Integrated Quantitative Analysis of Tagged Magnetic Resonance Images

  • Patrick Clarysse
  • Pierre Croisille
  • Luc Bracoud
  • Isabelle Magnin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2230)


An image processing pipeline is presented for the quantitative analysis of 2D grid tagged Magnetic Resonance images. The first step concerns the automatic extraction of the tagging pattern and the definition of the left ventricular myocardial contours. In a second step, a spatio-temporal displacement field is fitted to the tag data points. Finally, parameters related to the contractile function can be investigated through graphic displays, movies and statistical analysis.


Displacement Field Processing Pipeline Myocardial Contour Image Processing Pipeline Left Ventricular Contour 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    E. A. Zerhouni, D. M. Parish, W. J. Rogers, A. Yang, and E. P. Shapiro, “Human heart: tagging with MR imaging-A method for noninvasive assessment of myocardial motion,” Radiology, vol. 169, pp. 59–63, 1988.Google Scholar
  2. [2]
    C. C. Moore, “Calculation of three-dimensional left ventricular strains from biplanar tagged MR images,” Journal of Magnetic Resonance Imaging, vol. March/April, pp. 165–175, 1992.CrossRefGoogle Scholar
  3. [3]
    W. G. O'Dell, C. C. Moore, W. C. Hunter, E. A. Zerhouni, and E. R. McVeigh, “Threedimensional myocardial deformations: calculation with displacement field fitting to tagged MR images,” Radiology, vol. 195, pp. 829–835, 1995.Google Scholar
  4. [4]
    M. A. Guttman, J. L. Prince, and E. R. McVeigh, “Tag and contour detection in tagged MR images of the left ventricle,” IEEE Transactions on Medical Imaging, vol. 13, N°1, pp. 74–88, 1994.CrossRefGoogle Scholar
  5. [5]
    D. L. Kraitchman, A. A. Young, C.-N. Chang, and L. Axel, “Semi-automatic tracking of myocardial motion in MR tagged images,” IEEE Trans. Medical Imaging, vol. 14, N° 3, pp. 422–433, 1995.CrossRefGoogle Scholar
  6. [6]
    J. Park, D. Metaxas, A. A. Young, and L. Axel, “Deformable models with parameter functions for cardiac motion analysis from tagged MRI data,” IEEE Transactions on Medical Imaging, vol. 15, N° 1, pp. 178–289, 1996.CrossRefGoogle Scholar
  7. [7]
    J. Declerck, N. Ayache, and E. R. Mc Veigh, “Use of a 4D planispheric transformation for the tracking and the analysis of LV motion with tagged MR images,” INRIA Tech. Rep. RR-3535, October 1998.Google Scholar
  8. [8]
    T. S. Denney, “Estimation and detection of myocardial tags in MR image without userdefined myocardial contours,” IEEE Transactions on Medical Imaging, vol. 18, N° 4, pp. 330–344, 1999.CrossRefGoogle Scholar
  9. [9]
    N. F. Osman, E. R. Mc Veigh, and J. L. Prince, “Imaging heart motion using harmonic phase MRI,” IEEE Transactions on Medical Imaging, vol. 19, N° 3, pp. 186–202, 2000.CrossRefGoogle Scholar
  10. [10]
    P. Clarysse, C. Basset, L. Khouas, P. Croisille, D. Friboulet, C. Odet, and I. E. Magnin, “2D Spatial and temporal displacement and deformation field fitting from cardiac MR tagging,” MEDIA, vol. 3, pp. 253–268, 2000.Google Scholar
  11. [11]
    P. Clarysse, M. Han, P. Croisille, and I. E. Magnin, “Exploratory analysis of the spatiotemporal deformation of the myocardium during systole from tagged MRI,” Biomedical Engineering Society Annual Meeting, Seattle, Washington, USA, 2000.Google Scholar
  12. [12]
    M. Han, “Analyse exploratoire de la déformation spatio-temporelle du myocarde à partir de l’imagerie par résonance magnétique de marquage tissulaire,” PhD Thesis, INSA. Lyon, 1999, 201p.Google Scholar
  13. [13]
    M. Vasilescu and D. Terzopoulos, “Adaptive meshes and shells,” Computer Vision and Pattern Recognition, Urbana Champain, Illinnois, USA, 1992.Google Scholar
  14. [14]
    Y. Wang and O. Lee, “Active mesh-A feature seeking and tracking image sequence representation scheme,” IEEE Transactions on Image Processing, vol. 3, N° 5, pp. 610–624, 1994.CrossRefGoogle Scholar
  15. [15]
    C. Nastar and N. Ayache, “Frequency-based nonrigid motion analysis: application to four dimensional medical images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, N° 1, pp. 1067–1079, 1996.CrossRefGoogle Scholar
  16. [16]
    S. Lobregt and M. A. Viergever, “A discrete dynamic contour model,” IEEE Transactions on Medical Imaging, vol. 14, N° 1, pp. 12–24, 1995.CrossRefGoogle Scholar
  17. [17]
    L. Khouas, P. Clarysse, D. Friboulet, and C. Odet, “Fast 2D vector field visualization using a 2D texture synthesis based on an autoregressive filter. Application to cardiac imaging,” Machine Graphics and Vision, vol. 7, N° 4, pp. 751–764, 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Patrick Clarysse
    • 1
  • Pierre Croisille
    • 1
  • Luc Bracoud
    • 1
  • Isabelle Magnin
    • 1
  1. 1.CREATIS, UMR CNRS 5515VilleurbanneFrance

Personalised recommendations