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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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References

  1. Bartzokis, G. et al. In vivo evaluation of brain iron in Alzheimer disease using magnetic resonance imaging. Arch. Gen. Psychiatry 57, 47–53 (2000).

    Article  CAS  Google Scholar 

  2. Larsson, H. B. et al. Assessment of demyelination, edema, and gliosis by in vivo determination of T1 and T2 in the brain of patients with acute attack of multiple sclerosis. Magn. Reson. Med. 11, 337–348 (1989)

    Article  CAS  Google Scholar 

  3. Pitkänen, A. et al. Severity of hippocampal atrophy correlates with the prolongation of MRI T2 relaxation time in temporal lobe epilepsy but not in Alzheimer’s disease. Neurology 46, 1724–1730 (1996)

    Article  Google Scholar 

  4. Williamson, P. et al. Frontal, temporal, and striatal proton relaxation times in schizophrenic patients and normal comparison subjects. Am. J. Psychiatry 149, 549–551 (1992)

    Article  CAS  Google Scholar 

  5. Warntjes, J. B. Dahlqvist, O. & Lundberg, P. Novel method for rapid, simultaneous T1, T*2, and proton density quantification. Magn. Reson. Med. 57, 528–537 (2007)

    Article  CAS  Google Scholar 

  6. Warntjes, J. B. Leinhard, O. D., West, J. & Lundberg, P. Rapid magnetic resonance quantification on the brain: optimization for clinical usage. Magn. Reson. Med. 60, 320–329 (2008)

    Article  CAS  Google Scholar 

  7. Schmitt, P. et al. Inversion recovery TrueFISP: quantification of T1, T2, and spin density. Magn. Reson. Med. 51, 661–667 (2004)

    Article  Google Scholar 

  8. Ehses, P. et al. IR TrueFISP with a golden-ratio-based radial readout: fast quantification of T1, T2, and proton density. Magn. Reson. Med. 69, 71–81 (2013)

    Article  Google Scholar 

  9. Donoho, D. L. Compressed sensing. IEEE Trans. Inform. Theory 52, 1289–1306 (2006)

    Article  MathSciNet  Google Scholar 

  10. Candes, E. J. & Tao, T. Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inform. Theory 52, 5406–5425 (2006)

    Article  MathSciNet  Google Scholar 

  11. Lustig, M., Donoho, D. L. & Pauly, J. M. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58, 1182–1195 (2007)

    Article  Google Scholar 

  12. Bilgic, B., Goyal, V. K. & Adalsteinsson, E. Multi-contrast reconstruction with Bayesian compressed sensing. Magn. Reson. Med. 66, 1601–1615 (2011)

    Article  Google Scholar 

  13. Smith, D. S. et al. Robustness of quantitative compressive sensing MRI: the effect of random undersampling patterns on derived parameters for DCE- and DSC-MRI. IEEE Trans. Med. Imaging 31, 504–511 (2012)

    Article  Google Scholar 

  14. Deshpande, V. S., Chung, Y.-C., Zhang, Q., Shea, S. M. & Li, D. Reduction of transient signal oscillations in True-FISP using a linear flip angle series magnetization preparation. Magn. Reson. Med. 49, 151–157 (2003)

    Article  Google Scholar 

  15. Çukur, T. Multiple repetition time balanced steady-state free precession imaging. Magn. Reson. Med. 62, 193–204 (2009)

    Article  Google Scholar 

  16. Nayak, K. & Lee, H. Wideband SSFP: alternating repetition time balanced steady state free precession with increased band spacing. Magn. Reson. Med. 58, 931–938 (2007)

    Article  Google Scholar 

  17. Lee, K., Lee, H. & Hennig, J. Use of simulated annealing for the design of multiple repetition time balanced steady-state free precession imaging. Magn. Reson. Med. 68, 220–226 (2012)

    Article  Google Scholar 

  18. Ernst, R. R. Magnetic resonance with stochastic excitation. J. Magn. Reson. 3, 10–27 (1970)

    ADS  CAS  Google Scholar 

  19. Scheffler, K. & Hennig, J. Frequency resolved single-shot MR imaging using stochastic k-space trajectories. Magn. Reson. Med. 35, 569–576 (1996)

    Article  CAS  Google Scholar 

  20. Haldar, J. P., Hernando, D. & Liang, Z.-P. Compressed-sensing MRI with random encoding. IEEE Trans. Med. Imaging 30, 893–903 (2011)

    Article  Google Scholar 

  21. Doneva, M. et al. Compressed sensing reconstruction for magnetic resonance parameter mapping. Magn. Reson. Med. 64, 1114–1120 (2010)

    Article  Google Scholar 

  22. Stoecker, T., Vahedipour, K., Pracht, E., Brenner, D. & Shah, N. J. in Proc. 19th Scientific Meeting International Society for Magnetic Resonance in Medicine 381 (Int. Soc. Magn. Reson. Med., 2011)

    Google Scholar 

  23. Davenport, M. A., Wakin, M. B. & Baraniuk, R. G. The Compressive Matched Filter (Tech. Rep. TREE 0610, Rice University, 2006)

    Google Scholar 

  24. Tropp, J. A. & Gilbert, A. C. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inform. Theory 53, 4655–4666 (2007)

    Article  MathSciNet  Google Scholar 

  25. Schmitt, P. et al. A simple geometrical description of the TrueFISP ideal transient and steady-state signal. Magn. Reson. Med. 55, 177–186 (2006)

