Estimation of Cardiac Electrical Propagation from Medical Image Sequence

  • Heye Zhang
  • Chun Lok Wong
  • Pengcheng Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


A novel strategy is presented to recover cardiac electrical excitation pattern from tomographic medical image sequences. The geometrical/physical representation of the heart and the dense motion field of the myocardium are first derived from imaging data through segmentation and motion recovery. The myocardial active forces are then calculated through the law of force equilibrium from the motion field, realized with a stochastic multiframe algorithm. Since tissue active forces are physiologically driven by electrical excitations, we can readily relate the pattern of active forces to the pattern of electrical propagation in myocardium, where spatial regularization is enforced. Experiments are conducted on three-dimensional synthetic data and canine magnetic resonance image sequence with favorable results.


Active Force Tikhonov Regularization Force Equilibrium Move Less Square Essential 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.


  1. 1.
    MacLeod, R.S., Brooks, D.H.: Recent progress in inverse problems in electrocardiology. IEEE EMBS Magazine 17, 73–83 (1998)CrossRefGoogle Scholar
  2. 2.
    Prince, J.L., McVeigh, E.R.: Motion estimation form tagged MR image sequences. IEEE Trans. Med. Img., 238–249 (1992)Google Scholar
  3. 3.
    Haber, E., Metaxas, D., Axel, L.: Motion analysis of the right ventricle from MRI images. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 177–188. Springer, Heidelberg (1998)Google Scholar
  4. 4.
    Chandrashekara, R., Mohiaddin, R., Rueckert, D.: Analysis of 3D myocardial motion in tagged MR images using nonrigid image registration. IEEE Trans. Med. Img. 23, 1245–1250 (2004)CrossRefGoogle Scholar
  5. 5.
    Wong, C.L., Shi, P.: Finite deformation guided nonlinear filtering for multiframe cardiac motion analysis. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 895–902. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Sanchez-Ortiz, G., Sermesant, M., Rhode, K., Chandrashekara, R., Razavi, R., Hill, D., Rueckert, D.: Localization of abnormal conduction pathways for tachyarrhythmia treatment using tagged MRI. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 425–433. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Belytschko, T., Liu, W., Moran, B.: Nonlinear finite elements for continua and structures. John Wiley Sons Ltd, Chichester (2000)MATHGoogle Scholar
  8. 8.
    Borisenko, A.I., Tarapov, I.E.: Vector and tensor analysis with applications. Dover Publications, Inc. (1979)Google Scholar
  9. 9.
    Rogers, J., McCulloch, A.: A collation-galerkin finite element model of cardiac action potential propagation. IEEE Trans. BioMed. Eng. 41, 743–756 (1994)CrossRefGoogle Scholar
  10. 10.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Heye Zhang
    • 1
  • Chun Lok Wong
    • 1
  • Pengcheng Shi
    • 1
    • 2
  1. 1.Medical Image Computing GroupHong Kong University of Science & TechnologyHong Kong
  2. 2.School of Biomedical EngineeringSouthern Medical UniversityGuangzhouChina

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