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Importance of R2 accuracy in susceptibility source separation.

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|August 16, 2025
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Summary
This summary is machine-generated.

Accurate R2 values are crucial for reliable brain susceptibility mapping. Errors in R2 significantly impact paramagnetic and diamagnetic outputs, with one method (χ-sepnet) showing greater robustness to these inaccuracies.

Keywords:
R2 mapsource separationsusceptibilitytransverse relaxation mapping

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Area of Science:

  • Neuroimaging
  • Magnetic Resonance Imaging (MRI)
  • Biophysics

Background:

  • Susceptibility source separation techniques aim to differentiate paramagnetic and diamagnetic contributions in the brain.
  • Accurate R2 (1/T2) relaxation rate quantification is essential for these methods.
  • Inaccuracies in R2 can arise from various sources, including fitting errors and approximations.

Purpose of the Study:

  • To evaluate the impact of R2 accuracy on paramagnetic and diamagnetic outputs derived from two susceptibility source separation methods.
  • To compare the sensitivity of χ-separation and χ-sepnet to R2 errors.

Main Methods:

  • R2 errors were systematically introduced into baseline R2 maps obtained from Bloch modeling in 11 healthy volunteers.
  • These errors simulated simple exponential fitting, R2 multiplication factors, and R2 approximation using only R2*.
  • Altered R2 maps were used as input for χ-separation and χ-sepnet to assess output differences and percentage errors within regions of interest (ROIs).

Main Results:

  • R2 errors directly influenced paramagnetic and diamagnetic component accuracy.
  • χ-sepnet demonstrated higher robustness to R2 errors compared to χ-separation, with errors generally within ±20% in ROIs.
  • χ-separation exhibited significantly larger errors (up to 56%) with R2 inaccuracies, particularly when using default parameters or R2* approximation.

Conclusions:

  • The accuracy of R2 measurements critically affects the reliability of paramagnetic and diamagnetic outputs from susceptibility source separation.
  • Simple R2 fitting or approximation methods can introduce substantial bias.
  • χ-sepnet offers improved stability against R2 errors in brain susceptibility mapping.