Article

Magnetic resonance fingerprinting

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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.

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Acknowledgements

Support for this study was provided by NIH R01HL094557 and Siemens Healthcare. We also thank H. Saybasili and G. Lee for technical assistance during the implementation of these concepts; M. Lustig and W. Grissom for discussions regarding this work; and A. Exner, S. Brady-Kalnay, E. Karathanasis, E. Lavik and H. Salz for their assistance in preparing the manuscript.

Author information

Affiliations

  1. Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, USA

    • Dan Ma
    • , Vikas Gulani
    • , Nicole Seiberlich
    • , Jeffrey L. Duerk
    •  & Mark A. Griswold
  2. Department of Radiology, Case Western Reserve University and University Hospitals of Cleveland, 11100 Euclid Avenue, Cleveland, Ohio 44106, USA

    • Vikas Gulani
    • , Jeffrey L. Sunshine
    • , Jeffrey L. Duerk
    •  & Mark A. Griswold
  3. Siemens Healthcare USA, 51 Valley Stream Parkway, Malvern, Pennsylvania 19355, USA

    • Kecheng Liu

Authors

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Contributions

D.M., concept development, technical implementation, data collection and analysis, manuscript development and editing; V.G., concept development, manuscript development and editing; N.S., concept development, manuscript development and editing; K.L., concept development, technical implementation, manuscript development and editing; J.L.S., concept development, manuscript development and editing; J.L.D., concept development, manuscript development and editing; M.A.G., concept development, data collection and analysis, manuscript development and editing.

Competing interests

This work was supported by Siemens Healthcare. K.L. is an employee of Siemens Healthcare.

Corresponding author

Correspondence to Mark A. Griswold.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains Supplementary Text and Data 1-3, Supplementary Figures 1-3 and additional references.

Videos

  1. 1.

    Time resolved in vivo images acquired from fully sampled MRF scan.

    The video covers the first 50 time frames. Oscillations in signal intensity appear across all of the time frames.

  2. 2.

    Time resolved in vivo images generated from an accelerated MRF scan that acquired only 1/48th of the normally required data.

    The video covers the first 50 time frames out of 1000. High intensity but incoherent undersampling errors are present in all time frames.

  3. 3.

    The motion corrupted scan.

    The subject started to move after 12 seconds of a 15 seconds acquisition. The video clearly shows the motion as well as severe aliasing artifacts from the highly undersampled data.