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Computational Diffusion MRI pp 117–128Cite as

Probabilistic Tractography for Complex Fiber Orientations with Automatic Model Selection

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Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Fiber tractography aims to reconstruct white matter (WM) connections in the brain. Challenges in these reconstructions include estimation of the fiber orientations in regions with multiple fiber populations, and the uncertainty in the fiber orientations as a result of noise. In this work, we use a range of multi-tensor models to cope with crossing fibers. The uncertainty in fiber orientation is captured using the Cramér-Rao lower bound. Furthermore, model selection is performed based on model complexity and goodness of fit. The performance of the framework on the fibercup phantom and human data was compared to the open source diffusion MRI toolkit Camino for a range of SNRs. Performance was quantified by using the Tractometer measures in the fibercup phantom and by comparing streamline counts of lateral projections of the corpus callosum (CC) in the human data. On the phantom data, the comparison showed that our method performs similar to Camino in crossing fiber regions, whilst performing better in a region with kissing fibers (median angular error of 0.73∘ vs 2.7∘, valid connections of 57% vs 21% when seed is in the corresponding region of interest). Furthermore, the amount of counts in the lateral projections was found to be higher using our method (19–89% increase depending on a subject). Altogether, our method outperforms the reference method on both phantom and human data allowing for in-vivo probabilistic multi fiber tractography with an objective model selection procedure.

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Author information

Authors and Affiliations

  1. Imaging Physics, Delft University of Technology, Delft, The Netherlands

    Edwin Versteeg, Frans M. Vos, Joor A. M. Arkesteijn & Olena Filatova

  2. Department of Radiology, Academic Medical Center, Amsterdam, The Netherlands

    Frans M. Vos

  3. Department of Rehabilitation Medicine, VU University Medical Center, Amsterdam, The Netherlands

    Gert Kwakkel

  4. BioMechanical Engineering Department, Delft University of Technology, Delft, The Netherlands

    Frans C. T. van der Helm & Olena Filatova

Authors
  1. Edwin Versteeg
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  2. Frans M. Vos
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  3. Gert Kwakkel
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  4. Frans C. T. van der Helm
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  5. Joor A. M. Arkesteijn
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  6. Olena Filatova
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Corresponding author

Correspondence to Olena Filatova .

Editor information

Editors and Affiliations

  1. Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom

    Enrico Kaden

  2. Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom

    Francesco Grussu

  3. Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Lipeng Ning

  4. Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom

    Chantal M. W. Tax

  5. Center for Biomedical Imaging, New York University School of Medicine, New York, New York, USA

    Jelle Veraart

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Versteeg, E., Vos, F.M., Kwakkel, G., van der Helm, F.C.T., Arkesteijn, J.A.M., Filatova, O. (2018). Probabilistic Tractography for Complex Fiber Orientations with Automatic Model Selection. In: Kaden, E., Grussu, F., Ning, L., Tax, C., Veraart, J. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-73839-0_9

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