Advertisement

Spatially-Adaptive Multi-scale Optimization for Local Parameter Estimation: Application in Cardiac Electrophysiological Models

  • Jwala DhamalaEmail author
  • John L. Sapp
  • Milan Horacek
  • Linwei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

The estimation of local parameter values for a 3D cardiac model is important for revealing abnormal tissues with altered material properties and for building patient-specific models. Existing works in local parameter estimation typically represent the heart with a small number of pre-defined segments to reduce the dimension of unknowns. Such low-resolution approaches have limited ability to estimate tissues with varying sizes, locations, and distributions. We present a novel optimization framework to achieve a higher-resolution parameter estimation without using a high number of unknowns. It has two central elements: (1) a multi-scale coarse-to-fine optimization that uses low-resolution solutions to facilitate the higher-resolution optimization; and (2) a spatially-adaptive scheme that dedicates higher resolution to regions of heterogeneous tissue properties whereas retaining low resolution in homogeneous regions. Synthetic and real-data experiments demonstrate the ability of the presented framework to improve the accuracy of local parameter estimation in comparison to optimization based on fixed-segment models.

Keywords

Parameter estimation Cardiac electrophysiological model Multi-scale optimization Gaussian process 

Notes

Acknowledgment

This work is supported by the National Science Foundation under CAREER Award ACI-1350374 and the National Institute of Heart, Lung, and Blood of the National Institutes of Health under Award R21Hl125998.

References

  1. 1.
    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 diagnosis and planning of therapy. IEEE Trans. Med. Imaging 27(11), 1631–1642 (2008)CrossRefGoogle Scholar
  2. 2.
    Clayton, R., Panfilov, A.: A guide to modelling cardiac electrical activity in anatomically detailed ventricles. Prog. Biophys. Mol. Biol. 96(1), 19–43 (2008)CrossRefGoogle Scholar
  3. 3.
    Hastie, T., Tibshirani, R., Friedman, J.: Unsupervised Learning. Springer, New York (2009)zbMATHGoogle Scholar
  4. 4.
    Konukoglu, E., Relan, J., Cilingir, U., Menze, B.H., Chinchapatnam, P., Jadidi, A., Cochet, H., Hocini, M., Delingette, H., Jaïs, P., et al.: Efficient probabilistic model personalization integrating uncertainty on data and parameters: application to eikonal-diffusion models in cardiac electrophysiology. Prog. Biophys. Mol. Biol. 107(1), 134–146 (2011)CrossRefGoogle Scholar
  5. 5.
    Lê, M., Delingette, H., Kalpathy-Cramer, J., Gerstner, E.R., Batchelor, T., Unkelbach, J., Ayache, N.: Bayesian personalization of brain tumor growth model. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015, Part II. LNCS, vol. 9350, pp. 424–432. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24571-3_51CrossRefGoogle Scholar
  6. 6.
    Powell, M.J.: The bobyqa algorithm for bound constrained optimization without derivatives. Cambridge NA report NA2009/06, University of Cambridge, Cambridge (2009)Google Scholar
  7. 7.
    Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., Freitas, N.: Taking the human out of the loop: a review of bayesian optimization. Proc. IEEE 104(1), 148–175 (2016)CrossRefGoogle Scholar
  8. 8.
    Wang, L., Zhang, H., Wong, K.C., Liu, H., Shi, P.: Physiological-model-constrained noninvasive reconstruction of volumetric myocardial transmembrane potentials. IEEE Trans. Biomed. Eng. 57(2), 296–315 (2010)CrossRefGoogle Scholar
  9. 9.
    Wong, K.C., Sermesant, M., Rhode, K., Ginks, M., Rinaldi, C.A., Razavi, R., Delingette, H., Ayache, N.: Velocity-based cardiac contractility personalization from images using derivative-free optimization. J. Mech. Behav. Biomed. Mater. 43, 35–52 (2015)CrossRefGoogle Scholar
  10. 10.
    Zettinig, O., et al.: Fast data-driven calibration of a cardiac electrophysiology model from images and ECG. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 1–8. Springer, Heidelberg (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Jwala Dhamala
    • 1
    Email author
  • John L. Sapp
    • 2
  • Milan Horacek
    • 2
  • Linwei Wang
    • 1
  1. 1.Rochester Institute of TechnologyRochesterUSA
  2. 2.Dalhousie UniversityHalifaxCanada

Personalised recommendations