable insights into deep gray matter structure.