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Automated adaptive preconditioner for quantitative susceptibility mapping
Author(s) -
Liu Zhe,
Wen Yan,
Spincemaille Pascal,
Zhang Shun,
Yao Yihao,
Nguyen Thanh D.,
Wang Yi
Publication year - 2020
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27900
Subject(s) - preconditioner , quantitative susceptibility mapping , computer science , image quality , algorithm , artificial intelligence , medicine , image (mathematics) , radiology , magnetic resonance imaging , iterative method
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 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. 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.