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Technical Note: Optimization of quantitative susceptibility mapping by streaking artifact detection
Author(s) -
Wu MingLong,
Wang ChunKun,
Lin PoYu,
Chao TzuCheng
Publication year - 2020
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.14460
Subject(s) - streaking , quantitative susceptibility mapping , artificial intelligence , artifact (error) , computer vision , iterative reconstruction , computer science , pattern recognition (psychology) , magnetic resonance imaging , physics , medicine , optics , radiology
Purpose In quantitative susceptibility mapping (QSM) using magnetic resonance imaging, image reconstruction methods usually aim at suppressing streaking artifacts. In this study, a streaking detection method is proposed for evaluating and optimizing quantitative susceptibility maps. Methods Nine healthy subjects participated in this study and underwent three‐dimensional multi‐echo gradient echo scans. Regularized iterative algorithm was used for reconstruction of tissue susceptibility maps in all subjects. Streaking detection was applied to evaluate streaking artifact in tissue susceptibility maps. In addition, an optimization process for QSM reconstruction by streaking detection was applied and was compared with matching noise level method. Results It is shown that the proposed streaking detection technique effectively delineates streaking artifact in tissue susceptibility maps. In QSM reconstruction, optimization by streaking detection successfully determines the regularization factor that balances between streaking artifact suppression and tissue texture preservation. ROI analyses of brain tissue susceptibility show that optimization by streaking detection achieves results in good agreement with that from matching noise level method. Conclusions Streaking detection enables direct visualization of streaking patterns in tissue susceptibility maps. It can be applied both for evaluating QSM reconstruction quality and for comparing different reconstruction algorithms. Furthermore, streaking detection can be incorporated into an optimization process of QSM reconstruction. Therefore, we conclude that the proposed method will add value to reconstruction of QSM.