A lagrangian formulation for statistical fluid registration

Brun, C. C., Lepore, N., Pennec, X., Chou, Y. Y., Lee, A. D., Barysheva, M., de Zubicaray, G. I., McMahon, K. L., Wright, M. J., Toga, A. W., & Thompson, P. M. (2009) A lagrangian formulation for statistical fluid registration. In ISBI '09. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009. Proceedings, IEEE, Boston, USA, pp. 975-978.

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We defined a new statistical fluid registration method with Lagrangian mechanics. Although several authors have suggested that empirical statistics on brain variation should be incorporated into the registration problem, few algorithms have included this information and instead use regularizers that guarantee diffeomorphic mappings. Here we combine the advantages of a large-deformation fluid matching approach with empirical statistics on population variability in anatomy. We reformulated the Riemannian fluid algorithmdeveloped in [4], and used a Lagrangian framework to incorporate 0 th and 1st order statistics in the regularization process. 92 2D midline corpus callosum traces from a twin MRI database were fluidly registered using the non-statistical version of the algorithm (algorithm 0), giving initial vector fields and deformation tensors. Covariance matrices were computed for both distributions and incorporated either separately (algorithm 1 and algorithm 2) or together (algorithm 3) in the registration. We computed heritability maps and two vector and tensorbased distances to compare the power and the robustness of the algorithms.

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ID Code: 85692
Item Type: Conference Paper
Refereed: No
Keywords: Genetics, Registration, Riemannian metrics, Statistical prior
DOI: 10.1109/ISBI.2009.5193217
ISBN: 9781424439324
ISSN: 1948-7928
Divisions: Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Copyright Owner: Copyright 2009 IEEE
Deposited On: 09 Oct 2015 05:30
Last Modified: 21 Oct 2015 03:23

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