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Bayesian estimation of probabilistic atlas for anatomically-informed functional MRI group analyses

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Résumé

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.

langue originaleAnglais
titreMedical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages592-599
Nombre de pages8
EditionPART 3
Les DOIs
étatPublié - 24 oct. 2013
Evénement16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japon
Durée: 22 sept. 201326 sept. 2013

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
nombrePART 3
Volume8151 LNCS
ISSN (imprimé)0302-9743
ISSN (Electronique)1611-3349

Une conférence

Une conférence16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Pays/TerritoireJapon
La villeNagoya
période22/09/1326/09/13

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