A lagrangian formulation for statistical fluid registration

Brun, Caroline C., Lepore, Natasha, Pennec, Xavier, Chou, Yi-Yu, Lee, Agatha D., Barysheva, Marina, de Zubicaray, Greig I., Katie McMahon, Wright, Margaret J., Toga, Arthur and Thompson, Paul M. (2009). A lagrangian formulation for statistical fluid registration. In: Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI '09: IEEE International Symposium on Biomedical Imaging, Boston, Massachusetts, USA, (975-978). 28 June to 1 July 2009. doi:10.1109/ISBI.2009.5193217

Author Brun, Caroline C.
Lepore, Natasha
Pennec, Xavier
Chou, Yi-Yu
Lee, Agatha D.
Barysheva, Marina
de Zubicaray, Greig I.
Katie McMahon
Wright, Margaret J.
Toga, Arthur
Thompson, Paul M.
Title of paper A lagrangian formulation for statistical fluid registration
Conference name ISBI '09: IEEE International Symposium on Biomedical Imaging
Conference location Boston, Massachusetts, USA
Conference dates 28 June to 1 July 2009
Proceedings title Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro   Check publisher's open access policy
Journal name 2009 Ieee International Symposium On Biomedical Imaging: From Nano to Macro, Vols 1 and 2   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2009
Sub-type Fully published paper
DOI 10.1109/ISBI.2009.5193217
ISBN 9781424439317
ISSN 1945-7928
Start page 975
End page 978
Total pages 4
Collection year 2010
Language eng
Abstract/Summary 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 algorithm developed in [4], and used a Lagrangian framework to incorporate 0th 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 tensor-based distances to compare the power and the robustness of the algorithms.
Subjects E1
920199 Clinical Health (Organs, Diseases and Abnormal Conditions) not elsewhere classified
970111 Expanding Knowledge in the Medical and Health Sciences
080106 Image Processing
110999 Neurosciences not elsewhere classified
170205 Neurocognitive Patterns and Neural Networks
Keyword Registration
Statistical prior
Riemannian metrics
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

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Created: Thu, 08 Apr 2010, 12:52:20 EST by Sandrine Ducrot on behalf of Centre For Magnetic Resonance