Magnetic resonance fingerprinting review part 2: Technique and directions

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, focusin...

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Veröffentlicht in:Journal of magnetic resonance imaging Jg. 51; H. 4; S. 993 - 1007
Hauptverfasser: McGivney, Debra F., Boyacıoğlu, Rasim, Jiang, Yun, Poorman, Megan E., Seiberlich, Nicole, Gulani, Vikas, Keenan, Kathryn E., Griswold, Mark A., Ma, Dan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 01.04.2020
Wiley Subscription Services, Inc
Schlagworte:
Algorithms
Brain
deep learning
DNA fingerprinting
Fingerprinting
Image Processing, Computer-Assisted
Learning algorithms
Machine learning
Magnetic resonance
magnetic resonance fingerprinting
Magnetic Resonance Imaging
Magnetic Resonance Spectroscopy
Optimization
Pattern matching
Phantoms, Imaging
reconstruction
Resonance
deep learning
magnetic resonance fingerprinting
reconstruction
machine learning
optimization
ISSN:1053-1807, 1522-2586, 1522-2586
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1053-1807
1522-2586
1522-2586
DOI:10.1002/jmri.26877