Beyond Crossing Fibers: Tractography Exploiting Sub-voxel Fibre Dispersion and Neighbourhood Structure

  • Matthew Rowe
  • Hui Gary Zhang
  • Neil Oxtoby
  • Daniel C. Alexander
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


In this paper we propose a novel algorithm which leverages models of white matter fibre dispersion to improve tractography. Tractography methods exploit directional information from diffusion weighted magnetic resonance (DW-MR) imaging to infer connectivity between different brain regions. Most tractography methods use a single direction (e.g. the principal eigenvector of the diffusion tensor) or a small set of discrete directions (e.g. from the peaks of an orientation distribution function) to guide streamline propagation. This strategy ignores the effects of within-bundle orientation dispersion, which arises from fanning or bending at the sub-voxel scale, and can lead to missing connections. Various recent DW-MR imaging techniques estimate the fibre dispersion in each bundle directly and model it as a continuous distribution. Here we introduce an algorithm to exploit this information to improve tractography. The algorithm further uses a particle filter to probe local neighbourhood structure during streamline propagation. Using information gathered from neighbourhood structure enables the algorithm to resolve ambiguities between converging and diverging fanning structures, which cannot be distinguished from isolated orientation distribution functions. We demonstrate the advantages of the new approach in synthetic experiments and in vivo data. Synthetic experiments demonstrate the effectiveness of the particle filter in gathering and exploiting neighbourhood information in recovering various canonical fibre configurations and experiments with in vivo brain data demonstrate the advantages of utilising dispersion in tractography, providing benefits in practical situations.


Neighbourhood Structure Orientation Distribution Function Synthetic Experiment Inferior Longitudinal Fasciculus Anatomical Connectivity 
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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  1. 1.
    Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C.M.: Three dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265–269 (1999)CrossRefGoogle Scholar
  2. 2.
    Conturo, T.E., Lori, N.F., Cull, T.S., Akbudak, E., Snyder, A.Z., Shimony, J.S., McKinstry, R.C., Burton, H., Raichle, M.E.: Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. U.S.A. 96, 10422–10427 (1999)Google Scholar
  3. 3.
    Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vitro fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625–632 (2000)CrossRefGoogle Scholar
  4. 4.
    Parker, G.J., Alexander, D.C.: Probabilistic Monte Carlo based mapping of cerebral connections utilising whole-brain crossing fibre information. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 684–695. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Behrens, T.E.J., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A.M., Boulby, P.A., Barker, G.J., Sillery, E.L., Sheehan, K., Cicarelli, O., Thompson, A.J., Brady, J.M., Matthews, P.M.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50, 1077–1088 (2003)CrossRefGoogle Scholar
  6. 6.
    Parker, G.J., Alexander, D.C.: Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue. Philosophical Transactions of the Royal Society B: Biological Sciences 360, 893–902 (2005)CrossRefGoogle Scholar
  7. 7.
    Behrens, T.E.J., Johansen-Berg, H., Jbabdi, S., Rushworth, M.F.S., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage 34, 144–155 (2007)CrossRefGoogle Scholar
  8. 8.
    Tournier, J., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion mri: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35, 1459–1472 (2007)CrossRefGoogle Scholar
  9. 9.
    Jeurissen, B., Leemans, A., Tournier, J.D., Sijbers, J.: Estimation of uncertainty in constrained spherical deconvolution fiber orientations. In: IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 907–910 (2008)Google Scholar
  10. 10.
    Jeurissen, B., Leemans, A., Jones, D.K., Tournier, J.D., Sijbers, J.: Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Human Brain Mapping 32, 461–479 (2011)CrossRefGoogle Scholar
  11. 11.
    Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Probabilistic fibre tracking through regions containing crossing fibres. In: Proc. Intl. Soc. Mag. Reson. Med., vol. 13, p. 1343 (2005)Google Scholar
  12. 12.
    Sherbondy, A.J., Dougherty, R.F., Ananthanarayanan, R., Modha, D.S., Wandell, B.A.: Think Global, Act Local; Projectome Estimation with Bluematter. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 861–868. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Fillard, P., Poupon, C., Mangin, J.-F.: A Novel Global Tractography Algorithm Based on an Adaptive Spin Glass Model. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 927–934. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Kreher, B.W., Mader, I., Kiselev, V.G.: Gibbs Tracking: A Novel Approach for the Reconstruction of Neuronal Pathways. Magnetic Resonance in Medicine 60, 953–963 (2008)CrossRefGoogle Scholar
  15. 15.
    Sherbondy, A.J., Rowe, M.C., Alexander, D.C.: MicroTrack: an algorithm for concurrent projectome and microstructure estimation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 183–190. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Li, L., Rilling, J.K., Preuss, T.M., Glasser, M.F., Damen, F.W., Hu, X.: Quantitative assessment of a framework for creating anatomical brain networks via global tractography. NeuroImage 61, 1017–1030 (2012)CrossRefGoogle Scholar
  17. 17.
    Kaden, E., Knosche, T.R., Anwander, A.: Parametric spherical deconvolution: Inferring anatomical connectivity using diffusion MR imaging. NeuroImage 37, 474–488 (2007)CrossRefGoogle Scholar
  18. 18.
    Zhang, H., Hubbard, P.L., Parker, G.J.M., Alexander, D.C.: Axon diameter mapping in the presence of orientation dispersion with diffusion MRI. NeuroImage 56, 1301–1315 (2011)CrossRefGoogle Scholar
  19. 19.
    Sotiropoulos, S.N., Behrens, T.E.J., Jbabdi, S.: Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI. NeuroImage 60, 1412–1425 (2012)CrossRefGoogle Scholar
  20. 20.
    Zhang, H., Shneider, T., Wheeler-Kingshott, C., Alexander, D.C.: NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61, 1000–1016 (2012)CrossRefGoogle Scholar
  21. 21.
    Jespersen, S.N., Leigland, L.A., Cornea, A., Kroenke, C.D.: Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging. IEEE Trans. Med. Imaging 31, 16–32 (2012)CrossRefGoogle Scholar
  22. 22.
    Rowe, M., Zhang, H., Alexander, D.C.: Utilising measures of fiber dispersion in white matter tractography. In: MICCAI CDMRI Workshop (2012)Google Scholar
  23. 23.
    Savadjiev, P., Campbell, J.S.W., Descoteaux, M., Deriche, R., Pike, G.B., Siddiqi, K.: Labeling of ambiguous subvoxel fibre bundle configurations in high angular resolution diffusion MRI. NeuroImage 41, 58–68 (2008)CrossRefGoogle Scholar
  24. 24.
    Zhang, F., Hancock, E.R., Goodlett, C., Gerig, G.: White matter tractography using sequential importance sampling. In: Proc. ISMRM Annual Meeting, vol. 10 (2002)Google Scholar
  25. 25.
    Zhang, F., Hancock, E.R., Goodlett, C., Gerig, G.: Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling. Med. Image Anal. 13, 5–18 (2009)CrossRefGoogle Scholar
  26. 26.
    Pontabry, J., Rousseau, F.: Probabilistic tractography using Q-ball modeling and particle filtering. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 209–216. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  27. 27.
    Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. and Comput. 10, 197–208 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Matthew Rowe
    • 1
  • Hui Gary Zhang
    • 1
  • Neil Oxtoby
    • 1
  • Daniel C. Alexander
    • 1
  1. 1.Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonUK

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