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Magnetic resonance fingerprinting
Open accessAbstract
Magnetic resonance is an exceptionally powerful and versatile measurement technique. The basic structure of a magnetic resonance experiment has remained largely unchanged for almost 50 years, being mainly restricted to the qualitative probing of only a limited set of the properties that can in principle be accessed by this technique. Here we introduce an approach to data acquisition, post-processing and visualization--which we term 'magnetic resonance fingerprinting' (MRF)--that permits the simultaneous non-invasive quantification of multiple important properties of a material or tissue. MRF thus provides an alternative way to quantitatively detect and analyse complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to identify the presence of a specific target material or tissue, which will increase the sensitivity, specificity and speed of a magnetic resonance study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern-recognition algorithm, MRF inherently suppresses measurement errors and can thus improve measurement accuracy.
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“It uses a pseudo-randomized acquisition that causes the signals from different tissues to have a unique signal evolution ('fingerprint') that is a function of the multiple material properties being investigated.”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [150]
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Metadata
- Authors
- Ma, D., Gulani, V., Seiberlich, N., Liu, K., Sunshine, J. L., Duerk, J. L., & Griswold, M. A.
- Affiliations
- Case Western Reserve University (US)
- University Hospitals of Cleveland (US)
- Siemens Healthcare (United States) (US)
- Corresponding author
- Griswold, Mark A.
- ORCID
- Venue
- Nature
- Published
- 2013-03
- Volume
- 495
- Issue
- 7440
- Pages
- 187-192
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3602925
- OpenAlex
- W2144288697
- Cited by
- 1,577
- References
- 53
- Citations by year
- 2026: 115 · 2025: 147 · 2024: 148 · 2023: 160 · 2022: 149 · 2021: 175 · 2020: 168 · 2019: 157 · 2018: 108 · 2017: 101 · 2016: 65 · 2015: 42 · 2014: 26 · 2013: 10
- FWCI
- 36.92
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- OhioLink ETD Center (Ohio Library and Information Network) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-20-CE19-0030, ANR-11-INBS-0006, ANR-21-CE17-0028)
- National Institutes of Health (KL2TR000440)
- National Heart, Lung, and Blood Institute (R01HL094557)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Advanced NMR Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › NMR spectroscopy and applications
- Keywords
- magnetic resonance fingerprinting, quantitative MRI, pattern recognition, tissue characterization, disease detection, non-invasive measurement, nuclear magnetic resonance
- MeSH terms
- Algorithms, Diagnostic Tests, Routine, Humans, Motion, Magnetic Resonance Spectroscopy, Pattern Recognition, Automated, Research Design, Sensitivity and Specificity, Reproducibility of Results, Phantoms, Imaging
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