Probabilistic tractography provides estimates of the probability of a structural connection between points or regions in a brain volume, based on information from diffusion MRI. The ability to estimate the uncertainty associated with reconstructed pathways is valuable, but noise in the image data leads to premature termination or erroneous trajectories in sampled streamlines. In this work we describe automated methods, based on a probabilistic model of tract shape variability between individuals, which can be applied to select seed points in order to maximise consistency in tract segmentation; and to discard streamlines which are unlikely to belong to the tract of interest. Our method is shown to ameliorate false positives and remove the widely observed falloff in connection probability with distance from the seed region due to noise, two important problems in the tractography literature. Moreover, the need to apply an arbitrary threshold to connection probability maps is entirely obviated by our approach, thus removing a significant user-specified parameter from the tractography pipeline.


Fractional Anisotropy Seed Region Orientation Distribution Function Seed Point Markov Chain Monte Carlo Method 
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.


  1. 1.
    Jones, D.: Studying connections in the living human brain with diffusion MRI. Cortex 44, 936–952 (2008)CrossRefGoogle Scholar
  2. 2.
    Morris, D., Embleton, K., Parker, G.: Probabilistic fibre tracking: differentiation of connections from chance events. NeuroImage 42, 1329–1339 (2008)CrossRefGoogle Scholar
  3. 3.
    Clayden, J., Storkey, A., Bastin, M.: A probabilistic model-based approach to consistent white matter tract segmentation. IEEE Trans. Med. Imag. 26, 1555–1561 (2007)CrossRefGoogle Scholar
  4. 4.
    Maddah, M., Zöllei, L., Grimson, W., Westin, C.F., Wells, W.: A mathematical framework for incorporating anatomical knowledge in DT-MRI analysis. In: Proc. ISBI, pp. 105–108 (2008)Google Scholar
  5. 5.
    Clayden, J., Storkey, A., Muñoz Maniega, S., Bastin, M.: Reproducibility of tract segmentation between sessions using an unsupervised modelling-based approach. NeuroImage 45, 377–385 (2009)CrossRefGoogle Scholar
  6. 6.
    Behrens, T., Woolrich, M., Jenkinson, M., Johansen-Berg, H., Nunes, R., Clare, S., Matthews, P., Brady, J., Smith, S.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50, 1077–1088 (2003)CrossRefGoogle Scholar
  7. 7.
    Hua, K., Zhang, J., Wakana, S., Jiang, H., Li, X., Reich, D., Calabresi, P., Pekar, J., van Zijl, P., Mori, S.: Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification. NeuroImage 39, 336–347 (2008)CrossRefGoogle Scholar
  8. 8.
    Muñoz Maniega, S., Bastin, M., McIntosh, A., Lawrie, S., Clayden, J.: Atlas-based reference tracts improve automatic white matter segmentation with neighbourhood tractography. In: Proc. ISMRM, p. 3318 (2008)Google Scholar
  9. 9.
    Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001)CrossRefGoogle Scholar
  10. 10.
    Bates, D., Pinheiro, J.: Computational methods for multilevel modelling. Technical report, Bell Laboratories (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jonathan D. Clayden
    • 1
  • Martin D. King
    • 1
  • Chris A. Clark
    • 1
  1. 1.Institute of Child HealthUniversity College LondonUK

Personalised recommendations