Abstract
Computer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However the number of simulation parameters in these models can be high and expert knowledge is required to properly design studies involving these models, and analyse the results. In particular it is important to know how the parameters vary in various clinical or physiological settings. In this paper we build a data-driven model of cardiovascular parameter evolution during digestion, from a clinical study involving more than 80 patients. We first present a method for longitudinal parameter estimation in 3D cardiac models, which we apply to 21 patient-specific hearts geometries at two instants of the study, for 6 parameters (two fixed and four time-varying parameters). From these personalised hearts, we then extract and validate a law which links the changes of cardiac output and heart rate under constant arterial pressure to the evolution of these parameters, thus enabling the fast simulation of hearts during digestion for future patients.
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Ackowledgements
This work has been partially funded by the EU FP7-funded project MD-Paedigree (Grant Agreement 600932) and contributes to the objectives of the ERC advanced grant MedYMA (2011-291080).
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Mollero, R. et al. (2017). Longitudinal Parameter Estimation in 3D Electromechanical Models: Application to Cardiovascular Changes in Digestion. In: Pop, M., Wright, G. (eds) Functional Imaging and Modelling of the Heart. FIMH 2017. Lecture Notes in Computer Science(), vol 10263. Springer, Cham. https://doi.org/10.1007/978-3-319-59448-4_41
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DOI: https://doi.org/10.1007/978-3-319-59448-4_41
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