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Bayesian Estimation of Probabilistic Atlas for Anatomically-Informed Functional MRI Group Analyses
Hao Xu1, Bertrand Thirion2,3, and Stéphanie Allassonnière1
1CMAP Ecole Polytechnique, Route de Saclay, 91128, Palaiseau, France
2Parietal Team, INRIA, Saclay-Île-de-France, France
3CEA, DSV, I2BM, Neurospin bât 145, 91191, Gif-Sur-Yvette, France
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: Probabilistic atlas, geometric variability, joint registration segmentation, atlas-based segmentation, multi-modal, T1 MRI and fMRI
LNCS 8151, p. 592 ff.
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© Springer-Verlag Berlin Heidelberg 2013