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Simultaneous Fine and Coarse Diffeomorphic Registration: Application to Atrophy Measurement in Alzheimer’s Disease

dc.contributor.authorRueckert, Daniel
dc.contributor.authorHolm, Darryl
dc.contributor.authorWolz, Robin
dc.contributor.authorVialard, François-Xavier
dc.contributor.authorRisser, Laurent
HAL ID: 17551
dc.date.accessioned2011-10-04T16:57:23Z
dc.date.available2011-10-04T16:57:23Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/7116
dc.language.isoenen
dc.subjectLarge Deformation Diffeomorphic Metric Mapping (LDDMM)en
dc.subjectmultiscale registration of 3D medical imagesen
dc.subject.ddc519en
dc.titleSimultaneous Fine and Coarse Diffeomorphic Registration: Application to Atrophy Measurement in Alzheimer’s Diseaseen
dc.typeCommunication / Conférence
dc.description.abstractenIn this paper, we present a fine and coarse approach for the multiscale registration of 3D medical images using Large Deformation Diffeomorphic Metric Mapping (LDDMM). This approach has particularly interesting properties since it estimates large, smooth and invertible optimal deformations having a rich descriptive power for the quantification of temporal changes in the images. First, we show the importance of the smoothing kernel and its influence on the final solution. We then propose a new strategy for the spatial regularization of the deformations, which uses simultaneously fine and coarse smoothing kernels. We have evaluated the approach on both 2D synthetic images as well as on 3D MR longitudinal images out of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Results highlight the regularizing properties of our approach for the registration of complex shapes. More importantly, the results also demonstrate its ability to measure shape variations at several scales simultaneously while keeping the desirable properties of LDDMM. This opens new perspectives for clinical applications.en
dc.identifier.citationpages610-617en
dc.relation.ispartofseriestitleLecture Notes in Computer Science
dc.relation.ispartofseriesnumber6362
dc.relation.ispartoftitleMedical Image Computing and Computer-Assisted Intervention – MICCAI 2010 13th International Conference, Beijing, China, September 20-24, 2010, Proceedings, Part IIen
dc.relation.ispartofeditorViergever, Max
dc.relation.ispartofeditorPluim, Josien
dc.relation.ispartofeditorNavab, Nassir
dc.relation.ispartofeditorJiang, Tianzi
dc.relation.ispartofpublnameSpringeren
dc.relation.ispartofpublcityBerlinen
dc.relation.ispartofdate2010
dc.relation.ispartofpages704en
dc.relation.ispartofurlhttp://dx.doi.org/10.1007/978-3-642-15745-5en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.ispartofisbn978-3-642-15744-8en
dc.relation.conftitleMedical Image Computing and Computer-Assisted Intervention (MICCAI 2010)en
dc.relation.confdate2010-09
dc.relation.confcityBeijingen
dc.relation.confcountryChineen
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-642-15745-5_75


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