Estimation of the Deformation Field for the Left Ventricle Walls in 4-D Multislice Computerized Tomography

  • Antonio Bravo
  • Rubén Medina
  • Gianfranco Passariello
  • Mireille Garreau
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


This paper describes a method for estimating the deformation field of the Left Ventricle (LV) walls from a 4–D Multi Slice Computerized Tomography (MSCT) database. The approach is composed of two stages: in the first, a 2–D non–rigid correspondence algorithm matches a set of contours on the LV at consecutive time instants. In the second, a 3–D curvature–based correspondence algorithm is used to optimize the initial approximate correspondence. The dense displacement field is obtained based on the optimized correspondence. Parameters like LV volume, radial contraction and torsion are estimated. The algorithm is validated on synthetic objects and tested using a 4–D MSCT database. Results are promising as the error of the displacement vectors is 2.69 ± 1.38 mm using synthetic objects and, when tested in real data, local parameters extracted agree with values obtained using tagged magnetic resonance imaging.


Multi Slice Computerize Tomography Left Ventricle Wall Correspondence Algorithm Radial Contraction Synthetic Object 
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  1. 1.
    Opie, L.: Mechanics of cardiac contraction and relaxation. In: Braunwald, E., Zipes, D., Libby, P. (eds.) Heart Disease: A Textbook of Cardiovascular Medicine, 6th edn., pp. 443–478. W. B. Saunders, Philadelphia (2001)Google Scholar
  2. 2.
    Arts, T., Meerbaum, S., Reneman, R.: Torsion of the left ventricle during the ejection phase in the intact dog. Cardiovasc. Res. 18, 183 (1984)CrossRefGoogle Scholar
  3. 3.
    Ingels, N., Daughters, G., Stinson, E., Alderman, E.: Evaluation of methods for quantitating left ventricular segmental wall motion in man using myocardial markers as a standard. Circ. 61(5), 966–972 (1980)Google Scholar
  4. 4.
    Villarreal, F., Waldman, L., Lew, W.: Technique for measuring regional two-dimensional finite strains in canine left ventricle. Circ. Res. 62(4), 711–721 (1988)Google Scholar
  5. 5.
    Fenton, T., Cherry, J., Klassen, G.: Transmural myocardial deformation in the canine left ventricle wall. Am. J. Physiol. Heart Circ. Physiol. 235(4), H523–H530 (1978)Google Scholar
  6. 6.
    Dougherty, L., Asmuth, J.C., Blom, A.S., Axel, L., Kumar, R.: Validation of an optical flow method for tag displacement estimation. IEEE Trans. Med. Imag. 18(4), 359–363 (1999)CrossRefGoogle Scholar
  7. 7.
    Chandrashekara, R., Mohiaddin, R., Rueckert, D.: Analysis of 3–D myocardial motion in tagged MR images using nonrigid image registration. IEEE Trans. Med. Imag. 23(10), 1245–1250 (2004)CrossRefGoogle Scholar
  8. 8.
    Frangi, A.J., Rueckert, D., Duncan, J.S.: Three–dimensional cardiovascular image analysis. IEEE Trans. Med. Imag. 21(9), 1005–1010 (2002)CrossRefGoogle Scholar
  9. 9.
    Simon, A., Garreau, M., Boulmier, D., Coatrieux, J.-L., Le Breton, H.: Cardiac motion extraction using 3D surface matching in multislice computed tomography. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 1057–1059. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Allouche, C., Makram, S., Ayache, N., Delingette, H.: A new kinetic modeling scheme for the human left ventricle wall motion with MR–tagging imaging. In: Katila, T., Magnin, I.E., Clarysse, P., Montagnat, J., Nenonen, J. (eds.) FIMH 2001. LNCS, vol. 2230, pp. 61–68. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  11. 11.
