Abstract
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.
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Keywords
- Autism Spectrum Disorder
- Autism Spectrum Disorder
- Partial Little Square
- Typically Develop
- Hierarchical Bayesian Model
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References
Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International Journal of Computer Vision 61(2), 139–157 (2005)
Bullo, F.: Invariant affine connections and controllability on lie groups. Tech. rep., Geometric Mechanics, California Institute of Technology (1995)
Di Martino, A., Yan, C.G., Li, Q., Denio, E., Castellanos, F.X., Alaerts, K., Anderson, J.S., Assaf, M., Bookheimer, S.Y., Dapretto, M., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry (2013)
Jahanshad, N., Kochunov, P.V., Sprooten, E., Mandl, R.C., Nichols, T.E., et al.: Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: a pilot project of the enigma–dti working group. Neuroimage 81, 455–469 (2013)
Leporé, N., Brun, C., Chou, Y.Y., Lee, A., Barysheva, M., De Zubicaray, G.I., et al.: Multi-atlas tensor-based morphometry and its application to a genetic study of 92 twins. In: 2nd MICCAI Workshop on Mathematical Foundations of Computational Anatomy, pp. 48–55 (2008)
Miller, M.I., Trouvé, A., Younes, L.: Geodesic shooting for computational anatomy. Journal of Mathematical Imaging and Vision 24(2), 209–228 (2006)
Mueller, S.G., Weiner, M.W., Thal, L.J., Petersen, R.C., Jack, C.R., Jagust, W., Trojanowski, J.Q., Toga, A.W., Beckett, L.: Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s & Dementia 1(1), 55–66 (2005)
Qiu, A., Miller, M.I.: Multi-structure network shape analysis via normal surface momentum maps. NeuroImage 42(4), 1430–1438 (2008)
Styner, M.A., Charles, H.C., Park, J., Gerig, G.: Multisite validation of image analysis methods: assessing intra-and intersite variability. In: Medical Imaging 2002, pp. 278–286. International Society for Optics and Photonics (2002)
Thompson, P.M., Stein, J.L., Medland, S.E., Hibar, D.P., Vasquez, A.A., Renteria, M.E., Toro, R., Jahanshad, N., Schumann, G., Franke, B., et al.: The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging and Behavior 8(2), 153–182 (2014)
Wold, H.: Partial least squares. Encyclopedia of Statistical Sciences (1985)
Zhang, M., Singh, N., Fletcher, P.T.: Bayesian estimation of regularization and atlas building in diffeomorphic image registration. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 37–48. Springer, Heidelberg (2013)
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Hromatka, M. et al. (2015). A Hierarchical Bayesian Model for Multi-Site Diffeomorphic Image Atlases. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9350. Springer, Cham. https://doi.org/10.1007/978-3-319-24571-3_45
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DOI: https://doi.org/10.1007/978-3-319-24571-3_45
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