Physiome Model Based State-Space Framework for Cardiac Kinematics Recovery

  • Ken C. L. Wong
  • Heye Zhang
  • Huafeng Liu
  • Pengcheng Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


In order to more reliably recover cardiac information from noise-corrupted patient-specific measurements, it is essential to employ meaningful a priori constraining models and adopt appropriate optimization criteria to couple the models with the measurements. While biomechanical models have been extensively used for myocardial motion recovery with encouraging results, the passive nature of such constraints limits their ability to fully count for the deformation caused by active forces of the myocytes. To overcome such limitations, we propose to adopt a cardiac physiome model as the prior constraint for heart motion analysis. The model is comprised of a cardiac electric wave propagation model, an electromechanical coupling model, and a biomechanical model, and thus more completely describes the macroscopic cardiac physiology. Embedded within a multiframe state-space framework, the uncertainties of the model and the patient-specific measurements are systematically dealt with to arrive at optimal estimates of the cardiac kinematics and possibly beyond. Experiments have been conducted on synthetic data and MR image sequences to illustrate its abilities and benefits.


Synthetic Data Active Force Biomechanical Model Active Region Model Iterative Close Point Algorithm 
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  1. 1.
    Braunwald, E., Zipes, D., Libby, P.: Heart Disease: A Textbook of Cardiovascular Medicine, 6th edn. W.B. Saunders Company, Philadelphia (2001)Google Scholar
  2. 2.
    Knudsen, Z., Holden, A., Brindley, J.: Qualitative modelling of mechano-electrical feedback in a ventricular cell. Bulletin of Mathematical Biology 6 (1997)Google Scholar
  3. 3.
    Nash, M.: Mechanics and Material Properties of the Heart using an Anatomically Accurate Mathematical Model. PhD thesis, University of Auckland (1998)Google Scholar
  4. 4.
    Glass, L., Hunter, P., McCulloch, A. (eds.): Theory of Heart: Biomechanics, Biophysics, and Nonlinear Dynamics of Cardiac Function. Springer, Heidelberg (1991)Google Scholar
  5. 5.
    McCulloch, A., Bassingthwaighte, J., Hunter, P., Noble, D.: Computational biology of the heart: From structure to function. Progress in Biophysics and Molecular Biology 69, 153–155 (1998)CrossRefGoogle Scholar
  6. 6.
    Wong, K.C., Shi, P.: Finite deformation guided nonlinear filtering for multiframe cardiac motion analysis. In: International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 867–874 (2004)Google Scholar
  7. 7.
    Rogers, J., McCulloch, A.: A collocation-Galerkin finite element model of cardiac action potential propagation. IEEE Transactions on Biomedical Engineering 41, 743–757 (1994)CrossRefGoogle Scholar
  8. 8.
    Sermesant, M., Coudière, Y., Delingette, H., Ayache, N.: Progress towards an electro-mechanical model of the heart for cardiac image analysis. In: IEEE International Symposium on Biomedical Imaging, pp. 10–14 (2002)Google Scholar
  9. 9.
    Ayache, N., Chapelle, D., Clément, F., Coudière, Y., Delingette, H., Désidéri, J., Sermesant, M., Sorine, M., Urquiza, J.: Towards model-based estimation of the cardiac electro-mechanical activity from ECG signals and ultrasound images. In: Katila, T., Magnin, I.E., Clarysse, P., Montagnat, J., Nenonen, J. (eds.) FIMH 2001. LNCS, vol. 2230, pp. 120–127. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Wong, L., Liu, H., Sinusas, A., Shi, P.: Spatio-temporal active region model for simultaneous segmentation and motion estimation of the whole heart. In: IEEE Workshop on Variational, Geometric and Level Set Methods in Computer Vision, pp. 193–200 (2003)Google Scholar
  11. 11.
    Besl, P.J., McKay, H.D.: A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 239–256 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ken C. L. Wong
    • 1
  • Heye Zhang
    • 1
  • Huafeng Liu
    • 3
  • Pengcheng Shi
    • 1
    • 2
  1. 1.Department of Electronic and Computer EngineeringHong Kong University of Science and TechnologyHong Kong
  2. 2.School of Biomedical EngineeringSouthern Medical UniversityGuangzhouChina
  3. 3.State Key Laboratory of Modern Optical InstrumentationZhejiang UniversityHanzhouChina

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