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

Hao Xu 1 Bertrand Thirion 2, 3 Stéphanie Allassonnière 4
1 CMAP
CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
3 NEUROSPIN - Service NEUROSPIN
4 CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique
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.
Keywords : joint registration segmentation Probabilistic atlas geometric variability atlas-based segmentation multi-modal T1 MRI and fMRI
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
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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〉

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