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Deformation for image guided interventions using a three component tissue model

Part of the Lecture Notes in Computer Science book series (LNCS,volume 1230)

Abstract

In image guided neurosurgery it is necessary to align preoperative image data with the patient. The rigid body approximation is usually applied, but is often not valid due to tissue deformation. Most non-rigid registration algorithms, such as those used for atlas matching, provide a smooth deformation, which does not model the characteristics of different tissues accurately since, for example, bone will appear to deform. We suggest that a physically based model of tissue could provide a powerful tool for tracking tissue movement. Since the algorithm must ultimately run in real time, we have developed a simplified model of tissue deformation based on a three component system. Regions are labelled as either rigid, deformable or fluid. A novel strategy to avoid folding in the transformation is described. Our model was applied to MRI and CT data from a neurosurgery patient with epilepsy. The test data is limited and the current implementation is in 2D, but initial results are promising.

Keywords

  • Thin Plate Spline
  • Fluid Region
  • Tissue Deformation
  • Normal Pressure Hydrocephalus
  • Rigid Registration

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.

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References

  1. A. C. Evans, S. Marrett, J. Torrescorzo, S. Ku, and L. Collins. MRI-PET correlation in three dimensions using a volume-of-interest (VOI) atlas. J. Cereb. Blood Flow Metab., 11:A69–A78, 1991.

    PubMed  Google Scholar 

  2. K. J. Friston, C. D. Frith, P. F. Liddle, and R. S. J. Frackowiak. Plastic transformation of PET images. J. Comput. Assist. Tomogr., 15:635–639, 1991.

    Google Scholar 

  3. J. Talairach and P. Tournoux. A coplanar stereotaxic atlas of a human brain. Thieme, Stuttgart, 1988.

    Google Scholar 

  4. R. Bajcy and S. Kovacic. Multiresolution elastic matching. CVGIP: Graphical Models and Image Processing, 46:1–21, 1989.

    Google Scholar 

  5. C. A. Christensen, R. D. Rabbitt, M. I. Miller, S. C. Joshi, U. Grenader, A. Coogan, and D. C. Van Essen. Topological properties of smooth anatomic maps. In Y. Bizais, C. Barillot, and R. Di Paola, editors, Information Processing in Medical Imaging, pages 101–112. Kluwer Academic Publishers, 1995.

    Google Scholar 

  6. F. L. Bookstein. Thin-plate splines and the atlas problem for biomedical images. In A. C. F. Colchester and D. J. Hawkes, editors, Information Processing in Medical Imaging, 1991.

    Google Scholar 

  7. D. L. Collins, A. C. Evans, C. Holmes, and T. M. Peters. Automatic 3d segmentation of neuro-anatomical structures. In Y. Bizais, C. Barillot, and R. Di Paola, editors, Information Processing in Medical Imaging, pages 139–152. Kluwer Academic Publishers, 1995.

    Google Scholar 

  8. K. V. Mardia and J. A. Little. Image warping using derivative information. In F. L. Bookstein, J. S. Duncan, N. Lange, and D. C. Wilson, editors, Mathematical Methods in Medical Imaging III, pages 16–31. SPIE Proceedings 2099, July 1994.

    Google Scholar 

  9. M. Moshfeghi. Elastic matching of multimodality medical images. CVGIP: Graphical Models and Image Processing, 53(3):271–282, 1991.

    Google Scholar 

  10. K. Waters. A physical model of facial tissue and muscle articulation derived from computer tomography data. In SPIE 1808 Visualization in Biomedical Computing, pages 574–583, 1992.

    Google Scholar 

  11. D Terzopoulos and K. Waters. Analysis and synthesis of facial image sequences using physical and anatomical models. IEEE Trans. PAMI, 15(6):569–579, 1993.

    Google Scholar 

  12. P. J. Edwards, D. L. G. Hill, J. Little, V. A. S. Sahni, and D. J. Hawkes. Medical image registration incorporating deformations. In D. Pycock, editor, Proc. 6th Brit. Machine Vision Conf., volume 2, pages 691–699, 1995.

    Google Scholar 

  13. J. Little, D. L. G. Hill, and D. J. Hawkes. Deformations incorporating rigid structures. In Proc. Mathematical Methods in Biomedical Image Analysis, pages 104–113. IEEE Computer Society Press, 1995.

    Google Scholar 

  14. D. L. G. Hill, C. R. Maurer, M. Y. Wang, R. J. Maciunas, and J. M. Barwise, J. A. Fitzpatrick. Estimation of intraoperative brain surface movement. In Proc. MRCAS/CVRMed, in press, 1997.

    Google Scholar 

  15. M. Kass, A. Witkin, and D. Terzopoulos. Regularization of inverse visual problems involving discontinuities. Int. J. Computer Vision, 8(4):321331, 1986.

    Google Scholar 

  16. A. K. F. Lui and D. J. Bone. Integrating graphical editing into sparse data interpolation using non-uniform thin plate splines. In Proc. 1st Int. Conf. on Visual Information Systems, pages 541–550. Melbourne, Australia, 1995.

    Google Scholar 

  17. D. Terzopoulos. Regularization of inverse visual problems involving discontinuities. IEEE trans. Pattern Analysis and Machine Intelligence, 8(4):413–424, 1986.

    Google Scholar 

  18. S. Hakim, J. G. Venegas, and J.D. Burton. The phisics of the cranial cavity, hydrocephalus and normal pressure hydrocephalus. Surgical Neurology, 5:187–210, 1976.

    PubMed  Google Scholar 

  19. W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery. Numerical Recipes in C, 2nd Edition. Cambridge University Press, 1992.

    Google Scholar 

  20. C Studholme, D.L.G. Hill, and D.J. Hawkes. Automated 3-D registration of MR and CT images of the head. Medical Image Analysis, 1(2), 1996.

    Google Scholar 

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© 1997 Springer-Verlag Berlin Heidelberg

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Edwards, P.J., Hill, D.L.G., Little, J.A., Hawkes, D.J. (1997). Deformation for image guided interventions using a three component tissue model. In: Duncan, J., Gindi, G. (eds) Information Processing in Medical Imaging. IPMI 1997. Lecture Notes in Computer Science, vol 1230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63046-5_17

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  • DOI: https://doi.org/10.1007/3-540-63046-5_17

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