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Data-driven reduced modeling of pleural pressure
1 : Laboratoire de mécanique des solides, CNRS, École polytechnique, Institut Polytechnique de Paris
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Site web
Ecole Polytechnique, Centre National de la Recherche Scientifique
Palaiseau -
France
2 : LaBoratoire d'Imagerie biOmédicale MultimodAle Paris-Saclay
Service Hospitalier Frédéric Joliot, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, CEA, CNRS, Université Paris-Saclay
4 : LaBoratoire d'Imagerie biOmédicale MultimodAle Paris-Saclay
(BIOMAPS)
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Site web
CEA-CNRS-Université Paris Saclay, Institut National de la Santé et de la Recherche Médicale - INSERM
CEA - Service Hospitalier Frédéric Joliot . 4, place du Général Leclerc 91401 Orsay Cedex -
France
5 : LaBoratoire d'Imagerie biOmédicale MultimodAle Paris-Saclay
* : Auteur correspondant
Service Hospitalier Frédéric Joliot, Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, CEA, CNRS, Université Paris-Saclay
Pulmonary digital twins could enhance clinical diagnosis and treatment, but still rely on simplified boundary conditions for mechanical simulations. To address this, we build a pleural pressure model based on dynamic MRI using a poromechanical approach. Higher-order SVD is applied to pressure estimates from 10 volunteers to extract distinct spatial and temporal modes and quantify their separability. This reduced-order model identifies common patterns across subjects, potentially related to physiological variables, supporting the development of more realistic boundary conditions.
