Modeling White-matter Fiber-orientation Uncertainty for Improved Probabilistic Tractography

Adelino R. Ferreira da Silva

Abstract

Tractography uses fiber-orientation estimates to trace the likely paths of white-matter tracts through the brain, in order to map brain connectivity non-invasively. In this paper, we propose a novel probabilistic framework for modeling fiber-orientation uncertainty and improve probabilistic tractography. The main innovation in the present formulation consists in coupling a particle filtering process with a clustered-mixture model approach to model directional data. Mixtures of von Mises-Fisher (vMF) distributions are used to support the probabilistic estimation of intravoxel fiber directions. The fitted parameters of the clustered vMF mixture at each voxel are then used to estimate white-matter pathways using particle filtering techniques. The technique is validated on simulated as well as on real human brain data experiments.

References

  1. Alexander, A. L., Hasan, K. M., Lazar, M., Tsuruda, J. S., and Parker, D. L. (2001). Analysis of Partial Volume Effects in Diffusion-Tensor MRI. Magnetic Resonance in Medicine, 45:770-780.
  2. Assaf, Y. and Basser, P. J. (2005). Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. NeuroImage, 27(1):48-58.
  3. Banerjee, A., Dhillon, I. S., Ghosh, J., and Sra, S. (2005). Clustering on the Unit Hypersphere using von MisesFisher Distributions. Journal of Machine Learning Research, 6:1345-1382.
  4. Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., and Woolrich, M. W. (2007). Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage, 34(1):144-155.
  5. Doucet, A., Godsill, S., and Andrieu, C. (2000). On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing, 10(3):197-208.
  6. Doucet, A. and Johansen, A. M. (2011). A Tutorial on Particle Filtering and Smoothing: Fifteen years later. In Crisan, D. and Rozovsky, B., editors, The Oxford Handbook of Nonlinear Filtering. Oxford University Press.
  7. Ferreira da Silva, A. (2012). Facing the Challenge of Estimating Human Brain White Matter Pathways. In Madani, K., Kacprzyk, J., and Filipe, J., editors, Proc. of the 4th International Joint Conference on Computational Intelligence, pages 709-714. SciTePress.
  8. Ferreira da Silva, A. (2013). Computational Representation of White Matter Fiber Orientations. International Journal of Biomedical Imaging, 2013. Article ID 232143.
  9. Hornik, K. and GrĂ¼n, B. (2012). Mixtures of von Mises Fisher Distributions. R package version 0.1-0.
  10. Liu, J. S. (2001). Monte Carlo Strategies in Scientific Computing. Springer Series in Statistics. Springer.
  11. Mardia, K. V. and Jupp, P. (2000). Directional Statistics. John Wiley and Sons Ltd., 2nd edition.
  12. Mori, S. and van Zijl, P. C. M. (2002). Fiber tracking: principles and strategies - a technical review. NMR in Biomedicine, 15:468-480.
  13. Parker, G. and Alexander, D. (2003). Probabilistic Monte Carlo based mapping of cerebral connections utilising whole-brain crossing fiber information. In Proc. IPMI, pages 684-695.
  14. Pontabry, J., Rousseau, F., Oubel, E., Studholme, C., Koob, M., and Dietemann, J.-L. (2013). Probabilistic tractography using Q-ball imaging and particle filtering: Application to adult and in-utero fetal brain studies. Medical Image Analysis, 17(3):297-310.
  15. Rathi, Y., Michailovich, O., Shenton, M. E., and Bouix, S. (2009). Directional Functions for Orientation Distribution Estimation. Medical Image Analysis, 13(3):433-444.
  16. Rowe, M. C., Zhang, H. G., Oxtoby, N., and Alexander, D. C. (2013). Beyond crossing fibers: Tractography exploiting sub-voxel fibre dispersion and neighbourhood structure. In IPMI, pages 402-413.
  17. Schwartz, G. (1979). Estimating the dimension of a model. Annals of Statistics, 6:461-464.
  18. Seunarine, K. K. and Alexander, D. C. (2009). Multiple fibres: beyond the diffusion tensor. In Johansen-Berg, H. and Behrens, T. E. J., editors, Diffusion MRI: from quantitative measurement to in vivo neuroanatomy, pages 56-74. Academic Press.
  19. Seunarine, K. K., Cook, P. A., Hall, M. G., Embleton, K. V., Parker, G. J. M., and Alexander, D. C. (2007). Exploiting peak anisotropy for tracking through complex structures. In Proc. 11th IEEE International Conference on Computer Vision Workshop on MMBIA, Rio de Janeiro.
  20. Tuch, D. S. (2004). Q-Ball Imaging. Magnetic Resonance in Medicine, 52:1358-1372.
  21. Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., and Wedeen, V. J. (2002). High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magnetic Resonance in Medicine, 48:577-582.
  22. Wedeen, V. J., Hagmann, P., Tseng, W.-Y. I., Reese, T. G., and Weisskoff, R. M. (2005). Mapping Complex Tissue Architecture With Diffusion Spectrum Magnetic Resonance Imaging. Magnetic Resonance in Medicine, 54:1377-1386.
  23. Yeh, F.-C., Wedeen, V. J., and Tseng, W.-Y. I. (2010). Generalized q-Sampling Imaging. IEEE Transactions on Medical Imaging, 29(9):1626-1635.
  24. Zhang, F., Hancock, E. R., Goodlett, C., and Gerig, G. (2009). Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling. Medical Image Analysis, 13(1):5-18.
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Paper Citation


in Harvard Style

R. Ferreira da Silva A. (2014). Modeling White-matter Fiber-orientation Uncertainty for Improved Probabilistic Tractography . In Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX, ISBN 978-989-758-056-7, pages 71-78. DOI: 10.5220/0005069300710078


in Bibtex Style

@conference{neurotechnix14,
author={Adelino R. Ferreira da Silva},
title={Modeling White-matter Fiber-orientation Uncertainty for Improved Probabilistic Tractography},
booktitle={Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,},
year={2014},
pages={71-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005069300710078},
isbn={978-989-758-056-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Congress on Neurotechnology, Electronics and Informatics - Volume 1: NEUROTECHNIX,
TI - Modeling White-matter Fiber-orientation Uncertainty for Improved Probabilistic Tractography
SN - 978-989-758-056-7
AU - R. Ferreira da Silva A.
PY - 2014
SP - 71
EP - 78
DO - 10.5220/0005069300710078