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Efficient estimation of cardiac conductivities: A proper generalized decomposition approach

hal.structure.identifierDepartment of Mathematics and Computer Science [Emory University]
hal.structure.identifierUniversità Campus Bio-Medico di Roma / University Campus Bio-Medico of Rome [ UCBM]
dc.contributor.authorBARONE, Alessandro
hal.structure.identifierModeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorCARLINO, Michele Giuliano
hal.structure.identifierUniversità Campus Bio-Medico di Roma / University Campus Bio-Medico of Rome [ UCBM]
dc.contributor.authorGIZZI, Alessio
hal.structure.identifierModeling and Scientific Computing [Milano] [MOX]
dc.contributor.authorPEROTTO, Simona
hal.structure.identifierDepartment of Mathematics and Computer Science [Emory University]
dc.contributor.authorVENEZIANI, Alessandro
dc.date.issued2020-09
dc.identifier.issn0021-9991
dc.description.abstractEnWhile the potential groundbreaking role of mathematical modeling in electrophysiology has been demon-strated for therapies like cardiac resynchronization or catheter ablation, its extensive use in clinics is pre-vented by the need of an accurate customized conductivity identification. Data assimilation techniques are,in general, used to identify parameters that cannot be measured directly, especially in patient-specific set-tings. Yet, they may be computationally demanding. This conflicts with the clinical timelines and volumesof patients to analyze. In this paper, we adopt a model reduction technique, developed by F. Chinesta andhis collaborators in the last 15 years, called Proper Generalized Decomposition (PGD), to accelerate the esti-mation of the cardiac conductivities required in the modeling of the cardiac electrical dynamics. Specifically,we resort to the Monodomain Inverse Conductivity Problem (MICP) deeply investigated in the literaturein the last five years. We provide a significant proof of concept that PGD is a breakthrough in solvingthe MICP within reasonable timelines. As PGD relies on the offline/online paradigm and does not needany preliminary knowledge of the high-fidelity solution, we show that the PGD online phase estimates theconductivities in real-time for both two-dimensional and three-dimensional cases, including a patient-specificventricle.
dc.language.isoen
dc.publisherElsevier
dc.subject.enComputational Electrophysiology
dc.subject.enModel Order Reduction
dc.subject.enData Assimilation
dc.subject.enProper Generalized Decomposition
dc.subject.enParameter Identification
dc.title.enEfficient estimation of cardiac conductivities: A proper generalized decomposition approach
dc.typeArticle de revue
dc.identifier.doi10.1016/j.jcp.2020.109810
dc.subject.halMathématiques [math]
dc.subject.halScience non linéaire [physics]
dc.description.sponsorshipEuropeAccurate Roms for Industrial Applications
bordeaux.journalJournal of Computational Physics
bordeaux.page109810
bordeaux.peerReviewedoui
hal.identifierhal-02938735
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02938735v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Computational%20Physics&rft.date=2020-09&rft.spage=109810&rft.epage=109810&rft.eissn=0021-9991&rft.issn=0021-9991&rft.au=BARONE,%20Alessandro&CARLINO,%20Michele%20Giuliano&GIZZI,%20Alessio&PEROTTO,%20Simona&VENEZIANI,%20Alessandro&rft.genre=article


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