| Literature DB >> 31347226 |
Debra F McGivney1, Rasim Boyacıoğlu1, Yun Jiang1,2, Megan E Poorman3,4, Nicole Seiberlich5,2, Vikas Gulani1,2, Kathryn E Keenan4, Mark A Griswold1, Dan Ma1.
Abstract
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.Entities:
Keywords: deep learning; machine learning; magnetic resonance fingerprinting; optimization; reconstruction
Mesh:
Year: 2019 PMID: 31347226 PMCID: PMC6980890 DOI: 10.1002/jmri.26877
Source DB: PubMed Journal: J Magn Reson Imaging ISSN: 1053-1807 Impact factor: 4.813