The DOI matches its Crossref record; 52 of the 53 references it cites were found in a registry or an index.
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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Cites
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An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012
AI applications to medical images: From machine learning to deep learning
Castiglioni, I., Rundo, L., Codari, M., Leo, G. D., Salvatore, C., Interlenghi, M., Gallivanone, F., Cozzi, A., D’Amico, N. C., & Sardanelli, F. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.02.006 · Cited by 779
A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology
Iglesias, J. E., Insausti, R., Lerma‐Usabiaga, G., Bocchetta, M., Leemput, K. V., Greve, D. N., van der Kouwe, A. J. W., Fischl, B. R., Caballero‐Gaudes, C., & Paz‐Alonso, P. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.012 · Cited by 575
Pros and cons of ultra-high-field MRI/MRS for human application
Ladd, M. E., Bachert, P., Meyerspeer, M., Moser, E., Nagel, A. M., Norris, D. G., Schmitter, S., Speck, O., Straub, S., & Zaiß, M. (2018) · Progress in Nuclear Magnetic Resonance Spectroscopy · doi:10.1016/j.pnmrs.2018.06.001 · Cited by 586
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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