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dc.contributor.authorCotter, Colin
dc.contributor.authorVialard, François-Xavier
dc.contributor.authorRisser, Laurent
HAL ID: 17551
dc.contributor.authorRueckert, Daniel
dc.date.accessioned2011-10-05T13:46:18Z
dc.date.available2011-10-05T13:46:18Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/7124
dc.language.isoenen
dc.subjectGeodesic shootingen
dc.subjectComputational anatomyen
dc.subjectAdjoint equationsen
dc.subjectHamiltonian equationsen
dc.subjectLarge deformations via diffeomorphismsen
dc.subject.ddc519en
dc.titleDiffeomorphic 3D Image Registration via Geodesic Shooting Using an Efficient Adjoint Calculationen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenIn the context of large deformations by diffeomorphisms, we propose a new diffeomorphic registration algorithm for 3D images that performs the optimization directly on the set of geodesic flows. The key contribution of this work is to provide an accurate estimation of the so-called initial momentum, which is a scalar function encoding the optimal deformation between two images through the Hamiltonian equations of geodesics. Since the initial momentum has proven to be a key tool for statistics on shape spaces, our algorithm enables more reliable statistical comparisons for 3D images. Our proposed algorithm is a gradient descent on the initial momentum, where the gradient is calculated using standard methods from optimal control theory. To improve the numerical efficiency of the gradient computation, we have developed an integral formulation of the adjoint equations associated with the geodesic equations. We then apply it successfully to the registration of 2D phantom images and 3D cerebral images. By comparing our algorithm to the standard approach of Beg et al. (Int. J. Comput. Vis. 61:139–157, 2005), we show that it provides a more reliable estimation of the initial momentum for the optimal path. In addition to promising statistical applications, we finally discuss different perspectives opened by this work, in particular in the new field of longitudinal analysis of biomedical images.en
dc.relation.isversionofjnlnameInternational Journal of Computer Vision
dc.relation.isversionofjnlvol97
dc.relation.isversionofjnlissue2
dc.relation.isversionofjnldate2012
dc.relation.isversionofjnlpages229-241
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s11263-011-0481-8en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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