MR Diffusion-Based Inference of a Fiber Bundle Model from a Population of Subjects

This paper proposes a method to infer a high level model of the white matter organization from a population of subjects using MR diffusion imaging. This method takes as input for each subject a set of trajectories stemming from any tracking algorithm. Then the inference results from two nested clustering stages. The first clustering converts each individual set of trajectories into a set of bundles supposed to represent large white matter pathways. The second clustering matches these bundles across subjects in order to provide a list of candidates for the bundle model. The method is applied on a population of eleven subjects and leads to the inference of 17 such candidates.

• Type: Conference
• Year: 2005
• Where: MICCAI
• Authors: Vincent El Kouby, Yann Cointepas, Cyril Poupon, Denis Rivière, Narly Golestani, Jean-Baptiste Poline, Denis Le Bihan, Jean-Francois Mangin
• Keywords: Clustering Converts, Medical Imaging, MICCAI 2005, Nested Clustering Stages, White Matter Pathways