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

Hao Xu (1) , Bertrand Thirion (2, 3) , Stéphanie Allassonnière (1)

1 CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
3 NEUROSPIN - Service NEUROSPIN

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.

Mots clés: Probabilistic atlas; geometric variability; atlas-based segmentation; multi-modal; T1 MRI and fMRI; joint registration; segmentation

Domaines: Méthodologie [stat.ME]

Hao Xu, Bertrand Thirion, Stéphanie Allassonnière. Bayesian Estimation of Probabilistic Atlas for Anatomically-Informed Functional MRI Group Analyses. MICCAI - 16th International Conference on Medical Image Computing and Computer Assisted Intervention - 2013, Sep 2013, Nagoya, Japan. ⟨hal-00853097⟩