Advertisement

Fast Motion Tracking of Tagged MRI Using Angle-Preserving Meshless Registration

  • Ting Chen
  • Xiaoxu Wang
  • Dimitris Metaxas
  • Leon Axel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)

Abstract

Fast tracking of motion is the key step towards tagged MRI-based quantitative cardiac analysis. Existing motion tracking approaches, including the widely used HARP method, are either time consuming or qualitatively inconsistent, or both. We present in this paper a new fast motion tracking method based on a meshless kernel. For MR image sequences containing multiple image frames, tag intersections are automatically detected in all frames and indexed in the first frame. Then a thin plate spline approach is used to establish a point-to-point correspondence between tag intersections in the initial and the current frame. Lastly, we use a meshless registration kernel to generate a dense displacement map that minimizes the residual of sparse motion at intersections. To further improve the motion tracking, we develop a special technique to preserve tangential angles of tags at tag intersections. We tested our new method on both numerical phantoms and in vivo heart data. The motion tracking results are evaluated against the ground truth and manually drawn tags. Clinical application potential is demonstrated by cardiac strain analysis based on the proposed methodology.

Keywords

tagged MRI motion tracking meshless registration 

Supplementary material

978-3-540-85990-1_38_MOESM1_ESM.zip (1.2 mb)
Electronic Supplementary Material (1,269 KB)

References

  1. 1.
    Gabor, D.: Theory of communication. J. IEE 93(3), 429–457 (1946)Google Scholar
  2. 2.
    Lancaster, P., Salkauskas, K.: Surfaces generated by moving least squares methods. Mathematics of Computation, 141–158 (1981)Google Scholar
  3. 3.
    Axel, L., Dougherty, L.: MR imaging of motion with spatial modulation of magnetization. Radiology l7l, 841–845 (1989)CrossRefGoogle Scholar
  4. 4.
    Axel, L., Dougherty, L.: Improved method of spatial modulation of magnetization (SPAMM) for MRI of heart wall motion. Radiology l72, 349–350 (1989)CrossRefGoogle Scholar
  5. 5.
    Wahba, G.: Spline models for observational data. SIAM, Philadelphia (1990)CrossRefzbMATHGoogle Scholar
  6. 6.
    Metaxas, D.: Physics-based Deformable Models: Application to Computer Vision, Graphics and Medical Imaging. Springer, Heidelberg (1996)Google Scholar
  7. 7.
    Belytschko, T., Krongauz, Y., Organ, D., Fleming, M., Krysl, P.: Meshless methods: An Overview and Recent Developments. Computer Methods in Applied Mechanics and Engineering, 3–47 (1996)Google Scholar
  8. 8.
    Young, A.: Model tags: direct 3D tracking of heart wall motion from tagged magnetic resonance images. Medical Image Analysis, 361–372 (1999)Google Scholar
  9. 9.
    Osman, N.F., McVeigh, E.R., Prince, J.L.: Imaging heart motion using Harmonic Phase MRI. IEEE Trans. on Medical Imaging 19(3), 186–202 (2000)CrossRefGoogle Scholar
  10. 10.
    Amini, A.A., Chen, Y., Elayyadi, M., Radeva, P.: Tag surface reconstruction and tracking of myocardial beads from SPAMM-MRI with parametric B-spline surfaces. IEEE Trans. on Medical Imaging 20(2), 94–103 (2001)CrossRefGoogle Scholar
  11. 11.
    Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. Computer Vision and Image Understanding 89(2-3), 114–141 (2003)CrossRefzbMATHGoogle Scholar
  12. 12.
    Müller, M., Keiser, R., Nealen, A., Pauly, M., Gross, M., Alexa, M.: Point based animation of elastic, plastic and melting objects. In: Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation, pp. 141–151 (2004)Google Scholar
  13. 13.
    Axel, L., Chen, T., Manglik, T.: Dense myocardium deformation estimation for 2D tagged MRI. In: Proceedings of FIMH, pp. 446–456 (2005)Google Scholar
  14. 14.
    Chen, T., Axel, L.: Using Gabor filters bank and temporal-spatial constraints to compute 3D myocardium strain. In: Proceedings of EMBC (2006)Google Scholar
  15. 15.
    Schaefer, S., McPhail, T., Warren, J.: Image deformation using moving least squares. In: Proceedings of the 2006 ACM SIGGRAPH pages, pp. 533–540 (2006)Google Scholar
  16. 16.
    Abd-Elmoniem, K.Z., Parthasarathy, V., Prince, J.L.: Improving HARP cardiac strain mapping using nonlinear diffusion. In: Proceedings of ISMRM (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ting Chen
    • 1
  • Xiaoxu Wang
    • 2
  • Dimitris Metaxas
    • 2
  • Leon Axel
    • 1
  1. 1.Radiology DepartmentNew York University Medical CenterNew York CityUSA
  2. 2.Computer Science DepartmentRutgers UniversityPiscatawayUSA

Personalised recommendations