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)


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.


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.


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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

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