Probabilistic Tractography Using Q-Ball Modeling and Particle Filtering

  • Julien Pontabry
  • François Rousseau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)

Abstract

By assuming that orientation information of brain white matter fibers can be inferred from Diffusion Weighted Magnetic Resonance Imaging (DWMRI) measurements, tractography algorithms provide an estimation of the brain connectivity in-vivo. The two key ingredients of tractography are the diffusion model (tensor, high-order tensor, Q-ball, etc.) and the way to deal with uncertainty during the tracking process (deterministic vs probabilistic). In this paper, we investigate the use of an analytical Q-ball model for the diffusion data within a well-formalized particle filtering framework. The proposed method is validated and compared to other tracking algorithms on the MICCAI’09 contest Fiber Cup phantom and on in-vivo brain DWMRI data.

Keywords

Fractional Anisotropy Diffusion Tensor Imaging Posterior Density Orientation Distribution Function Effective Sample Size 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Julien Pontabry
    • 1
  • François Rousseau
    • 1
  1. 1.LSIIT, UMR 7005 CNRS-Université de StrasbourgFrance

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