Adaptive Multi-modal Particle Filtering for Probabilistic White Matter Tractography

  • Aymeric Stamm
  • Olivier Commowick
  • Christian Barillot
  • Patrick Pérez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)


Particle filtering has recently been introduced to perform probabilistic tractography in conjunction with DTI and Q-Ball models to estimate the diffusion information. Particle filters are particularly well adapted to the tractography problem as they offer a way to approximate a probability distribution over all paths originated from a specified voxel, given the diffusion information. In practice however, they often fail at consistently capturing the multi-modality of the target distribution. For brain white matter tractography, this means that multiple fiber pathways are unlikely to be tracked over extended volumes.

We propose to remedy this issue by formulating the filtering distribution as an adaptive M-component non-parametric mixture model. Such a formulation preserves all the properties of a classical particle filter while improving multi-modality capture. We apply this multi-modal particle filter to both DTI and Q-Ball models and propose to estimate dynamically the number of modes of the filtering distribution. We show on synthetic and real data how this algorithm outperforms the previous versions proposed in the literature.


Orientation Distribution Function Local Curvature Phantom Data Proposal Density Order Markov Chain 
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 2013

Authors and Affiliations

  • Aymeric Stamm
    • 1
  • Olivier Commowick
    • 1
  • Christian Barillot
    • 1
  • Patrick Pérez
    • 2
  1. 1.VISAGES: INSERM U746 - CNRS UMR6074 - INRIAUniv. of Rennes IFrance
  2. 2.TechnicolorRennesFrance

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