Advertisement

Myocardial Deformation Recovery Using a 3D Biventricular Incompressible Model

  • Arnaud Bistoquet
  • W. James Parks
  • Oskar Škrinjar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4057)

Abstract

This paper presents a nonrigid image registration method for cardiac deformation recovery from 3D MR image sequences. The main contribution of this work is that the method is mathematically guaranteed to generate incompressible deformations. This is a desirable property since the myocardium has been shown to be close to incompressible. The method is based on an incompressible deformable model that can include all four cardiac chambers and has a relatively small number of parameters. The myocardium needs to be segmented in an initial frame after which the method automatically determines the tissue deformation everywhere in the myocardium throughout the cardiac cycle. The method has been tested with four 3D cardiac MR image sequences for the left and right ventricles and it has been evaluated against manual segmentation. The volume agreement between the model and the manual segmentation exceeds 90% and the distance between the model and the manually generated endocardial and epicardial surface is 1.65mm on average.

Keywords

Right Ventricle Transformation Model Manual Segmentation Right Atrium Normalize Mutual Information 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tustison, N., Amini, A.: Biventricular Myocardial Strains via Nonrigid Registration of AnFigatomical NURBS Models. IEEE Trans. on Medical Imaging 25, 94–112 (2006)CrossRefGoogle Scholar
  2. 2.
    Pan, L., Prince, J., Lima, J., Osman, N.: Fast tracking of cardiac motion using 3D-HARP. IEEE Trans. on Biomedical Engineering 52, 1425–1435 (2005)CrossRefGoogle Scholar
  3. 3.
    Cao, Y., Miller, M., Winslow, R., Younes, L.: Large Deformation Diffeomorphic Metric Mapping of Vector Fields. IEEE Trans. on Medical Imaging 24, 1216–1230 (2005)CrossRefGoogle Scholar
  4. 4.
    Gilson, W., Yuang, Z., French, B., Epstein, F.: Measurement of myocardial mechanics in mice before and after infarction using multislice displacement-encoded MRI with 3D motion encoding. American Journal of Physiol.- Heart Circ. Physiol. 288, 1491–1497 (2005)CrossRefGoogle Scholar
  5. 5.
    Meyer, F., Constable, R., Sinusas, A., Duncan, J.: Tracking Myocardial Deformation Using Phase Contrast MR Velocity Fields: A Stochastic Approach. IEEE Trans. on Medical Imaging 15, 453–465 (1996)CrossRefGoogle Scholar
  6. 6.
    Kaus, M., Von Berg, J., Weese, J., Niessen, W., Pekar, V.: Automated segmentation of the left ventricle in cardiac MRI. Medical Image Analysis 8, 245–254 (2004)CrossRefGoogle Scholar
  7. 7.
    Huang, H., Shen, L., Zhang, R., Makedon, F., Hettleman, B., Pearlman, J.: Surface Alignment of 3D Spherical Harmonics Models: Application to Cardiac MRI Analysis. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 67–74. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Uzümcü, M., Frangi, A., Sonka, M., Reiber, J., Lelieveldt, B.: ICA vs. PCA Active Appearance Models: Application to Cardiac MR Segmentation. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 451–458. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Gering, D.: Automatic segmentation of cardiac MRI. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 524–532. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Van Assen, H., Danilouchkine, M., Behloul, F., Lamb, H., Van Der Geest, R., Reiber, J., Lelieveldt, B.: Cardiac LV Segmentation Using a 3D Active Shape Model Driven by Fuzzy Inference. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 533–540. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Montagnat, J., Delingette, H.: 4D deformable Models with temporal constraints: applications to 4D cardiac image segmentation. Medical Image Analysis 9, 87–100 (2005)CrossRefGoogle Scholar
  12. 12.
    Sermesant, M., Forest, C., Pennec, X., Delingette, H., Ayache, N.: Deformable biomechanical models: Application to 4D cardiac image analysis. Medical Image Analysis 7, 475–488 (2003)CrossRefGoogle Scholar
