Temporal Diffeomorphic Motion Analysis from Echocardiographic Sequences by Using Intensity Transitivity Consistency

  • Zhijun Zhang
  • David J. Sahn
  • Xubo Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7085)


Quantitative motion analysis from echocardiography is an important yet challenging problem. We develop a motion estimation algorithm for echocardiographic sequences based on diffeomorphic image registration in which the velocity field is spatiotemporally smooth. The novelty of this work is that we propose a functional of the velocity field which minimizes the intensity consistency error of the local unwarped frames. The consistency error is measured as the sum of squared difference of the four frames evolving to any time point between the two inner frames of them. The estimated spatiotemporal transformation has maximum local transitivity consistency. We validate our method by using simulated images with known ground truth and real ultrasound datasets, experiment results indicate that our motion estimation method is more accurate than other methods.


Motion Estimation Image Registration Cardiac Motion Nonrigid Registration Deformable Image Registration 
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.


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  1. 1.
    Buckberg, G., Hoffman, J.I.E., Mahajan, A., Saleh, S., Coghlan, C.: Cardiac Mechanics Revisited The Relationship of Cardiac Architecture to Ventricular Function. Circulation 118, 2571–2587 (2008)CrossRefGoogle Scholar
  2. 2.
    Hung, J., Lang, R., Flachskampf, F., Shernan, S.K., McCulloch, M.L., Adams, D.B., Thomas, J., Vannan, M., Ryan, T.: 3D Echocardiograhy: A Review of the Current Status and Future Directions. JASE 20(3), 213–233 (2007)Google Scholar
  3. 3.
    Frangi, A.F., Niessen, W.J., Viergever, M.V.: Three-Dimensional Modeling for Functional Analysis of Cardiac Images: A Review. IEEE Trans. Med. Imag. 20(1), 1–25 (2001)CrossRefGoogle Scholar
  4. 4.
    Papademetris, X., Sinusas, A.J., Dione, D.P., Duncan, J.S.: Estimation of 3D left ventricular deformation from echocardiography. Med. Imag. Anal. 5(1), 17–28 (2001)CrossRefGoogle Scholar
  5. 5.
    Wang, Y., Georgescu, B., Houle, H., Comaniciu, D.: Volumetric Myocardial Mechanics from 3D+t Ultrasound Data with Multi-model Tracking. In: Camara, O., Pop, M., Rhode, K., Sermesant, M., Smith, N., Young, A. (eds.) STACOM 2010. LNCS, vol. 6364, pp. 184–193. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Elen, A., Choi, H.F., Loeckx, D., Gaom, H., Claus, P., Suetens, P., Maes, F., D’hooge, J.: Three-dimensional cardiac strain estimation using spatio-temporal elastic registration of ultrasound images: a feasibility study. IEEE Trans. Med. Imag. 27(11), 1580–1591 (2008)CrossRefGoogle Scholar
  7. 7.
    Myronenko, A., Song, X., Sahn, D.J.: LV Motion Tracking from 3D Echocardiography Using Textural and Structural Information. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 428–435. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Rougon, N., Petitjean, C., Preteux, F., Cluzel, P., Grenier, P.: A non-rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures. Medical Image Analysis 9, 353–375 (2005)CrossRefGoogle Scholar
  9. 9.
    Ledesma-Carbayo, M.J., Mah-Casado, P., Santos, A., Prez-David, E., GarMA, D.M.: Spatio-Temporal Nonrigid Registration for Ultrasound Cardiac Motion Estimation. IEEE Trans. Med. Imag. 24(9), 1113–1126 (2005)CrossRefGoogle Scholar
  10. 10.
    Metz, C.T., Klein, S., Schaap, M., Walsum, T., Niessen, W.J.: Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach. Med. Imag. Anal. 15(2), 238–249 (2011)CrossRefGoogle Scholar
  11. 11.
    Delhay, B., Clarysse, P., Magnin, I.E.: Locally Adapted Spatio-temporal Deformation Model for Dense Motion Estimation in Periodic Cardiac Image Sequences. In: Sachse, F.B., Seemann, G. (eds.) FIHM 2007. LNCS, vol. 4466, pp. 393–402. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Castillo, E., Castillo, R., Martinez, J., Shenoy, M., Guerrero, T.: Four-dimensional deformable image registration using trajectory modeling. Physics in Medicine and Biology 55, 305–327 (2010)CrossRefGoogle Scholar
  13. 13.
    Sundar, H., Littb, H., Shen, D.G.: Estimating myocardial motion by 4D image warping. Pattern Recognition 42, 2514–2526 (2009)CrossRefGoogle Scholar
  14. 14.
    Skrinjar, O., Bistoquet, A., Tagare, H.: Symmetric and Transitive Registration of Image Sequences. IJBI (2008)Google Scholar
  15. 15.
    Beg, M.F., Miller, M.I., Trouve, A., Younes, L.: Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms. International Journal of Computer Vision 61(2), 139–157 (2005)CrossRefGoogle Scholar
  16. 16.
    Arsigny, V., Commowick, O., Pennec, X., Ayache, N.: A Log-Euclidean Framework for Statistics on Diffeomorphisms. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 924–931. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  17. 17.
    Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroImage 38(1), 95–113 (2007)CrossRefGoogle Scholar
  18. 18.
    Khan, A.R., Beg, M.F.: Representation of time-varying shapes in the large deformation diffeomorphic framework. In: ISBI 2008, pp. 1521–1524 (2008)Google Scholar
  19. 19.
    De Craene, M., Camara, O., Bijnens, B.H., Frangi, A.F.: Large Diffeomorphic FFD Registration for Motion and Strain Quantification from 3D-US Sequences. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 437–446. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    De Craene, M., Piella, G., Duchateau, N., Silva, E., Doltra, A., Gao, H., D’hooge, J., Camara, O., Brugada, J., Sitges, M., Frangi, A.F.: Temporal Diffeomorphic Free-Form Deformation for Strain Quantification in 3D-US Images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 1–8. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Dupuis, P., Grenander, U.: Variational problems on flows of diffeomorphisms for image matching. Q. Appl. Math. 56(3), 587–600 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Rueckert, D., Aljabar, P., Heckemann, R.A., Hajnal, J.V., Hammers, A.: Diffeomorphic Registration Using B-Splines. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 702–709. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  23. 23.
    Meunier, J.: Tissue motion assessment from 3D echographic speckle tracking. Phys. Med. Biol. 43, 1241–1254 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhijun Zhang
    • 1
  • David J. Sahn
    • 1
    • 2
  • Xubo Song
    • 1
  1. 1.Department of Biomedical EngineeringOregon Health and Science UniversityBeavertonUSA
  2. 2.Department of Pediatric CardiologyOregon Health and Science UniversityBeavertonUSA

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