Variational Myocardial Tracking from Cine-MRI with Non-linear Regularization: Validation of Radial Displacements vs. Tagged-MRI

  • Viateur Tuyisenge
  • Adélaïde Albouy-Kissi
  • Laurent Sarry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7945)


We present a new motion estimation approach for cardiac Magnetic Resonance Imaging (Cine-MRI) data from variational framework. The improved performance of this variational approach has been achieved by designing a new regularization term that properly handles motion discontinuities. This approach was applied to both synthetic and real data. The quantitative evaluation revealed that the results of proposed method on cine-MRI correlates with the results given by inTag, reference approach on tagged-MRI.


Motion Estimation Cardiac Magnetic Resonance Image Radial Displacement Regularization Term Angular Error 
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  1. 1.
    Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. In: ICCV (2007)Google Scholar
  2. 2.
    Zhang, Z., Song, X., Sahn, D.J.: Cardiac motion estimation from 3D echocardiography with spatiotemporal regularization. In: Metaxas, D.N., Axel, L. (eds.) FIMH 2011. LNCS, vol. 6666, pp. 350–358. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. International Journal of Computer Vision 12, 43–77 (1994)CrossRefGoogle Scholar
  4. 4.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Carranza-Herrezueloa, N., Bajo, A., Sroubek, F., Santamarta, C., Cristobal, G., Santos, A., Ledesma-Carbayo, M.J.: Motion estimation of tagged cardiac magnetic resonance images using variational techniques. Computerized Medical Imaging and Graphics 34, 514–522 (2010)CrossRefGoogle Scholar
  6. 6.
    Cohen, I.: Nonlinear variational method for optical flow computation. In: Proceedings of the 8th SCIA, pp. 523–630 (1993)Google Scholar
  7. 7.
    Deriche, R., Kornprobst, P., Aubert, G.: Optical-flow estimation while preserving its discontinuities: A variational approach. In: Asian Conference on Computer Vision, vol. 2, pp. 71–80 (1995)Google Scholar
  8. 8.
    Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  9. 9.
    Kima, Y., Martinez, A.M., Kaka, A.C.: Robust motion estimation under varying illumination. Image and Vision Computing 23, 365–375 (2005)CrossRefGoogle Scholar
  10. 10.
    Mansi, T., Peyrat, J.-M., Sermesant, M., Delingette, H., Blanc, J., Boudjemline, Y., Ayache, N.: Physically-constrained diffeomorphic demons for the estimation of 3D myocardium strain from cine-MRI. In: Ayache, N., Delingette, H., Sermesant, M. (eds.) FIMH 2009. LNCS, vol. 5528, pp. 201–210. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Mitiche, A., Bouthemy, P.: Computation and analysis of image motion: A synopsis of current problems and methods. IJCV 19, 29–55 (1996)CrossRefGoogle Scholar
  12. 12.
    Nagel, H.H.: Constraints for the estimation of displacement vector fields from image sequences. In: Proc. Eighth Int. Joint Conf. on Artificial Intelligence, IJCAI, pp. 945–951 (1983)Google Scholar
  13. 13.
    Papademetris, X., Sinusas, A.J., Dione, D.P., Duncan, J.S.: Estimation of 3D left ventricular deformation from echocardiography. Medical Image Analysis 5, 17–28 (2001)CrossRefGoogle Scholar
  14. 14.
    Stiller, C., Konrad, J.: Estimating motion in image sequences. IEEE Signal Proc. Magazine 16, 70–91 (1999)CrossRefGoogle Scholar
  15. 15.
    Tschumperlé, D., Deriche, R.: Restauration d’images vectorielles par EDP. In: 12ème Congres RFIA, vol. 17, pp. 247–256 (2000)Google Scholar
  16. 16.
    Tuyisenge, V., Albouy-Kissi, A., Cassagnes, L., Coupez, E., Merlin, C., Windyga, P., Sarry, L.: Variational myocardial tracking from cine-MRI with non-linear regularization. In: Proc. of the 10th IEEE Int. Symposium on Biomedical Imaging 2013 (ISBI 2013), San Francisco, USA. IEEE (2013)Google Scholar
  17. 17.
    Weickert, J.: On discontinuity-preserving optical flow. In: Proc. Computer Vision and Mobile Robotics Workshop, pp. 115–122 (1998)Google Scholar
  18. 18.
    Weickert, J., Bruhn, A., Brox, T., Papenberg, N.: A survey on variational optic flow methods for small displacements. Mathematics in Industry 10, 103–136 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Viateur Tuyisenge
    • 1
  • Adélaïde Albouy-Kissi
    • 1
  • Laurent Sarry
    • 1
  1. 1.ISIT UMR 6284 UdA-CNRSClermont Université, Université d’ AuvergneClermont-FerrandFrance

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