A Hierarchical Bayesian Model for Multi-Site Diffeomorphic Image Atlases

Image templates, or atlases, play a critical role in imaging studies by providing a common anatomical coordinate system for analysis of shape and function. It is now common to estimate an atlas as a deformable average of the very images being studied, in order to provide a representative example of the particular population, imaging hardware, protocol, etc. However, when imaging data is aggregated across multiple sites, estimating an atlas from the pooled data fails to account for the variability of these factors across sites. In this paper, we present a hierarchical Bayesian model for diffeomorphic atlas construction of multi-site imaging data that explicitly accounts for the inter-site variability, while providing a global atlas as a common coordinate system for images across all sites. Our probabilistic model has two layers: the first consists of the average diffeomorphic transformations from the global atlas to each site, and the second consists of the diffeomorphic transformations from the site level to the individual input images. Our results on multi-site datasets, both synthetic and real brain MRI, demonstrate the capability of our model to capture inter-site geometric variability and give more reliable alignment of images across sites.