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• W2081770103 abstract "The aim of this study is to perform a thorough comparison of quantitative susceptibility mapping (QSM) techniques and their dependence on the assumptions made. The compared methodologies were: two iterative single orientation methodologies minimizing the l2, l1TV norm of the prior knowledge of the edges of the object, one over-determined multiple orientation method (COSMOS) and a newly proposed modulated closed-form solution (MCF). The performance of these methods was compared using a numerical phantom and in-vivo high resolution (0.65 mm isotropic) brain data acquired at 7 T using a new coil combination method. For all QSM methods, the relevant regularization and prior-knowledge parameters were systematically changed in order to evaluate the optimal reconstruction in the presence and absence of a ground truth. Additionally, the QSM contrast was compared to conventional gradient recalled echo (GRE) magnitude and R2* maps obtained from the same dataset. The QSM reconstruction results of the single orientation methods show comparable performance. The MCF method has the highest correlation (corr MCF=0.95, r(2)MCF=0.97) with the state of the art method (COSMOS) with additional advantage of extreme fast computation time. The L-curve method gave the visually most satisfactory balance between reduction of streaking artifacts and over-regularization with the latter being overemphasized when the using the COSMOS susceptibility maps as ground-truth. R2* and susceptibility maps, when calculated from the same datasets, although based on distinct features of the data, have a comparable ability to distinguish deep gray matter structures." @default.
• W2081770103 created "2016-06-24" @default.
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• W2081770103 date "2015-02-01" @default.
• W2081770103 modified "2023-10-14" @default.
• W2081770103 title "A modulated closed form solution for quantitative susceptibility mapping — A thorough evaluation and comparison to iterative methods based on edge prior knowledge" @default.
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• W2081770103 doi "https://doi.org/10.1016/j.neuroimage.2014.11.038" @default.
• W2081770103 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25463463" @default.
• W2081770103 hasPublicationYear "2015" @default.
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