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Dense 2D displacement reconstruction from SPAMM-MRI with constrained elastic splines: Implementation and validation

  • Amir A. Amini
  • Yasheng Chen
  • Jean Sun
  • Vaidy Mani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

Efficient constrained thin-plate spline warps are proposed in this paper which can warp an area in the plane such that two embedded snake grids obtained from two SPAMM frames are brought into registration, interpolating a dense displacement vector field. The reconstructed vector field adheres to the known displacement information at the intersections, forces corresponding snakes to be warped into one another, and for all other points in the myocardium, where no information is available, a C1 continuous vector field is interpolated. The formalism proposed in this paper improves on our previous variational-based implementation and generalizes warp methods to include biologically relevant contiguous open curves, in addition to standard landmark points. The method has been extensively validated with a cardiac motion simulator, in addition to in-vivo tagging data sets.

Keywords

Vector Field Conjugate Gradient Algorithm Length Error Dense Deformation Displacement Vector Field 
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.

References

  1. 1.
    A. A. Amini. Automated methods for cardiac motion analysis from MR tagging. Proposal funded by The Whitaker Foundation, 1992.Google Scholar
  2. 2.
    A. A. Amini, R. W. Curwen, and John C. Gore. Snakes and splines for tracking non-rigid heart motion. In European Conference on Computer Vision, pages 251–261, University of Cambridge, UK, April 1996.Google Scholar
  3. 3.
    A. A. Amini and et al. MR physics-based snake tracking and dense deformations from tagged MR cardiac images (oral presentation). In AAAI Symposium on Applications of Computer Vision to Medical Image Processing, Stanford University, Stanford, California, March 1994.Google Scholar
  4. 4.
    T. Arts, W. Hunter, A. Douglas, A. Muijtjens, and R. Reneman. Description of the deformation of the left ventricle by a kinematic model. J. Biomechanics, 25(10):1119–1127, 1992.CrossRefGoogle Scholar
  5. 5.
    L. Axel and L. Dougherty. MR imaging of motion with spatial modulation of magnetization. Radiology, 171(3):841–845, 1989.CrossRefPubMedGoogle Scholar
  6. 6.
    F. Bookstein. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-11:567–585, 1989.CrossRefGoogle Scholar
  7. 7.
    T. Denney and J. Prince. Reconstruction of 3-d left ventricular motion from planar tagged cardiac MR images: An estimation-theoretic approach. IEEE Transactions on Medical Imaging, 14(4):625–635, December 1995.CrossRefPubMedGoogle Scholar
  8. 8.
    W. Grimson. An implementation of a computational theory of visual surface interpolation. Computer Vision, Graphics, and Image Processing, 22:39–69, 1983.CrossRefGoogle Scholar
  9. 9.
    J. Park, D. Metaxas, and L. Axel. Volumetric deformable models with parameter functions: A new approach to the 3d motion analysis of the LV from MRI-SPAMM. In International Conference on Computer Vision, pages 700–705, 1995.Google Scholar
  10. 10.
    W. Press, B. Flannery, S. Teukolsky, and W. Vetterling. Numerical recipes in C. Cambridge University Press, Cambridge, 1988.Google Scholar
  11. 11.
    P. Radeva, A. Amini, and J. Huang. Deformable B-Solids and implicit snakes for 3d localization and tracking of SPAMM MRI data. Computer Vision and Image Understanding, 66(2):163–178, May 1997.CrossRefGoogle Scholar
  12. 12.
    D. Terzopoulos. Multiresolution Computation of Visible Representation. PhD thesis, MIT, 1984.Google Scholar
  13. 13.
    E. Waks, J. Prince, and A. Douglas. Cardiac motion simulator for tagged MRI. In Proc. of Mathematical Methods in Biomedical Image Analysis, pages 182–191, 1996.Google Scholar
  14. 14.
    A. Young, D. Kraitchman, L. Dougherty, and L. Axel. Tracking and finite element analysis of stripe deformation in magnetic resonance tagging. IEEE Transactions on Medical Imaging, 14(3):413–421, September 1995.CrossRefPubMedGoogle Scholar
  15. 15.
    E. Zerhouni, D. Parish, W. Rogers, A. Yang, and E. Shapiro. Human heart: Tagging with MR imaging — a method for noninvasive assessment of myocardial motion. Radiology, 169:59–63, 1988.CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Amir A. Amini
    • 1
  • Yasheng Chen
    • 1
  • Jean Sun
    • 1
  • Vaidy Mani
    • 2
  1. 1.CVIA LabWashington University Medical CenterSt. Louis
  2. 2.Iterated Systems, Inc.AtlantaGeorgia

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