Adaptive Multi-modal Particle Filtering for Probabilistic White Matter Tractography

Aymeric Stamm; Olivier Commowick; Christian Barillot; Patrick Pérez

Adaptive Multi-modal Particle Filtering for Probabilistic White Matter Tractography

Aymeric Stamm ^{1, *} Olivier Commowick ¹ Christian Barillot ¹ Patrick Pérez ²

* Corresponding author

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.

Document type :

Conference papers

Domain :

Life Sciences [q-bio] / Bioengineering

Computer Science [cs] / Medical Imaging

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https://www.hal.inserm.fr/inserm-00849053
Contributor : Olivier Commowick <>
Submitted on : Tuesday, July 30, 2013 - 9:09:54 PM
Last modification on : Monday, March 4, 2019 - 2:07:47 PM
Long-term archiving on : Thursday, October 31, 2013 - 4:14:27 AM

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