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CardioModel – New Software for Cardiac Electrophysiology Simulation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 965))

Abstract

The rise of supercomputing technologies during the last decade has enabled significant progress towards the invention of a personal biologically relevant computer model of a human heart. In this paper we present a new code for numerical simulation of cardiac electrophysiology on supercomputers. Having constructed a personal segmented tetrahedral grid of the human heart based on a tomogram, we solve the bidomain equations of cardiac electrophysiology using the finite element method thus achieving the goal of modeling of the action potential propagation in heart. Flexible object-oriented architecture of the software allows us to expand its capabilities by using relevant cell models, preconditioners and numerical methods for solving SLAEs. The results of numerical modeling of heart under normal conditions as well as a number of simulated pathologies are in a good agreement with theoretical expectations. The software achieves at least 75% scaling efficiency on the 120 ranks on the Lobachevsky supercomputer.

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References

  1. Crampin, E.J., et al.: Computational physiology and the physiome project. Exp. Physiol. 89(1), 1–26 (2004)

    Article  Google Scholar 

  2. Lloyd, C.M., et al.: The CellML model repository. Bioinformatics 24(18), 2122–2123 (2008)

    Article  Google Scholar 

  3. Vázquez, M., et al.: Alya red CCM: HPC-based cardiac computational modelling. In: Klapp, J., Ruíz Chavarría, G., Medina Ovando, A., López Villa, A., Sigalotti, L. (eds.) Selected Topics of Computational and Experimental Fluid Mechanics. ESE, pp. 189–207. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11487-3_11

    Chapter  Google Scholar 

  4. Bishop, M.J., et al.: Development of an anatomically detailed MRI-derived rabbit ventricular model and assessment of its impact on simulations of electrophysiological function. Am. J. Physiol. Heart Circ. Physiol. 298(2), H699–H718 (2010)

    Article  Google Scholar 

  5. Vázquez, M., et al.: Alya: multiphysics engineering simulation toward exascale. J. Comput. Sci. 14, 15–27 (2016)

    Article  MathSciNet  Google Scholar 

  6. Trayanova, N.A.: Whole-heart modeling. Circ. Res. 108(1), 113–128 (2011)

    Article  Google Scholar 

  7. Arevalo, H.J., et al.: Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nat. Commun. 7, 11437 (2016)

    Article  Google Scholar 

  8. Richards, D.F., et al.: Towards real-time simulation of cardiac electrophysiology in a human heart at high resolution. Comput. Methods Biomech. Biomed. Eng. 16(7), 802–805 (2013)

    Article  MathSciNet  Google Scholar 

  9. Chapelle, D., Collin, A., Gerbeau, J.-F.: A surface-based electrophysiology model relying on asymptotic analysis and motivated by cardiac atria modeling. Math. Models Methods Appl. Sci. 23(14), 2749–2776 (2013)

    Article  MathSciNet  Google Scholar 

  10. Vassilevski, Y., Danilov, A., Ivanov, Y., Simakov, S., Gamilov, T.: Personalized anatomical meshing of the human body with applications. In: Quarteroni, A. (ed.) Modeling the Heart and the Circulatory System. MS&A, vol. 14, pp. 221–236. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-05230-4_9

    Chapter  Google Scholar 

  11. Danilov, A.A., et al.: Parallel software platform INMOST: a framework for numerical modeling. Supercomput. Frontiers Innov. 2(4), 55–66 (2015)

    Google Scholar 

  12. Pravdin, S., et al.: Human heart simulation software for parallel computing systems. Procedia Comput. Sci. 66, 402–411 (2015)

    Article  Google Scholar 

  13. Mirams, G.R., et al.: Chaste: an open source C++ library for computational physiology and biology. PLoS Comput. Biol. 9(3), e1002970 (2013)

    Article  MathSciNet  Google Scholar 

  14. Schmitt, O.H.: Biological information processing using the concept of interpenetrating domains. In: Leibovic, K.N. (ed.) Information Processing in The Nervous System, pp. 325–331. Springer, Heidelberg (1969). https://doi.org/10.1007/978-3-662-25549-0_18

