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Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge.

Christian Langkammer1, Ferdinand Schweser2,3, Karin Shmueli4

  • 1Department of Neurology, Medical University of Graz, Graz, Austria.

Magnetic Resonance in Medicine
|August 2, 2017
PubMed
Summary

The 2016 quantitative susceptibility mapping (QSM) challenge revealed that while algorithms minimized error metrics, they caused over-smoothing and loss of fine details in susceptibility maps. This highlights the need for improved image quality criteria in QSM.

Keywords:
assessmentchallengedipole inversionquantitative susceptibility mappingreconstruction algorithms

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Area of Science:

  • Medical Imaging
  • Biophysics
  • Computational Neuroscience

Background:

  • Quantitative susceptibility mapping (QSM) is crucial for analyzing magnetic susceptibility in biological tissues.
  • Accurate QSM reconstruction from phase data is essential for reliable interpretation of MRI scans.

Purpose of the Study:

  • To evaluate the performance of various QSM algorithms in reconstructing accurate susceptibility maps from MRI phase data.
  • To assess the ability of QSM algorithms to faithfully recover underlying magnetic susceptibility information.

Main Methods:

  • Gradient-echo images were acquired from a healthy volunteer at 3T with 1.06 mm isotropic resolution.
  • A reference susceptibility map from 12 orientations was compared against maps generated from single-orientation data.
  • Quantitative metrics including RMSE, SSIM, and HFEN were used to assess deviations.

Main Results:

  • Twenty-seven QSM algorithms were evaluated, with top performers utilizing compressed sensing strategies.
  • While error metrics were similar among the best algorithms, visual differences in susceptibility maps were substantial.
  • The top algorithms exhibited over-smoothing and loss of conspicuity in fine anatomical features like vessels.

Conclusions:

  • Current QSM algorithms, optimized for error metrics, lead to over-smoothing and reduced visibility of critical details.
  • The challenge underscores the necessity for developing superior numerical criteria for assessing QSM image quality.
  • Future research should focus on QSM methods that balance accuracy with the preservation of fine anatomical structures.