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From Medical Images to Fast Computational Models of Heart Electromechanics: An Integrated Framework towards Clinical Use

  • Oliver Zettinig
  • Tommaso Mansi
  • Bogdan Georgescu
  • Saikiran Rapaka
  • Ali Kamen
  • Jan Haas
  • Karen S. Frese
  • Farbod Sedaghat-Hamedani
  • Elham Kayvanpour
  • Ali Amr
  • Stefan Hardt
  • Derliz Mereles
  • Henning Steen
  • Andreas Keller
  • Hugo A. Katus
  • Benjamin Meder
  • Nassir Navab
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7945)

Abstract

With the recent advances in computational power, realistic modeling of heart function within a clinical environment has come into reach. Yet, current modeling frameworks either lack overall completeness or computational performance, and their integration with clinical imaging and data is still tedious. In this paper, we propose an integrated framework to model heart electromechanics from clinical and imaging data, which is fast enough to be embedded in a clinical setting. More precisely, we introduce data-driven techniques for cardiac anatomy estimation and couple them with an efficient GPU (graphics processing unit) implementation of the orthotropic Holzapfel-Ogden model of myocardium tissue, a GPU implementation of a mono-domain electrophysiology model based on the Lattice-Boltzmann method, and a novel method to correctly capture motion during isovolumetric phases. Benchmark experiments conducted on patient data showed that the computation of a whole heart cycle including electrophysiology and biomechanics with mesh resolutions of around 70k elements takes on average 1min 10s on a standard desktop machine (Intel Xeon 2.4GHz, NVIDIA GeForce GTX 580). We were able to compute electrophysiology up to 40.5× faster and biomechanics up to 15.2× faster than with prior CPU-based approaches, which breaks ground towards model-based therapy planning.

Keywords

Anatomical Model Heart Cycle Cardiac Electrophysiology Volume Mesh Mechanical Boundary Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-Euclidean metrics for fast and simple calculus on diffusion tensors. MRM 56(2), 411–421 (2006)CrossRefGoogle Scholar
  2. 2.
    Bayer, J., Blake, R., Plank, G., Trayanova, N.: A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. ABME 40, 2243–2254 (2012)Google Scholar
  3. 3.
    Clayton, R., Bernus, O., Cherry, E., Dierckx, H., Fenton, F., Mirabella, L., Panfilov, A., Sachse, F., Seemann, G., Zhang, H.: Models of cardiac tissue electrophysiology: Progress, challenges and open questions. PBMB 104(1-3), 22 (2011)Google Scholar
  4. 4.
    Holzapfel, G.A., Ogden, R.W.: Constitutive modelling of passive myocardium: a structurally based framework for material characterization. Phil. Trans. R. Soc. A 367(1902), 3445–3475 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Kerckhoffs, R., Neal, M., Gu, Q., Bassingthwaighte, J., Omens, J., McCulloch, A.: Coupling of a 3D finite element model of cardiac ventricular mechanics to lumped systems models of the systemic and pulmonic circulation. ABME 35, 1–18 (2007)Google Scholar
  6. 6.
    Mansi, T.: Image-Based Physiological and Statistical Models of the Heart, Application to Tetralogy of Fallot. Ph.D. thesis, Mines ParisTech (2010)Google Scholar
  7. 7.
    Marchesseau, S., Heimann, T., Chatelin, S., Willinger, R., Delingette, H.: Fast porous visco-hyperelastic soft tissue model for surgery simulation: Application to liver surgery. PBMB 103, 185–196 (2010)Google Scholar
  8. 8.
    McMurray, J., et al.: ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012. European Heart Journal 33(14), 1787–1847 (2012)CrossRefGoogle Scholar
  9. 9.
    Miller, K., Joldes, G., Lance, D., Wittek, A.: Total lagrangian explicit dynamics finite element algorithm for computing soft tissue deformation. Communications in Numerical Methods in Engineering 23(2), 121–134 (2007)MathSciNetzbMATHCrossRefGoogle Scholar
  10. 10.
    Moireau, P.: Assimilation de données par filtrage pour les systèmes hyperboliques du second ordre-Applications à la mécanique cardiaque. Ph.D. thesisGoogle Scholar
  11. 11.
    Mosegaard, J., Herborg, P., Sorensen, T.: A GPU accelerated spring mass system for surgical simulation. Studies Health Tech. & Inf. 111, 342–348 (2005)Google Scholar
  12. 12.
    Promayon, E., Baconnier, P., Puech, C.: Physically-Based Deformations Constrained in Displacements and Volume. Computer Graphics Forum 15(3), 155–164 (1996)CrossRefGoogle Scholar
  13. 13.
    Rapaka, S., Mansi, T., Georgescu, B., Pop, M., Wright, G.A., Kamen, A., Comaniciu, D.: LBM-EP: Lattice-boltzmann method for fast cardiac electrophysiology simulation from 3D images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 33–40. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  14. 14.
    Sermesant, M., Delingette, H., Ayache, N.: An electromechanical model of the heart for image analysis and simulation. IEEE TMI 25(5), 612–625 (2006)Google Scholar
  15. 15.
    Taylor, Z., Cheng, M., Ourselin, S.: High-speed nonlinear finite element analysis for surgical simulation using graphics processing units. IEEE Transactions on Medical Imaging 27(5), 650–663 (2008)CrossRefGoogle Scholar
  16. 16.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3 cardiac CT volumes using marginal space learning and steerable features. IEEE TMI 27, 1668–1681 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Oliver Zettinig
    • 1
    • 2
  • Tommaso Mansi
    • 1
  • Bogdan Georgescu
    • 1
  • Saikiran Rapaka
    • 1
  • Ali Kamen
    • 1
  • Jan Haas
    • 3
  • Karen S. Frese
    • 3
  • Farbod Sedaghat-Hamedani
    • 3
  • Elham Kayvanpour
    • 3
  • Ali Amr
    • 3
  • Stefan Hardt
    • 3
  • Derliz Mereles
    • 3
  • Henning Steen
    • 3
  • Andreas Keller
    • 4
    • 5
  • Hugo A. Katus
    • 3
  • Benjamin Meder
    • 3
  • Nassir Navab
    • 2
  • Dorin Comaniciu
    • 1
  1. 1.Imaging and Computer VisionSiemens Corporation, Corporate TechnologyPrincetonUSA
  2. 2.Computer Aided Medical ProceduresTechnische Universität MünchenGermany
  3. 3.Department of Internal Medicine III - Cardiology, Angiology and PneumologyUniversity Hospital HeidelbergHeidelbergGermany
  4. 4.Siemens AG, Healthcare StrategyErlangenGermany
  5. 5.Department of Human GeneticsSaarland UniversityGermany

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