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Repeatability of Susceptibility Source Separation Methods in Human Brain: A Single-Site Study at 3 T.

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Summary

Susceptibility source separation provides subvoxel brain insights but algorithm choice impacts repeatability. APART and χ-SepNet showed better performance, though reliability varied by brain region and was lower than conventional QSM.

Keywords:
3 Trepeatabilityscan–rescansusceptibility mappingsusceptibility source separation

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Medical Physics

Background:

  • Conventional quantitative susceptibility mapping (QSM) is limited to net susceptibility effects.
  • Susceptibility source separation aims to differentiate iron and myelin distribution.
  • Repeatability of R2'-based susceptibility separation algorithms requires thorough evaluation.

Purpose of the Study:

  • To investigate the repeatability of three R2'-based susceptibility separation algorithms: χ-Separation, χ-SepNet, and APART.
  • To assess the reliability and contrast of paramagnetic (χ+) and diamagnetic (χ-) maps generated by these algorithms.
  • To compare algorithm performance across different brain regions.

Main Methods:

  • Utilized 3-T scan-rescan data from 21 healthy subjects.
  • Assessed repeatability using intraclass correlation coefficient (ICC) and repeatability coefficient (RC).
  • Evaluated map contrast using average values and compared performance across brain regions.

Main Results:

  • Repeatability varied significantly between algorithms and brain regions.
  • APART and χ-SepNet demonstrated superior repeatability compared to χ-Separation.
  • Paramagnetic (χ+) and diamagnetic (χ-) maps showed moderate to good reliability, generally lower than conventional QSM due to R2' input reliability.
  • Repeatability was reduced in the frontal lobe and near air-tissue interfaces.
  • Average RCs for APART were 4 ppb (7 ppb in iron-rich regions for χ+), and for χ-SepNet were 5 ppb (10 ppb in iron-rich regions for χ+).

Conclusions:

  • Susceptibility source separation offers valuable subvoxel insights into brain iron and myelin.
  • Algorithm choice critically influences the repeatability and reliability of susceptibility separation results.
  • APART and χ-SepNet present promising options for susceptibility source separation, with moderate to good repeatability in most brain regions.
  • Further research may focus on improving the reliability of R2' input for enhanced separation accuracy.