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hal.structure.identifierUniversity Medical Center [Utrecht]
dc.contributor.authorBOSMA, Lando
hal.structure.identifierUniversity Medical Center [Utrecht]
dc.contributor.authorZACHIU, Cornel
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorDENIS DE SENNEVILLE, Baudouin
hal.structure.identifierUniversity Medical Center [Utrecht]
dc.contributor.authorRAAYMAKERS, Bas
hal.structure.identifierUniversity Medical Center [Utrecht]
dc.contributor.authorRIES, Mario
dc.date.accessioned2024-04-04T02:32:50Z
dc.date.available2024-04-04T02:32:50Z
dc.date.issued2023-03
dc.identifier.issn0094-2405
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190419
dc.description.abstractEnBackground: Deformable image registration is increasingly used in radiotherapy to adapt the treatment plan and accumulate the delivered dose. Consequently, clinical workflows using deformable image registration require quick and reliable quality assurance to accept registrations. Additionally, for online adaptive radiotherapy, quality assurance without the need for an operator to delineate contours while the patient is on the treatment table is needed. Established quality assurance criteria such as the Dice similarity coefficient or Hausdorff distance lack these qualities and also display a limited sensitivity to registration errors beyond soft tissue boundaries.Purpose: The purpose of this study is to investigate the existing intensity-based quality assurance criteria structural similarity and normalized mutual information for their ability to quickly and reliably identify registration errors for (online) adaptive radiotherapy and compare them to contour-based quality assurance criteria.Methods: All criteria were tested using synthetic and simulated biomechanical deformations of 3D MR images as well as manually annotated 4D CT data. The quality assurance criteria were scored for classification performance, for their ability to predict the registration error, and for their spatial correlation with the registration error.Results: We found that besides being fast and operator-independent, the intensitybased criteria have the highest area under the receiver operating characteristic curve and provide the best input for models to predict the registration error on all data sets. Structural similarity furthermore provides spatial information with a higher spatial correlation to the benchmark than the inverse consistency error, Jacobian determinant, and curl magnitude.Conclusions: Intensity-based quality assurance criteria can provide the required confidence in decisions about using mono-modal registrations in clinical workflows. They thereby enable automated quality assurance for deformable image registration in adaptive radiotherapy treatments.
dc.language.isoen
dc.publisherAmerican Association of Physicists in Medicine
dc.title.enTechnical Note: Intensity-based quality assurance criteria for deformable image registration in image-guided radiotherapy
dc.typeArticle de revue
dc.identifier.doi10.1002/mp.16367
dc.subject.halSciences du Vivant [q-bio]/Ingénierie biomédicale/Imagerie
bordeaux.journalMedical Physics
bordeaux.page5715-5722
bordeaux.volume50
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue9
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-04268726
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04268726v1
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