Abstract
Computational imaging of personalized cardiac electrophysiology has attracted increasing research interest because of its clinical relevance in aiding in the diagnosis and prediction of cardiac electrical malfunctions of individual subjects. We have developed a statistical physiological-model-constrained framework that, rather than delivering a personalized cardiac electrophysiological model with customized parameters, uses simple standard electrophysiological models as constraints and produces maximum a posteriori estimation of three-dimensionally distributed transmembrane potential (TMP) dynamics inside the ventricular myocardium of individual subjects [1]. Taking part in 2010 Cardiac Electrophysiological Simulation Challenge (CESC’10), we modify this framework to use epicardial optical mapping data to estimate subject-specific TMP dynamics inside the 3D myocardium. Results of estimated dynamics are compared to the simulations by the same electrophysiological model with standard or adjusted parameters. As shown, while it is rather challenging to personalize the parameters of a cardiac electrophysiological model for the entire 3D myocardium, because of the drastically simplified model structure and limited subject’s data, the presented approach of TMP estimation is able to computationally reproduce subject-specific electrical functions inside the 3D myocardium with simple standard model as constraints.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, L., Zhang, H., Wong, K., Liu, H., Shi, P.: Physiological-model-constrained noninvasive reconstruction of volumetric myocardial transmembrane potentials. IEEE Transactions on Biomedical Engineering 5(2), 296–315 (2010)
Chinchapatnam, P., Rhode, K.S., Ginks, M., Rinaldi, C.A., Lambiase, P., Razavi, R., Arridge, S., Sermesant, M.: Model-based imaging of cardiac apparent conductivity and local conduction velocity for diagnose and planning of theropy. IEEE Transactions on Medical Imaging 27(11), 1631–1641 (2008)
Pop, M., et al.: Fusion of optical imaging and mri for the evaluation and adjustment of macroscopic models of cardiac electrophysiology: A feasibility study. Medical Image Analysis 13, 370–380 (2009)
Durrer, D., Dam, R., Freud, G., Janse, M., Meijler, F., Arzbaecher, R.: Total excitation of the isolated human heart. Computer Methods in Applied Mechanics and Engineering 41(6), 899–912 (1970)
Aliev, R.R., Panfilov, A.V.: A simple two-variable model of cardiac excitation. Chaos, Solitions & Fractals 7(3), 293–301 (1996)
Liu, G.: Meshfree Methods. CRC Press, Boca Raton (2003)
Julier, S.J., Uhlmann, J.K.: A new extension of the kalman filter to nonlinear systems. In: International Symposium on Aerospace/Defense Sensing, Simulation, and Controls, pp. 182–193 (1997)
Wang, L.: Personalized Noninvasive Imaging of Volumetric Cardiac Electrophysiology. PhD thesis, Rochester Institute of Technology (May 2009)
Rogers, J.M., McCulloch, A.D.: A collocation-galerkin finite element model of cardiac action potential propagation. IEEE Transactions on Biomedical Engineering 41(8), 743–757 (1994)
Nash, M.P., Panfilov, A.V.: Electromechanical model of excitable tissue to study reentrant cardiac arrhythmias. Progress in Biophysics and Moledular Biology 85, 501–522 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Wong, K.C.L., Zhang, H., Liu, H., Shi, P. (2010). A Statistical Physiological-Model-Constrained Framework for Computational Imaging of Subject-Specific Volumetric Cardiac Electrophysiology Using Optical Imaging and MRI Data. In: Camara, O., Pop, M., Rhode, K., Sermesant, M., Smith, N., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. STACOM 2010. Lecture Notes in Computer Science, vol 6364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15835-3_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-15835-3_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15834-6
Online ISBN: 978-3-642-15835-3
eBook Packages: Computer ScienceComputer Science (R0)