Abstract
We consider a model-based estimation procedure – namely a data assimilation algorithm – of the atrial depolarization state of a subject using data corresponding to electro-anatomical maps. Our objective is to evaluate the sensitivity of such a model-based reconstruction with respect to model choices. The followed data assimilation approach is capable of using electrical activation times to adapt a monodomain model simulation, thanks to an ingenious model-data fitting term inspired from image processing. The resulting simulation smoothes and completes the activation maps when they are spatially incomplete. Moreover, conductivity parameters can also be inferred. The model sensitivity assessment is performed based on synthetic data generated with a validated realistic atria model and then inverted using simpler modeling ingredients. In particular, the impact of the muscle fibers definition and corresponding anisotropic conductivity parameters is studied. Finally, an application of the method to real data is presented, showing promising results.
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Chapelle, D., Fragu, M., Mallet, V., Moireau, P.: Fundamental principles of data assimilation underlying the Verdandi library: applications to biophysical model personalization within euHeart. Med. Biol. Eng. Comput. 51, 1–13 (2012)
Collin, A., Chapelle, D., Moireau, P.: A Luenberger observer for reaction-diffusion models with front position data. J. Comput. Phys. 300, 288–307 (2015)
Corrado, C., et al.: Personalized models of human atrial electrophysiology derived from endocardial electrograms. IEEE Trans. Biomed. Eng. 64(4), 735–742 (2017)
Courtemanche, M., Ramirez, R., Nattel, S.: Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. Am. J. Physiol. 275, H301–H321 (1998)
Jazwinski, A.H.: Stochastic Processes and Filtering Theory. Academic Press, Cambridge (1970)
Konukoglu, E., et al.: Efficient probabilistic model personalization integrating uncertainty on data and parameters: application to Eikonal-Diffusion models in cardiac electrophysiology. Prog. Biophys. Mol. Bio. 107(1), 134–146 (2011)
Labarthe, S., et al.: A bilayer model of human atria: mathematical background, construction, and assessment. Europace 16(Suppl. 4), iv21–iv29 (2014)
Mitchell, C., Schaeffer, D.: A two-current model for the dynamics of cardiac membrane. Bull. Math. Bio. 65, 767–793 (2003)
Moireau, P., Chapelle, D., Le Tallec, P.: Joint state and parameter estimation for distributed mechanical systems. Comput. Methods Appl. Mech. Eng. 197, 659–677 (2008)
Moireau, P., Chapelle, D.: Reduced-order unscented kalman filtering with application to parameter identification in large-dimensional systems. ESAIM Control Optimisation Calc. Var. 17(2), 380–405 (2011)
Moreau-Villeger, V., Delingette, H., Sermesant, M., Ashikaga, H., McVeigh, E., Ayache, N.: Building maps of local apparent conductivity of the epicardium with a 2-D electrophysiological model of the heart. IEEE Trans. Biomed. Eng. 53(8), 1457–1466 (2006)
Prabhu, S., et al.: Biatrial electrical and structural atrial changes in heart failure: electroanatomic mapping in persistent atrial fibrillation in humans. JACC Clin. Electrophysiol. 4(1), 87–96 (2018)
Talbot, H., Cotin, S., Razavi, R., Rinaldi, C., Delingette, H.: Personalization of cardiac electrophysiology model using the unscented kalman filtering. In: Computer Assisted Radiology and Surgery (CARS 2015) (2015)
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Gérard, A., Collin, A., Bureau, G., Moireau, P., Coudière, Y. (2019). Model Assessment Through Data Assimilation of Realistic Data in Cardiac Electrophysiology. In: Coudière, Y., Ozenne, V., Vigmond, E., Zemzemi, N. (eds) Functional Imaging and Modeling of the Heart. FIMH 2019. Lecture Notes in Computer Science(), vol 11504. Springer, Cham. https://doi.org/10.1007/978-3-030-21949-9_14
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DOI: https://doi.org/10.1007/978-3-030-21949-9_14
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