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<dc:title xml:lang="fr">Identification et caractérisation des conditions aux limites pour des simulations biomécaniques patient-spécifiques</dc:title>
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<dc:subject xml:lang="fr">Modélisation spécifique au patient</dc:subject>
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<dc:subject xml:lang="fr">Réalité augmentée</dc:subject>
<dc:subject xml:lang="fr">Biomécanique</dc:subject>
<dc:subject xml:lang="fr">Chirurgie assistée par ordinateur</dc:subject>
<dc:subject xml:lang="fr">Atlas statistique</dc:subject>
<dc:subject xml:lang="en">Patient-specific modeling</dc:subject>
<dc:subject xml:lang="en">Numerical simulation</dc:subject>
<dc:subject xml:lang="en">Data assimilation</dc:subject>
<dc:subject xml:lang="en">Augmented reality,</dc:subject>
<dc:subject xml:lang="en">Biomechanics</dc:subject>
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<dcterms:abstract xml:lang="fr">L'objectif de ce travail est trouvé un moyen d'estimer les conditions aux limites du foie. Elles jouent un rôle essentiel dans la capacité de prédiction du modèle biomécanique, mais sont principalement présentées par les ligaments, les vaisseaux et les organes environnants, dont les propriétés sont "spécifiques au patient" et ne peuvent être mesurées fidèlement. Nous proposons de présenter ces conditions comme des ressorts non linéaires et d'estimer ses paramètres. D’abord, nous créons une approximation initiale en utilisant la loi constitutive disponible dans la littérature et un atlas statistique obtenu à partir des modèles avec des ligaments segmentés. Après, nous la corrigeons basée sur le filtrage de Kalman non linéaire, qui assimile les données acquises d'une modalité pendant la chirurgie. Pour évaluation, nous avons réalisé des expériences avec des données synthétiques et réelles. Les résultats montrent une amélioration de la précision pour les cas avec des limites estimées.</dcterms:abstract>
<dcterms:abstract xml:lang="en">The purpose of the work is to find a way to estimate the boundary conditions of the liver. They play an essential role in forming the predictive capacity of the biomechanical model, but are presented mainly by ligaments, vessels, and surrounding organs, the properties of which are "patient specific" and cannot be measured reliably. We propose to present the boundary conditions as nonlinear springs and estimate their parameters. Firstly, we create a generalized initial approximation using the constitutive law available in the literature and a statistical atlas, obtained from a set of models with segmented ligaments. Then, we correct the approximation based on the nonlinear Kalman filtering approach, which assimilates data obtained from a modality during surgical intervention. To assess the approach, we performed experiments for both synthetic and real data. The results show a certain improvement in simulation accuracy for the cases with estimated boundaries.</dcterms:abstract>
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