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Modeling and Measurement of 3D Deformation of Scoliotic Spine Using 2D X-ray Images

  • Hao Li
  • Wee Kheng Leow
  • Chao-Hui Huang
  • Tet Sen Howe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5702)

Abstract

Scoliosis causes deformations such as twisting and lateral bending of the spine. To correct scoliotic deformation, the extents of 3D spinal deformation need to be measured. This paper studies the modeling and measurement of scoliotic spine based on 3D curve model. Through modeling the spine as a 3D Cosserat rod, the 3D structure of a scoliotic spine can be recovered by obtaining the minimum potential energy registration of the rod to the scoliotic spine in the x-ray image. Test results show that it is possible to obtain accurate 3D reconstruction using only the landmarks in a single view, provided that appropriate boundary conditions and elastic properties are included as constraints.

Keywords

Scoliosis spine 3D reconstruction modeling and measurement deformation Cosserat rod 

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References

  1. 1.
    Bernhardt, M., Bridwell, K.H.: Segmental analysis of the sagittal plane alignment of the normal thoracic and lumbar spines and thoracolumbar junction. In: 23rd Annual Meeting of the Scoliosis Research Society (1988)Google Scholar
  2. 2.
    Panjabi, M., White, A.A.: A mathematical approach for three-dimensional analysis of the mechanics of the spine. Journal of Biomechanics 4, 203–211 (1971)CrossRefGoogle Scholar
  3. 3.
    Dumas, R., Aissaoui, R., Mitton, D., de Guise, J.: Personalized body segment parameters from biplanar low-dose radiography. IEEE Trans. Biomed. Eng. 52(10), 1756–1763 (2005)CrossRefGoogle Scholar
  4. 4.
    Lam, G.C., Hill, D.L., Le, L.H., Raso, J.V., Lou, E.H.: Vertebral rotation measurement: A summary and comparison of common radiographic and CT methods. Scoliosis 3, 16–25 (2008)CrossRefGoogle Scholar
  5. 5.
    Lin, H., Sucato, D.: Identification of Lenke spine deformity classification by simplified 3D spine model. In: Proc. Int. Conf. IEEE/EMBS, pp. 3144–3146 (2004)Google Scholar
  6. 6.
    Lin, H.: Identification of spinal deformity classification with total curvature analysis and artificial neural network. IEEE Trans. Biomed. Eng. 55(1), 376–382 (2008)CrossRefGoogle Scholar
  7. 7.
    Verdonck, B., Nijlunsing, R., Gerritsen, F.A., Cheung, J., Wever, D.J., Veldhuizen, A., Devillers, S., Makram-Ebeid, S.: Computer assisted quantitative analysis of deformities of the human spine. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 822–831. Springer, Heidelberg (1998)Google Scholar
  8. 8.
    Benameur, S., Mignotte, M., Parent, S., Labelle, H., Skalli, W., de Cuise, J.: 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. Computerized Medical Imaging and Graphics 27, 321–337 (2003)CrossRefGoogle Scholar
  9. 9.
    Benameur, S., Mignotte, M., Labelle, H., Guise, J.A.D.: A hierarchical statistical modeling approach for the unsupervised 3-D biplanar reconstruction of the scoliotic spine. IEEE Trans. Biomed. Eng. 52(12), 2041–2057 (2005)CrossRefGoogle Scholar
  10. 10.
    Novosad, J., Cheriet, F., Petit, Y., Labelle, H.: Three-dimensional (3-D) reconstruction of the spine from a single x-ray image and prior vertebra models. IEEE Trans. Biomed. Eng. 51(9), 1628–1639 (2004)CrossRefGoogle Scholar
  11. 11.
    Lenke, L.G.: Adolescent idiopathic scoliosis. The Journal of Bone and Joint Surgery 83, 1169–1181 (2001)Google Scholar
  12. 12.
    King, H.A., Moe, J.H., Bradford, D.S.: The selection of fusion levels in thoracic idiopathic scoliosis. The Journal of Bone and Joint Surgery 65, 1302–1313 (1983)Google Scholar
  13. 13.
    Kauffmann, C., de Guise, J.A.: Digital radiography segmentation of scoliotic vertebral body using deformable models. In: Proc. SPIE (1997)Google Scholar
  14. 14.
    Benameur, S., Mignotte, M., Parent, S., Labelle, H., Skalli, W., de Guise, J.A.: 3D biplanar reconstruction of scoliotic vertebrae using statistical models. In: Proc. CVPR, pp. 577–582 (2001)Google Scholar
  15. 15.
    Mitulescu, A., Semaan, I., Guise, J.A.D., Leborgne, P., Adamsbaum, C., Skalli, W.: Validation of the non-stereo corresponding points stereoradiographic 3d reconstruction technique. Med. Biol. Eng. Comput. 39, 152–158 (2001)CrossRefGoogle Scholar
  16. 16.
    Antman, S.S.: Nonlinear Problems of Elasticity. Springer, Heidelberg (1995)zbMATHGoogle Scholar
  17. 17.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C++: The Art of Scientific Computing. Cambridge University Press, Cambridge (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hao Li
    • 1
  • Wee Kheng Leow
    • 1
  • Chao-Hui Huang
    • 1
  • Tet Sen Howe
    • 2
  1. 1.Dept. of Computer ScienceNational University of SingaporeSingapore
  2. 2.Dept. of OrthopaedicsSingapore General HospitalSingapore

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