Automated adaptive preconditioner for quantitative susceptibility mapping

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...

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Published inMagnetic resonance in medicine Vol. 83; no. 1; pp. 271 - 285
Main Authors Liu, Zhe, Wen, Yan, Spincemaille, Pascal, Zhang, Shun, Yao, Yihao, Nguyen, Thanh D., Wang, Yi
Format Journal Article
LanguageEnglish
Published United States Wiley Subscription Services, Inc 01.01.2020
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ISSN0740-3194
1522-2594
1522-2594
DOI10.1002/mrm.27900

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Summary: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 R2∗ 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.
Bibliography:Funding information
National Institutes of Health, grants R01 DK116126, R01 NS090464, R21 EB024366, and R01 NS105144.
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.27900