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A Realistic In Silico Brain Phantom for Quantifying Susceptibility Anisotropy-Induced Error in Susceptibility
Daniel Ridani1, Benjamin De Leener1,2,3, Eva Alonso-Ortiz1,2,4
1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada.
Purpose:
To create a realistic in silico brain phantom for positive and negative magnetic susceptibility that incorporates susceptibility anisotropy, enabling the evaluation of how susceptibility anisotropy influences susceptibility separation algorithm performance.
Methods:
We expanded an existing QSM validation phantom by creating separate maps for positive and negative susceptibility, with the option of modeling susceptibility anisotropy. Multi-echo gradient echo data were simulated to evaluate four susceptibility separation techniques ( -separation, DECOMPOSE-QSM, APART-QSM, and -QSM). To assess the impact of noise, simulations were performed at different SNR levels (50, 100, 200, 300).
Results:
Our findings showed that the error in negative susceptibility estimates increased by up to 53% when susceptibility anisotropy was present, compared to the case without susceptibility anisotropy, with -separation being the algorithm that was most sensitive to anisotropy. Robustness to noise varied across the assessed algorithms, with APART-QSM and -separation having the highest and lowest sensitivity to noise, respectively.
Conclusion:
The modified phantom is open-source and can serve as a numerical ground truth for evaluating susceptibility separation methods. Our findings emphasize the importance of incorporating susceptibility anisotropy into susceptibility separation models to improve their accuracy.
