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Magnetic resonance fingerprinting

Author

Listed:
  • Dan Ma

    (Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, USA)

  • Vikas Gulani

    (Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, USA
    Case Western Reserve University and University Hospitals of Cleveland, 11100 Euclid Avenue, Cleveland, Ohio 44106, USA)

  • Nicole Seiberlich

    (Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, USA)

  • Kecheng Liu

    (Siemens Healthcare USA, 51 Valley Stream Parkway, Malvern, Pennsylvania 19355, USA)

  • Jeffrey L. Sunshine

    (Case Western Reserve University and University Hospitals of Cleveland, 11100 Euclid Avenue, Cleveland, Ohio 44106, USA)

  • Jeffrey L. Duerk

    (Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, USA
    Case Western Reserve University and University Hospitals of Cleveland, 11100 Euclid Avenue, Cleveland, Ohio 44106, USA)

  • Mark A. Griswold

    (Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, USA
    Case Western Reserve University and University Hospitals of Cleveland, 11100 Euclid Avenue, Cleveland, Ohio 44106, USA)

Abstract

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.

Suggested Citation

  • Dan Ma & Vikas Gulani & Nicole Seiberlich & Kecheng Liu & Jeffrey L. Sunshine & Jeffrey L. Duerk & Mark A. Griswold, 2013. "Magnetic resonance fingerprinting," Nature, Nature, vol. 495(7440), pages 187-192, March.
    • Handle: RePEc:nat:nature:v:495:y:2013:i:7440:d:10.1038_nature11971
    DOI: 10.1038/nature11971
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    Citations

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    Cited by:

    1. Viktor Vegh & Shahrzad Moinian & Qianqian Yang & David C. Reutens, 2021. "Fractional Order Magnetic Resonance Fingerprinting in the Human Cerebral Cortex," Mathematics, MDPI, vol. 9(13), pages 1-21, July.
    2. Gastao Cruz & Torben Schneider & Tom Bruijnen & Andreia S Gaspar & René M Botnar & Claudia Prieto, 2018. "Accelerated magnetic resonance fingerprinting using soft-weighted key-hole (MRF-SOHO)," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-18, August.
    3. Christos G Xanthis & Anthony H Aletras, 2019. "coreMRI: A high-performance, publicly available MR simulation platform on the cloud," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-26, May.
    4. Nikita Sushentsev & Joshua D Kaggie & Rhys A Slough & Bruno Carmo & Tristan Barrett, 2021. "Reproducibility of magnetic resonance fingerprinting-based T1 mapping of the healthy prostate at 1.5 and 3.0 T: A proof-of-concept study," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-13, January.
    5. Ziyue Wu & Weiyi Chen & Krishna S Nayak, 2016. "Minimum Field Strength Simulator for Proton Density Weighted MRI," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-15, May.
    6. Selma Metzner & Gerd Wübbeler & Clemens Elster, 2019. "Approximate large-scale Bayesian spatial modeling with application to quantitative magnetic resonance imaging," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(3), pages 333-355, September.

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