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Magnetic resonance fingerprinting

Nature volume 495, pages 187–192 (2013)Cite this article

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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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Figure 1: MRF sequence pattern.
Figure 2: Signal properties and matching results from phantom study.
Figure 3: MRF results from highly undersampled data.
Figure 4: Demonstration of error tolerance in the presence of motion.
Figure 5: Accuracy, efficiency and error estimation for MRF and DESPOT.

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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.

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Authors and 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

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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.

Corresponding author

Correspondence to Mark A. Griswold.

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Competing interests

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

Supplementary information

Supplementary Information

This file contains Supplementary Text and Data 1-3, Supplementary Figures 1-3 and additional references. (PDF 470 kb)

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. (AVI 979 kb)

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. (AVI 1521 kb)

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. (AVI 10015 kb)

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Ma, D., Gulani, V., Seiberlich, N. et al. Magnetic resonance fingerprinting. Nature 495, 187–192 (2013). https://doi.org/10.1038/nature11971

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