Importance of R₂ accuracy in susceptibility source separation

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.