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 in | Magnetic resonance in medicine Vol. 83; no. 1; pp. 271 - 285 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Wiley Subscription Services, Inc
01.01.2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0740-3194 1522-2594 1522-2594 |
| DOI | 10.1002/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. |
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| Bibliography: | Funding information National Institutes of Health, grants R01 DK116126, R01 NS090464, R21 EB024366, and R01 NS105144. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ISSN: | 0740-3194 1522-2594 1522-2594 |
| DOI: | 10.1002/mrm.27900 |