    Gérard, O., Billon, A.C., Rouet, J.-M., Jacob, M., Fradkin, M., Allouche, C.: Efficient model–based quantification of left ventricular function in 3–D echocardiography. IEEE Trans. Med. Imag. 21(9), 1059–1068 (2002)CrossRefGoogle Scholar
  12. 12.
    Frangi, A.J., Niessen, W.J., Viergever, M.A.: Three–dimensional modeling for functional analysis of cardiac images: A review. IEEE Trans. Med. Imag. 20(1), 2–25 (2001)CrossRefGoogle Scholar
  13. 13.
    Papademetris, X., Sinusas, A.J., Dione, D.P., Constable, R.T., Duncan, J.S.: Estimation of 3–D left ventricular deformation from medical images using biomechanical models. IEEE Trans. Med. Imag. 21(7), 786–800 (2002)CrossRefGoogle Scholar
  14. 14.
    Simon, A., Garreau, M., Boulmier, D., Coatrieux, J.-L., Le Breton, H.: A surface/volume matching process using a markov random field model for cardiac motion extraction in MSCT imaging. In: Frangi, A.F., Radeva, P.I., Santos, A., Hernandez, M. (eds.) FIMH 2005. LNCS, vol. 3504, pp. 457–466. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Lehmann, T., Gönner, C., Spitzer, K.: Survey: Interpolation methods in medical image processing. IEEE Trans. Med. Imag. 18(11), 1049–1073 (1999)CrossRefGoogle Scholar
  16. 16.
    Yuille, A., Poggio, T.: Scaling theorems for zero crossings. IEEE Trans. Pattern Anal. Machine Intell. 8(1), 15–25 (1986)zbMATHCrossRefGoogle Scholar
  17. 17.
    Hill, A., Taylor, C., Brett, A.: A framework for automatic landmark identification using a new method of nonrigid correspondence. IEEE Trans. Pattern Anal. Machine Intell. 22(3), 241–251 (2000)CrossRefGoogle Scholar
  18. 18.
    Zhu, P., Chirlian, P.: On critical point detection of digital shapes. IEEE Trans. Pattern Anal. Machine Intell. 17(8), 737–748 (1995)CrossRefGoogle Scholar
  19. 19.
    Shi, P.: Image Analysis of 3D Cardiac Motion Using Physical and Geometrical Models. PhD thesis, Yale University (May 1996)Google Scholar
  20. 20.
    Sander, P., Zucker, S.: Inferring surface trace and differential structure from 3–D images. IEEE Trans. Pattern Anal. Machine Intell. 12(9), 833–854 (1990)CrossRefGoogle Scholar
  21. 21.
    Sederberg, T., Parry, S.: Free–form deformation of solid geometric models. Comput. Graph. 20(4), 537–541 (1986)Google Scholar
  22. 22.
    Bravo, A., Medina, R., Passariello, G., Garreau, M.: Deformable parametric model for left ventricle wall motion simulation. In: Proceedings of the 14th IASTED International Conference on Applied Simulation and Modelling, ASM 2005, Benalmádena, Spain, pp. 24–29. ACTA Press (June 2005)Google Scholar
  23. 23.
    Sermesant, M.: Modéle électromécanique du c\(\oe\)ur pour l‘analyse d‘image et la simulation. PhD thesis, Université de Nice Sophia–Antipolis, Institut National de Recherche en Informatique et Automatique (INRIA), France (2003)Google Scholar
  24. 24.
    Sniderman, A., Marpole, D., Fallen, E.: Regional contraction patterns in the normal and ischemic left ventricle of man. Amer. J. Cardiol. 31(4), 484–489 (1973)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Antonio Bravo
    • 1
  • Rubén Medina
    • 2
  • Gianfranco Passariello
    • 3
  • Mireille Garreau
    • 4
  1. 1.Grupo de BioingenieríaUniversidad Nacional Experimental del Táchira, Decanato de InvestigaciónSan CristóbalVenezuela
  2. 2.Grupo de Ingeniería BiomédicaUniversidad de Los Andes, Facultad de IngenieríaMéridaVenezuela
  3. 3.Grupo de Bioingeniería y Biofísica Aplicada (GBBA)Universidad Simón BolívarSartenejasVenezuela
  4. 4.Laboratoire Traitement du Signal et de L’ImageUniversité de Rennes 1RennesFrance

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