    Article  CAS  Google Scholar 

  26. Lee, J. H., Hargreaves, B. A., Hu, B. S. & Nishimura, D. G. Fast 3D imaging using variable-density spiral trajectories with applications to limb perfusion. Magn. Reson. Med. 50, 1276–1285 (2003)

    Article  Google Scholar 

  27. Marseille, G., De Beer, R., Fuderer, M., Mehlkopf, A. & Van Ormondt, D. Nonuniform phase-encode distributions for MRI scan time reduction. J. Magn. Reson. B. 111, 70–75 (1996)

    Article  CAS  Google Scholar 

  28. Tsai, C. M. & Nishimura, D. G. Reduced aliasing artifacts using variable-density K-space sampling trajectories. Magn. Reson. Med. 43, 452–458 (2000)

    Article  CAS  Google Scholar 

  29. Vymazal, J. et al. T1 and T2 in the brain of healthy subjects, patients with Parkinson disease, and patients with multiple system atrophy: relation to iron content. Radiology 211, 489–495 (1999)

    Article  CAS  Google Scholar 

  30. Deoni, S. C. L., Peters, T. M. & Rutt, B. K. High-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magn. Reson. Med. 53, 237–241 (2005)

    Article  Google Scholar 

  31. Whittall, K. P. et al. In vivo measurement of T2 distributions and water contents in normal human brain. Magn. Reson. Med. 37, 34–43 (1997)

    Article  CAS  Google Scholar 

  32. Poon, C. S. & Henkelman, R. M. Practical T2 quantitation for clinical applications. J. Magn. Reson. Imaging 2, 541–553 (1992)

    Article  CAS  Google Scholar 

  33. Haacke, E. M., Brown, R. W., Thompson, M. R. & Venkatesan, R. Magnetic Resonance Imaging: Physical Principles and Sequence Design 669–675 (Wiley & Sons, 1999)

    Google Scholar 

  34. Hahn, E. L. Spin echoes. Phys. Rev. 80, 580–594 (1950)

    Article  ADS  Google Scholar 

  35. Crawley, A. P. & Henkelman, R. M. A Comparison of one-shot and recovery methods in T1 imaging. Magn. Reson. Med. 7, 23–34 (1988)

    Article  CAS  Google Scholar 

  36. Deoni, S. C. L., Rutt, B. K. & Peters, T. M. Rapid combined T1 and T2 mapping using gradient recalled acquisition in the steady state. Magn. Reson. Med. 49, 515–526 (2003)

    Article  Google Scholar 

  37. Scheffler, K. & Lehnhardt, S. Principles and applications of balanced SSFP techniques. Eur. Radiol. 13, 2409–2418 (2003)

    Article  Google Scholar 

  38. Needell, D. & Tropp, J. A. Cosamp: iterative signal recovery from incomplete and inaccurate samples. Appl. Comput. Harmon. Anal. 26, 301–321 (2009)

    Article  MathSciNet  Google Scholar 

  39. Goldstein, T. & Osher, S. The split Bregman method for L1-regularized problems. SIAM J. Imaging Sci. 2, 323–343 (2009)

    Article  MathSciNet  Google Scholar 

  40. Chartrand, R. & Yin, W. in Proc. ICASSP 2008 IEEE International Conference 3869–3872 (IEEE, 2008)

    Google Scholar 

  41. Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S. & Ma, Y. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009)

    Article  Google Scholar 

  42. Turk, M. & Pentland, A. Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)

    Article  CAS  Google Scholar 

  43. Griswold, M. A. et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 47, 1202–1210 (2002)

    Article  Google Scholar 

  44. Pruessmann, K. P. et al. SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42, 952–962 (1999)

    Article  CAS  Google Scholar 

  45. Seiberlich, N., Ehses, P., Duerk, J., Gilkeson, R. & Griswold, M. A. Improved radial GRAPPA calibration for real-time free-breathing cardiac imaging. Magn. Reson. Med. 65, 492–505 (2011)

    Article  Google Scholar 

  46. Heidemann, R. M. et al. Direct parallel image reconstructions for spiral trajectories using GRAPPA. Magn. Reson. Med. 56, 317–326 (2006)

    Article  Google Scholar 

  47. Barger, A. V., Block, W. F., Toropov, Y., Grist, T. M. & Mistretta, C. A. Time-resolved contrast-enhanced imaging with isotropic resolution and broad coverage using an undersampled 3D projection trajectory. Magn. Reson. Med. 48, 297–305 (2002)

    Article  Google Scholar 

  48. Perlin, K. An image synthesizer. Comput. Graphics 19, 287–296 (1985)

    Article  Google Scholar 

  49. Hargreaves, B. A., Nishimura, D. G. & Conolly, S. M. Time-optimal multidimensional gradient waveform design for rapid imaging. Magn. Reson. Med. 51, 81–92 (2004)

    Article  Google Scholar 

  50. Fessler, J. A. & Sutton, B. P. Nonuniform fast Fourier transforms using min-max interpolation. IEEE Trans. Signal Process. 51, 560–574 (2003)

    Article  ADS  MathSciNet  Google Scholar 

  51. Riffe, M. J., Blaimer, M., Barkauskas, K. J., Duerk, J. L. & Griswold, M. A. in Proc. 15th Scientific Meeting, International Society for Magnetic Resonance in Medicine 1879 (Int. Soc. Magn. Reson. Med, 2007)

    Google Scholar 

  52. Lin, L. I.-K. A concordance correlation coefficient to evaluate reproducibility. Biometrics 45, 255–268 (1989)

    Article  CAS  Google Scholar 

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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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