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Magnetic resonance fingerprinting review part 2: Technique and directions
Author(s) -
McGivney Debra F.,
Boyacıoğlu Rasim,
Jiang Yun,
Poorman Megan E.,
Seiberlich Nicole,
Gulani Vikas,
Keenan Kathryn E.,
Griswold Mark A.,
Ma Dan
Publication year - 2020
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.26877
Subject(s) - computer science , magnetic resonance imaging , artificial intelligence , matching (statistics) , pattern recognition (psychology) , volume (thermodynamics) , mathematics , physics , radiology , medicine , statistics , quantum mechanics
Magnetic resonance fingerprinting (MRF) is a general framework to quantify multiple MR‐sensitive tissue properties with a single acquisition. There have been numerous advances in MRF in the years since its inception. In this work we highlight some of the recent technical developments in MRF, focusing on sequence optimization, modifications for reconstruction and pattern matching, new methods for partial volume analysis, and applications of machine and deep learning. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:993–1007.