    Chapter  Google Scholar 

  15. Tung, L.: A bi-domain model for describing ischemic myocardial dc potentials. Massachusetts Institute of Technology (1978)

    Google Scholar 

  16. Karypis, G., Kumar, V.: Parallel multilevel k-way partitioning scheme for irregular graphs. SIAM Rev. 41(2), 278–300 (1999)

    Article  MathSciNet  Google Scholar 

  17. Balay, S.: PETSc Users Manual, ANL-95/11–Revision 3.8. Argonne National Lab (2017)

    Google Scholar 

  18. Intel MKL. Sparse solver routines. https://software.intel.com/en-us/mkl-developer-reference-fortran-sparse-solver-routines. Accessed 1 Mar 2018

  19. Lebedev, S., Akhmedzhanov, D., Kozinov, E., Meyerov, I., Pirova, A., Sysoyev, A.: Dynamic parallelization strategies for multifrontal sparse cholesky factorization. In: Malyshkin, V. (ed.) PaCT 2015. LNCS, vol. 9251, pp. 68–79. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21909-7_7

    Chapter  Google Scholar 

  20. Bastrakov, S., et al.: High performance computing in biomedical applications. Procedia Comput. Sci. 18, 10–19 (2013)

    Article  Google Scholar 

  21. Linge, S., et al.: Numerical solution of the bidomain equations. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 367(1895), 1931–1950 (2009)

    Article  MathSciNet  Google Scholar 

  22. Clayton, R.H., et al.: Models of cardiac tissue electrophysiology: progress, challenges and open questions. Progr. Biophys. Mol. Biol. 104(1), 22–48 (2011)

    Article  Google Scholar 

  23. Pathmanathan, P., et al.: A numerical guide to the solution of the bidomain equations of cardiac electrophysiology. Progr. Biophys. Mol. Biol. 102(2), 136–155 (2010)

    Article  Google Scholar 

  24. Strang, G.: On the construction and comparison of difference schemes. SIAM J. Numer. Anal. 5(3), 506–517 (1968)

    Article  MathSciNet  Google Scholar 

  25. Bernabeu, M.O., et al.: Chaste: a case study of parallelisation of an open source finite-element solver with applications to computational cardiac electrophysiology simulation. Int. J. HPC Appl. 28(1), 13–32 (2014)

    MathSciNet  Google Scholar 

  26. Sundnes, J., Lines, G.T., Tveito, A.: An operator splitting method for solving the bidomain equations coupled to a volume conductor model for the torso. Math. Biosci. 194(2), 233–248 (2005)

    Article  MathSciNet  Google Scholar 

  27. Santos, R.W., et al.: Parallel multigrid preconditioner for the cardiac bidomain model. IEEE Trans. Biomed. Eng. 51(11), 1960–1968 (2004)

    Article  MathSciNet  Google Scholar 

  28. Vasiliev, E.: Generation of an atlas-based finite element model of the heart for cardiac simulation. Int. Sci. J. Math. Model. 4, 207–209 (2017)

    Google Scholar 

  29. Lachinov, D., Belokamenskaya, A., Turlapov, V.: Precise automatic cephalometric landmark detection algorithm for CT images. In: Proceedings of Graphicon 2017, pp. 275–278 (2017)

    Google Scholar 

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Acknowledgements

The study was supported by the Ministry of Education of Russian Federation (Contract # 02.G25.31.0157, date 01.12.2015).

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Correspondence to Grigory Osipov .

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Petrov, V. et al. (2019). CardioModel – New Software for Cardiac Electrophysiology Simulation. In: Voevodin, V., Sobolev, S. (eds) Supercomputing. RuSCDays 2018. Communications in Computer and Information Science, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-05807-4_17

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  • DOI: https://doi.org/10.1007/978-3-030-05807-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05806-7

  • Online ISBN: 978-3-030-05807-4

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