  13. 13.
    Remme, E., Augenstein, K., Young, A., Hunter, P.: Parameters Distribution Models for Estimation of Population Based Left Ventricular Deformation Using Sparse Fiducial Markers. IEEE Trans. on Medical Imaging 24, 381–392 (2005)CrossRefGoogle Scholar
  14. 14.
    Papademteris, X., Sinusas, A., Dione, D., Constable, R., Duncan, J.: Estimation of 3-D Left Ventricular Deformation From Medical Images Using Biomechanical Models. IEEE Trans. on Medical Imaging 21, 524–532 (2002)Google Scholar
  15. 15.
    Lorenzo-Valdes, M., Sanchez-Ortiz, G., Mohiaddin, R., Rueckert, D.: Atlas-based segmentation and tracking of 3D cardiac MR images using non-rigid registration. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 642–650. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Lorenzo-Valdes, M., Sanchez-Ortiz, G., Elkington, A., Mohiaddin, R., Rueckert, D.: Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Medical Image Analysis 8, 255–265 (2004)CrossRefGoogle Scholar
  17. 17.
    Shen, D., Sundar, H., Xue, Z., Fan, Y., Litt, H.: Consistent Estimation of Cardiac Motions by 4D Image Registration. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 902–910. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Lin, N., Duncan, J.: Generalized Robust Point Matching Using an Exented Free-Form Deformation Model: Application to Cardiac Images. In: International Symposium on Biomedical Imaging, pp. 320–324 (2004)Google Scholar
  19. 19.
    Papademetris, X., Shi, P., Dione, D., Sinusas, A., Duncan, J.: Recovery of soft tissue object deformation from 3D image sequences using biomechanical models. In: Kuba, A., Sámal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 352–357. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  20. 20.
    Fan, L., Chen, C.: LV Motion Estimation Based on the Integration of Continuum Mechanics and Estimation Theory. In: SPIE Medical Imaging, pp. 81–92 (1999)Google Scholar
  21. 21.
    Yin, F., Chan, C., Judd, R.: Compressibility of Perfused Passive Myocardium. American Journal of Physiol.- Heart Circ. Physiol. 8, 1864–1870 (1996)Google Scholar
  22. 22.
    Judd, R., Levy, B.: Effects of Barium-induced Cardiac Contraction on Large and Small Vessel Intramyocardial Blood Volume. Circulation, 217–225 (1991)Google Scholar
  23. 23.
    Liu, Y., Bahn, R., Ritman, E.: Dynamic Intramyocardial Blood Volume: Evaluation with a Radiological Opaque Marker Method. American Journal of Physiol.- Heart Circ. Physiol. 12, 963–967 (1992)Google Scholar
  24. 24.
    Vergroesen, I., Noble, M., Spaan, J.: Intramyocardial Blood Volume Change in First Moments of Cardiac Arrest in Anesthetized Goats. American Journal of Physiol.- Heart Circ. Physiol. 4, 307–316 (1987)Google Scholar
  25. 25.
    Jakob, M., Hess, O., Jenni, R., Heywood, J., Grimm, J.: Determination of the Left Ventricular Systolic Wall Thickness by Digital Substraction Angiography. European Heart Journal 12, 573–582 (1991)Google Scholar
  26. 26.
    Wahaba, G.: Spline Interpolation and Smoothing on the sphere. SIAM Journal Sci. Stat. Comput. 2, 1–15 (1981)CrossRefGoogle Scholar
  27. 27.
    Carmo, D.: Differential Geometry of Curves and Surfaces. Prentice-Hall, Englewood Cliffs (1976)MATHGoogle Scholar
  28. 28.
    Press, W., Flannery, B., Teukolsky, S., Vetterling, W.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1992)Google Scholar
  29. 29.
    Skrinjar, O., Bistoquet, A.: Cardiac deformation recovery via incompressible transformation decomposition. In: SPIE Medical Imaging, vol. 5747, pp. 361–370 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Arnaud Bistoquet
    • 1
  • W. James Parks
    • 2
  • Oskar Škrinjar
    • 1
    • 3
  1. 1.School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Department of PediatricsSibley Heart Center Cardiology, Children’s Healthcare, of Atlanta, Emory University School of MedicineAtlantaUSA
  3. 3.Department of Biomedical EngineeringGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations