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Joint state-parameter estimation for tumor growth model

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorCOLLIN, Annabelle
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorKRITTER, Thibaut
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorPOIGNARD, Clair
hal.structure.identifierModélisation Mathématique pour l'Oncologie [MONC]
dc.contributor.authorSAUT, Olivier
dc.date.accessioned2024-04-04T02:49:18Z
dc.date.available2024-04-04T02:49:18Z
dc.date.issued2021-03-23
dc.identifier.issn0036-1399
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191811
dc.description.abstractEnWe present a shape-oriented data assimilation strategy suitable for front-tracking tumor growth problems. A general hyperbolic/elliptic tumor growth model is presented as well as the available observations corresponding to the location of the tumor front over time extracted from medical imaging as MRI or CT scans. We provide sufficient conditions allowing to design a state observer by proving the convergence of the observer model to the target solution, for exact parameters. In particular, the similarity measure chosen to compare observations and simulation of tumor contour is presented. A specific joint state-parameter correction with a Luenberger observer correcting the state and a reduced-order Kalman filter correcting the parameters is introduced and studied. We then illustrate and assess our proposed observer method with synthetic problems. Our numerical trials show that state estimation is very effective with the proposed Luenberger observer, but specific strategies are needed to accurately perform parameter estimation in a clinical context. We then propose strategies to deal with the fact that data is very sparse in time and that the initial distribution of the proliferation rate is unknown. The results on synthetic data are very promising and work is ongoing to apply our strategy on clinical cases.
dc.language.isoen
dc.publisherSociety for Industrial and Applied Mathematics
dc.subject.enTumor growth
dc.subject.enData assimilation
dc.subject.enImage processing
dc.subject.enFront propagation
dc.title.enJoint state-parameter estimation for tumor growth model
dc.typeArticle de revue
dc.identifier.doi10.1137/20M131775X
dc.subject.halMathématiques [math]/Optimisation et contrôle [math.OC]
dc.subject.halMathématiques [math]/Equations aux dérivées partielles [math.AP]
dc.subject.halMathématiques [math]/Analyse numérique [math.NA]
bordeaux.journalSIAM Journal on Applied Mathematics
bordeaux.volume81
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue2
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-02960283
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02960283v1
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