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Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction

  • Oleh DzyubachykEmail author
  • Kirsten Koolstra
  • Nicola Pezzotti
  • Boudewijn P. F. Lelieveldt
  • Andrew Webb
  • Peter Börnert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11849)

Abstract

Quality assessment of different Magnetic Resonance Fingerprinting (MRF) sequences and their corresponding dictionaries remains an unsolved problem. In this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. First, we demonstrated stability of calculated embeddings that allows neglecting the stochastic nature of t-SNE. Next, we proposed and analyzed two algorithms for comparing the embeddings. Finally, we performed two simulations in which we reduced the MRF sequence/dictionary in length or size and analyzed the influence of this reduction on the resulting embedding. We believe that this research can pave the way to development of a software tool for analysis, including better understanding, optimization and comparison, of different MRF sequences.

Keywords

Dimensionality reduction t-SNE Magnetic Resonance Fingerprinting (MRF) Point cloud registration 

References

  1. 1.
    Ma, D., et al.: Magnetic resonance fingerprinting. Nature 495, 187 (2013)CrossRefGoogle Scholar
  2. 2.
    Jiang, Y., Ma, D., Seiberlich, N., Gulani, V., Griswold, M.A.: MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magn. Reson. Med. 74(6), 1621–1631 (2015)CrossRefGoogle Scholar
  3. 3.
    Cohen, O., Rosen, M.S.: Algorithm comparison for schedule optimization in MR fingerprinting. Magn. Reson. Imag. 41, 15–21 (2017)CrossRefGoogle Scholar
  4. 4.
    Van der Maaten, L., Postma, E., Van den Herik, J.: Dimensionality reduction: a comparative review. J. Mach. Learn. Res. 10, 66–71 (2009)Google Scholar
  5. 5.
    Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  6. 6.
    Chen, Y., Medioni, G.: Object modelling by registration of multiple range images. Image Vision Comput. 10(3), 145–155 (1992)CrossRefGoogle Scholar
  7. 7.
    Zinßer, T., Schmidt, J., Niemann, H.: Point set registration with integrated scale estimation. In: Proceedings of International Conference on Pattern Recognition and Information Processing, pp. 116–119, January 2005Google Scholar
  8. 8.
    Scheffler, K.: A pictorial description of steady-states in rapid magnetic resonance imaging. Concept. Magn. Reson. 11(5), 291–304 (1999)CrossRefGoogle Scholar
  9. 9.
    Pezzotti, N., Höllt, T., Lelieveldt, B.P.F., Eisemann, E., Vilanova, A.: Hierarchical stochastic neighbor embedding. Comput. Graph. Forum 35, 21–30 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Division of Image Processing, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
  2. 2.C.J. Gorter Center for High Field MRI, Department of RadiologyLeiden University Medical CenterLeidenThe Netherlands
  3. 3.Computer Graphics and Visualization GroupDelft University of TechnologyDelftThe Netherlands
  4. 4.Philips Research EindhovenEindhovenThe Netherlands
  5. 5.Intelligent Systems DepartmentDelft University of TechnologyDelftThe Netherlands
  6. 6.Philips Research HamburgHamburgGermany

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