Deformation Field Estimation for the Cardiac Wall Using Doppler Tissue Imaging

  • Valérie Moreau
  • Laurent D. Cohen
  • Denis Pellerin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2230)


This paper presents different ways to use the Doppler Tissue Imaging (DTI) in order to determine deformation of the cardiac wall. As an extra information added to the ultrasound images, the DTI gives the velocity in the direction of the sensor. We FIrst show a way to track points along the cardiac wall in a M-Mode image (1D+t). This is based on energy minimization similar to a deformable grid. We then extend the ideas to finding the deformation field in a sequence of 2D images (2D+t). This is based on energy minimization including spatio-temporal regularization and a priori constraints.


Cardiac image processing Ultrasound Image Doppler Tissue Imaging motion estimation multi-modality image fusion 


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  1. 1.
    L. D. Cohen, F. Pajany, D. Pellerin, and C. Veyrat. Cardiac wall tracking using doppler tissue imaging (DTI). In In Proc. of International Conference on Image Processing (ICIP’96), pages III–295–298, Lausanne, Switzerland, Sept. 1996.Google Scholar
  2. 2.
    M. Bro-Nielsen. Active Nets and Cubes. Technical Report, Institute of Mathematical Modelling, Technical University of Denmark, Nov 1994.Google Scholar
  3. 3.
    K. Yoshino, T. Kawashima and Y. Aoki. Dynamic Reconfiguration of Active Net Structure. Asian Conference on Computer Vision, November 1993.Google Scholar
  4. 4.
    L.D. Cohen. On active contours models and balloons. Computer Vision, Graphics, and Image Processing: Image Understanding, 53(2):211–218 March 1991.zbMATHGoogle Scholar
  5. 5.
    A. Blake, A. Zisserman, Visual Reconstruction. MIT Press, 1987.Google Scholar
  6. 6.
    C. Veyrat, D. Pellerin, L.D. Cohen, F. Larrazet, F. Extramiana, and S. Witchitz Spectral, one-or two-dimensional tissue velocity doppler imaging: which to choose? Cardiology, 9(1):9–18, 2000.Google Scholar
  7. 7.
    D. Pellerin, A. Berdeaux, L.D. Cohen, J.F. Giudicelli, S. Witchitz, and C. Veyrat, Comparison of two myocardial velocity gradient assessment methods during dobutamine infusion using doppler myocardial imaging. Journal of the American Society of Echocardiography, 12:22–31, 1999.CrossRefGoogle Scholar
  8. 8.
    B.K.P. Horn and B.G. Schunck. Determining Optical Flow. Artificial Intelligence, (17) (1–3):185–204, 1981.CrossRefGoogle Scholar
  9. 9.
    G. Aubert, R. Deriche and P. Kornprobst. Optical flow estimation while preserving its dicontinuities: A variational approach. Proceedings of the second asian conference on computer vision.,1995Google Scholar
  10. 10.
    J.L. Barron, D.J. Fleet, S.S. Beauchemin, Performances of optical flow techniques. International Journal of Computer Vision, 12(1) p.43–77 (1994).CrossRefGoogle Scholar
  11. 11.
    J. Weickert et C. Schnorr. Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint. Journal of Mathematical Imaging and Vision, 14(3), May 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Valérie Moreau
    • 1
  • Laurent D. Cohen
    • 1
  • Denis Pellerin
    • 2
  1. 1.CEREMADEUniversity of Paris-DauphineParisFrance
  2. 2.St George’s Hospital Medical SchoolUniversity of LondonLondon

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