Personalized X-Ray Reconstruction of the Proximal Femur via Intensity-Based Non-rigid 2D-3D Registration

  • Guoyan Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6892)


This paper presents a new approach for reconstructing a patient-specific shape model and internal relative intensity distribution of the proximal femur from a limited number (e.g., 2) of calibrated C-arm images or X-ray radiographs. Our approach uses independent shape and appearance models that are learned from a set of training data to encode the a priori information about the proximal femur. An intensity-based non-rigid 2D-3D registration algorithm is then proposed to deformably fit the learned models to the input images. The fitting is conducted iteratively by minimizing the dissimilarity between the input images and the associated digitally reconstructed radiographs of the learned models together with regularization terms encoding the strain energy of the forward deformation and the smoothness of the inverse deformation. Comprehensive experiments conducted on images of cadaveric femurs and on clinical datasets demonstrate the efficacy of the present approach.


Input Image Proximal Femur Appearance Model Reference Volume Active Appearance Model 
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.


  1. 1.
    Fleute, M., Lavallée, S.: Nonrigid 3-D/2-D registration of images using statistical models. In: Taylor, C.J., Colchester, A.C.F. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 138–147. Springer, Heidelberg (1999)Google Scholar
  2. 2.
    Zheng, G., Gollmer, S., Schumann, S., Dong, X., Feilkas, F., Gonzalez Ballester, M.A.: A 2D/3D correspondence building method for reconstruction of a patient-specific 3D bone surface model using point distribution models and calibrated X-ray images. Med. Image Anal. 13, 883–899 (2009)CrossRefGoogle Scholar
  3. 3.
    Baka, N., Niessen, W.J., Kaptein, B.L., van Walsum, T., Ferrarini, L., Reiber, J.H.C., Lelieveldt, B.P.F.: Correspondence free 3D statistical shape model fitting to sparse X-ray projections. In: Dawant, B.M., Haynor, D.R. (eds.) SPIE Medical Imaging 2010, vol. 7623, pp. 76230D-1–76230D9 (2010)Google Scholar
  4. 4.
    Sadowsky, O., Chintalapani, G., Taylor, R.H.: Deformable 2D-3D registration of the pelvis with a limited field of view, using shape statistics. In: Ayache, N., Ourselin, S., Maeder, A.J. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 519–526. Springer, Heidelberg (2007)Google Scholar
  5. 5.
    Hurvitz, A., Joskowicz, L.: Registration of a CT-like atlas to fluoroscopic X-ray images using intensity correspondences. Int. J. CARS 3, 493–504 (2008)CrossRefGoogle Scholar
  6. 6.
    Chintalapani, G., Taylor, R.H.: Integrating statistical models of bone density into shape based 2D-3D registration framework. In: PMMIA 2009, pp. 1–11 (2009)Google Scholar
  7. 7.
    Humbert, L., Whitmarsh, T., De Craene, M., del Rio Barquero, L.M., Fritscher, K.D., Schubert, R., Eckstein, F., Link, T.M., Frangi, A.F.: 3D reconstruction of bone shape and bone mineral density distribution of the femur from DXA images. In: ISBI 2010, pp. 456–459. IEEE, Los Alamitos (2010)Google Scholar
  8. 8.
    Ahmad, O., Ramamurthi, K., Wilson, K.E., Engelke, K., Prince, R.L., Taylor, R.H.: Volumetric DXA (VXA) - A new method to extract 3D information from multiple in vivo DXA images. J. Bone Miner. Res. 25, 2468–2475 (2010)CrossRefGoogle Scholar
  9. 9.
    Sadowsky, O., Lee, J., Sutter, E.G., Wall, S.J., Prince, J.L., Taylor, R.H.: Hybrid cone-beam tomographic reconstruction: incorporation of prior anatomical models to compensate for missing data. IEEE T. Med. Imaging 30, 69–83 (2011)CrossRefGoogle Scholar
  10. 10.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE T. Pattern Anal. 23, 681–685 (2001)CrossRefGoogle Scholar
  11. 11.
    Matthews, I., Baker, S.: Active appearance models revisited. Int. J. Comput. Vision 60, 135–164 (2004)CrossRefGoogle Scholar
  12. 12.
    Rueckert, D., Frangi, A.F., Schnabel, J.A.: Automatic construction of 3D statistical deformation models using non-rigid registration. In: Niessen, W., Viergever, M. (eds.) MICCAI 2001. LNCS, vol. 2208, p. 77–84. Springer, Heidelberg (2001)Google Scholar
  13. 13.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Non-parametric diffeomorphic image registration with the demons algorithm. In: Ayache, N., Ourselin, S., Maeder, A.J. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 319–326. Springer, Heidelberg (2007)Google Scholar
  14. 14.
    Chen, M., Lu, W., Chen, Q., Ruchala, K.J.: A simple fixed-point approach to invert a deformation field. Med. Phys. 35, 81–88 (2008)CrossRefGoogle Scholar
  15. 15.
    Cootes, T.F., Taylor, C.J.: Combining point distribution models with shape models based on finite element analysis. Image Vis. Computing 13, 403–409 (1995)CrossRefGoogle Scholar
  16. 16.
    Zheng, G.: Effective incorporating spatial information in a mutual information based 3D-2D registration of a CT volume to X-ray images. Comput. Med. Imag. Grap. 34, 553–562 (2010)CrossRefGoogle Scholar
  17. 17.
    Aspert, N., Santa-Cruz, D., Ebrahimi, T.: Mesh: measuring error between surfaces using the hausdorff distance. In: ICME 2002, pp. 705–708. IEEE, Los Alamitos (2002)Google Scholar
  18. 18.
    Nielsen, M., Florack, L., Deriche, R.: Regularization, scale-space, and edge detection filters. J. Math. Imaging Vis. 7, 291–307 (1997) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Guoyan Zheng
    • 1
  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland

Personalised recommendations