Advertisement

Machine Learning Based Compartment Models with Permeability for White Matter Microstructure Imaging

  • Gemma L. Nedjati-Gilani
  • Torben Schneider
  • Matt G. Hall
  • Claudia A. M. Wheeler-Kingshott
  • Daniel C. Alexander
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8675)

Abstract

The residence time τ i of water inside axons is an important biomarker for white matter pathologies of the human central nervous system, as myelin damage is hypothesised to increase axonal permeability, and thus reduce τ i . Diffusion-weighted (DW) MRI is potentially able to measure τ i as it is sensitive to the average displacement of water molecules in tissue. However, previous work addressing this has been hampered by a lack of both sensitive data and accurate mathematical models. We address the latter problem by constructing a computational model using Monte Carlo simulations and machine learning in order to learn a mapping between features derived from DW MR signals and ground truth microstructure parameters. We test our method using simulated and in vivo human brain data. Simulation results show that our approach provides a marked improvement over the most widely used mathematical model. The trained model also predicts sensible microstructure parameters from in vivo human brain data, matching values of τ i found in the literature.

Keywords

White Matter Random Forest Microstructure Parameter White Matter Pathology Parallel Cylinder 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Aboitiz, F., Scheibel, A.B., Fisher, R.S., Zaidel, E.: Fiber composition of the human corpus callosum. Brain Res. 598, 143–153 (1992)CrossRefGoogle Scholar
  2. 2.
    Alexander, D.C.: A general framework for experiment design in diffusion MRI and its application in measuring direct tissue-microstructure features. Mag. Res. Med. 60, 439–448 (2008)CrossRefGoogle Scholar
  3. 3.
    Alexander, D.C., Hubbard, P.L., Hall, M.G., Moore, E.A., Ptito, M., Parker, G.J., Dyrby, T.B.: Orientationally invariant indices of axon diameter and density from diffusion MRI. Neuroimage 52, 1374–1389 (2010)CrossRefGoogle Scholar
  4. 4.
    Assaf, Y., Blumenfeld-Katzir, T., Yovel, Y., Basser, P.J.: AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Mag. Res. Med. 59, 1347–1354 (2008)CrossRefGoogle Scholar
  5. 5.
    Criminisi, A., Shotton, J., Konukoglu, E.: Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning. Microsoft Research technical report (2011)Google Scholar
  6. 6.
    Hall, M.G., Alexander, D.C.: Convergence and parameter choice for Monte-Carlo simulations of diusion MRI. IEEE Trans. Med. Im. 28, 1354–1364 (2009)CrossRefGoogle Scholar
  7. 7.
    Kärger, J., Pfeifer, H., Wilfried, H.: Principles and application of self-diffusion measurements by nuclear magnetic resonance. Adv. Mag. Res. 12, 1–89 (1988)CrossRefGoogle Scholar
  8. 8.
    Nilsson, M., Alerstam, E., Wirestam, R., Ståhlberg, F., Brockstedt, S., Lätt, J.: Evaluating the accuracy and precision of a two-compartment Kärger model using Monte Carlo simulations. J. Mag. Res. 206, 59–67 (2010)CrossRefGoogle Scholar
  9. 9.
    Nilsson, M., Lätt, J., van Westen, D., Brockstedt, S., Lasič, S., Ståhlberg, F., Topgaard, D.: Noninvasive mapping of water diffusional exchange in the human brain using filter-exchange imaging. Mag. Res. Med. 69, 1573–1581 (2013)CrossRefGoogle Scholar
  10. 10.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E., Pedregosa, F., et al.: Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)zbMATHMathSciNetGoogle Scholar
  11. 11.
    Quirk, J.D., Bretthorst, G.L., Duong, T.Q., Snyder, A.Z., Springer, C.S., Ackerman, J.J.H., Neil, J.J.: Equilibrium water exchange between the intra- and extracellular spaces of mammalian brain. Mag. Res. Med. 50, 493–499 (2003)CrossRefGoogle Scholar
  12. 12.
    Stanisz, G.J., Wright, G.A., Henkelman, R.M., Szafer, A.: An analytical model of restricted diffusion in bovine optic nerve. Mag. Res. Med. 37, 103–111 (1997)CrossRefGoogle Scholar
  13. 13.
    Westin, C.-F., Maier, S.E., Mamata, H., Nabavi, A., Jolesz, F.A., Kikinis, R.: Processing and visualization for diffusion tensor MRI. Med. Im. Analysis 6, 93–108 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Gemma L. Nedjati-Gilani
    • 1
  • Torben Schneider
    • 2
  • Matt G. Hall
    • 1
  • Claudia A. M. Wheeler-Kingshott
    • 2
  • Daniel C. Alexander
    • 1
  1. 1.Centre for Medical Image Computing and Dept. of Computer ScienceUniversity College LondonLondonUK
  2. 2.Dept. of Neuroinflammation, Institute of NeurologyUniversity College LondonLondonUK

Personalised recommendations