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Automated adaptive preconditioner for quantitative susceptibility mapping
Zhe Liu1,2, Yan Wen1,2, Pascal Spincemaille2
1Department of Biomedical Engineering, Cornell University, Ithaca, New York.
Purpose:
To develop an automated adaptive preconditioner for QSM reconstruction with improved susceptibility quantification accuracy and increased image quality.
Theory And Methods:
The total field was used to rapidly produce an approximate susceptibility map, which was then averaged and trended over binning to generate a spatially varying distribution of preconditioning values. This automated adaptive preconditioner was used to reconstruct QSM via total field inversion and was compared with its empirical counterparts in a numerical simulation, a brain experiment with 5 healthy subjects and 5 patients with intracerebral hemorrhage, and a cardiac experiment with 3 healthy subjects.
Results:
Among evaluated preconditioners, the automated adaptive preconditioner achieved the fastest convergence in reducing the RMSE of the QSM in the simulation, suppressed hemorrhage-associated artifacts while preserving surrounding brain tissue contrasts, and provided cardiac chamber oxygenation values consistent with those reported in the literature.
Conclusion:
An automated adaptive preconditioner allows high-quality QSM from the total field in imaging various anatomies with dynamic susceptibility ranges.
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