• Zhe Liu, Yan Wen, Pascal Spincemaille, Shun Zhang, Yihao Yao, Thanh D Nguyen, and Yi Wang.
• Department of Biomedical Engineering, Cornell University, Ithaca, New York.
• Magn Reson Med. 2020 Jan 1; 83 (1): 271-285.

PurposeTo develop an automated adaptive preconditioner for QSM reconstruction with improved susceptibility quantification accuracy and increased image quality.Theory And MethodsThe total field was used to rapidly produce an approximate susceptibility map, which was then averaged and trended over R 2 ∗ 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.ResultsAmong 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.ConclusionAn automated adaptive preconditioner allows high-quality QSM from the total field in imaging various anatomies with dynamic susceptibility ranges.© 2019 International Society for Magnetic Resonance in Medicine.

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