Adaptive Multi-modal Particle Filtering for Probabilistic White Matter Tractography

Aymeric Stamm 1, * Olivier Commowick 1 Christian Barillot 1 Patrick Pérez 2
* Auteur correspondant
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
2 Technicolor [Cesson Sévigné]
Abstract : 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.
Type de document :
Communication dans un congrès
Information Processing in Medical Imaging, Jul 2013, Monterey, United States. 7917, pp.594-606, 2013, LNCS. 〈10.1007/978-3-642-38868-2_50〉
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Aymeric Stamm, Olivier Commowick, Christian Barillot, Patrick Pérez. Adaptive Multi-modal Particle Filtering for Probabilistic White Matter Tractography. Information Processing in Medical Imaging, Jul 2013, Monterey, United States. 7917, pp.594-606, 2013, LNCS. 〈10.1007/978-3-642-38868-2_50〉. 〈inserm-00849053〉

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