Bayesian Estimation of Probabilistic Atlas for Anatomically-Informed Functional MRI Group Analyses

Abstract : Traditional analyses of Functional Magnetic Resonance Imaging (fMRI) use little anatomical information. The registration of the images to a template is based on the individual anatomy and ignores functional information; subsequently detected activations are not confined to gray matter (GM). In this paper, we propose a statistical model to estimate a probabilistic atlas from functional and T1 MRIs that summarizes both anatomical and functional information and the geometric variability of the population. Registration and Segmentation are performed jointly along the atlas estimation and the functional activity is constrained to the GM, increasing the accuracy of the atlas.
Type de document :
Communication dans un congrès
Yoshinobu Sato and Christian Barillot and Nassir Navab. MICCAI - 16th International Conference on Medical Image Computing and Computer Assisted Intervention - 2013, Sep 2013, Nagoya, Japan. Springer, 2013
Liste complète des métadonnées

Littérature citée [14 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00853097
Contributeur : Bertrand Thirion <>
Soumis le : mercredi 21 août 2013 - 19:17:54
Dernière modification le : lundi 4 juin 2018 - 15:42:02
Document(s) archivé(s) le : vendredi 22 novembre 2013 - 04:17:15

Fichier

paper226.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00853097, version 1

Collections

Citation

Hao Xu, Bertrand Thirion, Stéphanie Allassonnière. Bayesian Estimation of Probabilistic Atlas for Anatomically-Informed Functional MRI Group Analyses. Yoshinobu Sato and Christian Barillot and Nassir Navab. MICCAI - 16th International Conference on Medical Image Computing and Computer Assisted Intervention - 2013, Sep 2013, Nagoya, Japan. Springer, 2013. 〈hal-00853097〉

Partager

Métriques

Consultations de la notice

515

Téléchargements de fichiers

240