Magnetic resonance fingerprinting review part 2: Technique and directions

Debra F McGivney; Rasim Boyacıoğlu; Yun Jiang; Megan E Poorman; Nicole Seiberlich; Vikas Gulani; Kathryn E Keenan; Mark A Griswold; Dan Ma

doi:10.1002/jmri.26877

Magnetic resonance fingerprinting review part 2: Technique and directions

J Magn Reson Imaging. 2020 Apr;51(4):993-1007. doi: 10.1002/jmri.26877. Epub 2019 Jul 25.

Authors

Debra F McGivney¹, Rasim Boyacıoğlu¹, Yun Jiang^{1

2}, Megan E Poorman^{3

4}, Nicole Seiberlich^{5

2}, Vikas Gulani^{1

2}, Kathryn E Keenan⁴, Mark A Griswold¹, Dan Ma¹

Affiliations

¹ Department of Radiology, Case Western Reserve University, Cleveland, Ohio, USA.
² Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
³ Department of Physics, University of Colorado Boulder, Boulder, Colorado, USA.
⁴ Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA.
⁵ Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.

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.

Keywords: deep learning; machine learning; magnetic resonance fingerprinting; optimization; reconstruction.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review

MeSH terms

Algorithms*
Brain
Image Processing, Computer-Assisted
Magnetic Resonance Imaging*
Magnetic Resonance Spectroscopy
Phantoms, Imaging

Abstract

Publication types

MeSH terms

Grants and funding