Advertisement

Robust Image-Based Estimation of Cardiac Tissue Parameters and Their Uncertainty from Noisy Data

  • Dominik Neumann
  • Tommaso Mansi
  • Bogdan Georgescu
  • Ali Kamen
  • Elham Kayvanpour
  • Ali Amr
  • Farbod Sedaghat-Hamedani
  • Jan Haas
  • Hugo Katus
  • Benjamin Meder
  • Joachim Hornegger
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Clinical applications of computational cardiac models require precise personalization, i.e. fitting model parameters to capture patient’s physiology. However, due to parameter non-identifiability, limited data, uncertainty in the clinical measurements, and modeling assumptions, various combinations of parameter values may exist that yield the same quality of fit. Hence, there is a need for quantifying the uncertainty in estimated parameters and to ascertain the uniqueness of the found solution. This paper presents a stochastic method to estimate the parameters of an image-based electromechanical model of the heart and their uncertainty due to noise in measurements. First, Bayesian inference is applied to fully estimate the posterior probability density function (PDF) of the model. To that end, Markov Chain Monte Carlo sampling is used, which is made computationally tractable by employing a fast surrogate model based on Polynomial Chaos Expansion, instead of the true forward model. Then, we use the mean-shift algorithm to automatically find the modes of the PDF and select the most likely one while being robust to noise. The approach is used to estimate global active stress and passive stiffness from invasive pressure and image-based volume quantification. Experiments on eight patients showed that not only our approach yielded goodness of fits equivalent to a well-established deterministic method, but we could also demonstrate the non-uniqueness of the problem and report uncertainty estimates, crucial information for subsequent clinical assessments of the personalized models.

Keywords

Probability Density Function Markov Chain Monte Carlo Gaussian Mixture Model Forward Model Markov Chain Monte Carlo Sampling 
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.

References

  1. 1.
    McMurray, J., Adamopoulos, S., Anker, S., Auricchio, A., Dickstein, K., Falk, V., Filippatos, G., Fonseca, C., Gomez-Sanchez, M.: ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur. Heart J. 33(14), 1787–1847 (2012)CrossRefGoogle Scholar
  2. 2.
    Trayanova, N.A.: Whole-heart modeling applications to cardiac electrophysiology and electromechanics. Circ. Res. 108(1), 113–128 (2011)CrossRefGoogle Scholar
  3. 3.
    Prakosa, A., Sermesant, M., Allain, P., Villain, N., Rinaldi, C., Rhode, K., Razavi, R., Delingette, H., Ayache, N.: Cardiac electrophysiological activation pattern estimation from images using a patient-specific database of synthetic image sequences. IEEE TBME 61(2), 235–245 (2014)Google Scholar
  4. 4.
    Chabiniok, R., Moireau, P., Lesault, P.F., Rahmouni, A., Deux, J.F., Chapelle, D.: Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model. Biomech. Model. Mechan. 11(5), 609–630 (2012)CrossRefGoogle Scholar
  5. 5.
    Xi, J., Lamata, P., Lee, J., Moireau, P., Chapelle, D., Smith, N.: Myocardial transversely isotropic material parameter estimation from in-silico measurements based on a reduced-order unscented Kalman filter. J. Mech. Behav. Biomed. 4(7), 1090–1102 (2011)CrossRefGoogle Scholar
  6. 6.
    Wallman, M., Smith, N.P., Rodriguez, B.: Computational methods to reduce uncertainty in the estimation of cardiac conduction properties from electroanatomical recordings. Med. Image Anal. 18(1), 228–240 (2014)CrossRefGoogle Scholar
  7. 7.
    Konukoglu, E., Relan, J., Cilingir, U., Menze, B.H., Chinchapatnam, P., Jadidi, A., Cochet, H., Hocini, M., Delingette, H., Jaïs, P., Haïssaguerre, M., Ayache, N., Sermesant, M.: 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)CrossRefGoogle Scholar
  8. 8.
    Zettinig, O., et al.: From medical images to fast computational models of heart electromechanics: An integrated framework towards clinical use. In: Ourselin, S., Rueckert, D., Smith, N. (eds.) FIMH 2013. LNCS, vol. 7945, pp. 249–258. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE TMI 27(11), 1668–1681 (2008)zbMATHGoogle Scholar
  10. 10.
    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
  11. 11.
    Zettinig, O., Mansi, T., Neumann, D., Georgescu, B., Rapaka, S., Seegerer, P., Kayvanpour, E., Sedaghat-Hamedani, F., Amr, A., Haas, J., Steen, H., Katus, H., Meder, B., Navab, N., Kamen, A., Comaniciu, D.: Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals. Med. Image Anal. (2014)Google Scholar
  12. 12.
    Marzouk, Y.M., Najm, H.N., Rahn, L.A.: Stochastic spectral methods for efficient bayesian solution of inverse problems. J. Comput. Phys. 224(2), 560–586 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Haario, H., Laine, M., Mira, A., Saksman, E.: DRAM: efficient adaptive MCMC. Stat. Comput. 16(4), 339–354 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Adams, B.M., Dalbey, K.R., Eldred, M.S., Gay, D.M., Swiler, L.P.: DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: Version 5.4 user’s manual. Tech. rep., Sandia National Laboratories (2013)Google Scholar
  15. 15.
    Powell, M.J.: The BOBYQA algorithm for bound constrained optimization without derivatives. Tech. rep., University of Cambridge (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dominik Neumann
    • 1
    • 2
  • Tommaso Mansi
    • 1
  • Bogdan Georgescu
    • 1
  • Ali Kamen
    • 1
  • Elham Kayvanpour
    • 3
  • Ali Amr
    • 3
  • Farbod Sedaghat-Hamedani
    • 3
  • Jan Haas
    • 3
  • Hugo Katus
    • 3
  • Benjamin Meder
    • 3
  • Joachim Hornegger
    • 2
  • Dorin Comaniciu
    • 1
  1. 1.Imaging and Computer VisionSiemens Corporate TechnologyPrincetonUSA
  2. 2.Pattern Recognition LabFAU Erlangen-NürnbergGermany
  3. 3.Department of Internal Medicine IIIUniversity Hospital HeidelbergGermany

Personalised recommendations