Magnetic Resonance Fingerprinting (MRF) sequences have variable flip angles and TRs that generate unique signal evolutions based on selected tissue properties. To obtain quantitative maps with accurate values in the dictionary matching step it is important to minimize various noise sources or simulate them into the dictionary. We propose to explore systematic artifacts or features that are not in the dictionary with independent component analysis of complex MRF time series. In vivo brain results with 3D MRF revealed global sequence related features (bias from B0 and B1 associated with phase of MRF data) and subject specific reconstruction artifacts.
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