Magnetic resonance fingerprinting (original) (raw)

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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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

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

Authors

  1. Dan Ma
  2. Vikas Gulani
  3. Nicole Seiberlich
  4. Kecheng Liu
  5. Jeffrey L. Sunshine
  6. Jeffrey L. Duerk
  7. Mark A. Griswold

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 toMark A. Griswold.

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Competing interests

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

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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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  1. Noam Harel 30 March 2013, 20:45
    This is a potentially revolutionary technique. As a non-MRI neuroscientist and practicing neurologist, I have a few questions and comments:
    --How does the resolution of MRF compare with conventional MRI? The images look grainy.
    --Currently, it is difficult to differentiate certain findings using brain MRI ? for example, tumor recurrence vs post-operative inflammation; toxoplasmosis vs lymphoma. Will MRF solve this? Would love to see a confirmatory study comparing MRF and conventional MRI to biopsy results.
    --Many clinicians, including me, take shortcuts when reviewing MRIs ? ie, I?ll check the DWI and FLAIR sequences, but usually not the T1, T2, PD, Echo, etc sequences. Will MRF really be able to include all the relevant information on one set of images??
    --The ?dictionary? used by the pattern-recognition software is based on >500,000 different pseudo-randomized scanning protocols. If I understand correctly, do you suggest that eventually, one ?best? scanning protocol will maximize the uniqueness of each fingerprint? Or, do different hardware setups necessitate a library of different scanning protocols?
    --The advantage of rapid scanning as a selling point cannot be overstated. Aside from the direct benefit of reduced cost, rapid MRF scanning would reduce waiting times for clinical MRI, and would increase image quality, since there would be less time for subjects to move around during the scan.
    --Will MRF?s ability to ?ignore? motion and other artifacts really hold up in the real world? In the example cited in the article, the algorithm essentially ignored the final 3s of a 15s scan. But in real life, a subject will move randomly throughout the scanning process. Will MRF be robust enough to withstand motion in an agitated subject? A child?
    Thank you and best of luck further establishing this technique!
    Noam Y. Harel, New York
  2. Mark Griswold 10 April 2013, 18:44
    Dear Noam,
    Sorry for my delay in responding to your questions. Thanks so much for your questions. I've addressed them in order here below:
  1. The images are currently about 2mm in plane, which is certainly worse than a lot of clinical images used today. We have recently gotten this down into the clinical 1mm range, and will continue working towards even better resolution. That said, I believe that the quantitative specificity in MRF provides the opportunity to use different kinds of analyses that won't necessarily require high spatial resolutions. In particular, we show a tissue-specific visualization in the supplemental material that will help resolve the content of voxels with mixed content. We hope that this will relax the resolution requirements a little bit.
  2. We hope that MRF will be able to help in these areas. The key point here is the quantitative sensitivity/efficiency of the method. Right now we are at ~15ms precision in T1 in 12 seconds of scan time. (This was at 1.5T by the way. 3T is better yet.) We would be below 10ms precision with just a few minutes of scanning, with sub-1ms precision in T2. We certainly hope that we can start to piece together very specific markers for these kinds of tissue changes at this level of precision... but this is to be proven down the road.
  3. Yes, we are working on a lot of different flexible ways to look at the data. We already have "push button" displays for all of the common contrasts and you can just toggle between them without any issue. They are all obviously coregistered. In addition, if you look at the News and Views on MRF written by Brian Welch you will see an example color visualization that we've been working on. I think we're still a ways away from a clinically routine tool, but I certainly think that there will be useful visualizations that take advantage of our ability to see color that will improve the interpretation of MRF images.
  4. We believe that there will be optimized sequences for any given hardware setup that should optimize the extraction of the information you want. So if you want e.g. T1, T2, M0, B0 and diffusion, the scanner will calculate the optimized sequence for you. There won't be very many knobs to turn. And this sequence will be different from one where you only want M0, T2 and diffusion, for example. This will hopefully be optimized on the fly.
  5. We agree with the point about rapid scanning. Most of our lab is dedicated to this point! We are now deploying our full "bag of tricks" to make MRF really much faster with better coverage.
  6. Yes, it should at least be better than a conventional scan in the sense that artifacts from motion shouldn't propagate through the entire data set. What the pattern recognition algorithm
    is essentially doing is asking "which tissue/material is most likely for this pixel?" So if the voxel is 80% white matter and 20% gray matter, the result for the "most likely tissue" will be white matter. There are of course subtle effects that have to be taken care of, but we hope that this will at a minimum be an improvement over what's done today. In terms of imaging during very rapid, agitated motions, etc I don't think that MRF will do magic here. This is where fast-real time conventional qualitative imaging will still probably have an advantage. However, we hope that MRF will have a direct, positive impact on a many standard cases where there is only moderate motion, or lack of compliance for breath holding, etc.
    Thanks again for your interest in MRF and for your great questions! Please feel free to contact me if you have any further questions.
    Best wishes,
    Mark A Griswold, Cleveland

Editorial Summary

Raising the profile of NMR

Although nuclear magnetic resonance is a powerful analytical tool for many scientific and medical disciplines, usually only a fraction of its potential power is harnessed. Most implementations are qualitative, and restricted in the range of properties that are probed. Dan Ma and colleagues introduce a new approach — termed magnetic resonance fingerprinting — aimed at greatly enhancing the amount of quantitative information that can be obtained in one measurement. Their approach combines a data-acquisition scheme that is indiscriminate in the material properties that it probes with pattern-recognition algorithms that look for the 'fingerprints' of interest within the data. Magnetic resonance fingerprinting has the potential to detect and analyse early indicators of disease or complex changes in materials, as well as increasing the sensitivity, specificity and speed of magnetic resonance studies.

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