3D femur model reconstruction from biplane X-ray images: a novel method based on Laplacian surface deformation

Original Article

Abstract

Purpose 

   Conventional methods for 3D bone model reconstruction from CT scans can require high-radiation dose, cost and time. A 3D model generated from 2D X-ray images may be a useful alternative. Reconfiguring a 3D template surface mesh model to match bone shape in orthogonal radiographs is a common technique for 3D reconstruction. A computationally efficient 3D bone modeling algorithm was developed and tested.

Method 

   An algorithm for bone template reconfiguration is proposed, which uses Kohonen self-organizing maps for 2D–3D correspondence between input X-ray images and the template. Laplacian surface deformation is then used for final deformation of the template. In the literature, Laplacian deformation has been shown to perform better than thin-plate splines and free form deformation in terms of computation time and mesh quality. The method was applied to 22 sets of simulated input contours generated from 3D models of the distal femur.

Results 

   An acceptable range of reconstruction error: 1.5 mm of RMS-P2S (root-mean-square point-to-surface) distance and 1.2 mm mean-P2S distance errors was observed based on comparison with the corresponding reference models/ground truth. Computation time for the 3D bone modeling algorithm was less than a minute for each case.

Conclusion 

   The new template reconfiguration algorithm based on Laplacian surface deformation provided acceptable reconstruction accuracy and high computation efficiency for 3D modeling of the distal femur using biplane radiographs. This algorithm may provide a useful option for orthopedic modeling applications.

Keywords

3D reconstruction X-ray Laplacian mesh deformation Self-organizing maps Medical modeling software 

References

  1. 1.
    Otomaru I, Nakamoto M, Kagiyama Y, Takao M, Sugano N, Tomiyama N, Tada Y, Sato Y (2012) Automated preoperative planning of femoral stem in total hip arthroplasty from 3D CT data: atlas-based approach and comparative study. Med Image Anal 16(2):415–426CrossRefPubMedGoogle Scholar
  2. 2.
    Ellis RE, Tso CY, Rudan JF, Harrison MM (1999) A surgical planning and guidance system for high tibial osteotomy. J Comput Aided Surg 4(5):264–274CrossRefGoogle Scholar
  3. 3.
    Kim YH, Kim JK, Choi C (2004) Three-dimensional reconstruction of human femur using consecutive computer tomography images and simulated implantation system. J Med Eng Technol 28(5):205–210CrossRefPubMedGoogle Scholar
  4. 4.
    Zhang B, Sun S, Sun J, Chi Z, Xi C (2010) 3D reconstruction method from biplanar radiography using dlt algorithm: application to the femur. In: Proceedings of 1st international conference on pervasive computing signal processing and applications (PCSPA 2010); 2010 Sep 17–19; Harbin, China, pp 251–254Google Scholar
  5. 5.
    Caponetti L, Fanelli AM (1990) 3D Bone reconstruction from two X-Ray views. In: Proceedings of twelfth annual international conference of the IEEE engineering in medicine and biology society (EMBS 1990); 1990 Nov 1–4; Philadelphia, PA, USA, pp 208–210Google Scholar
  6. 6.
    Fuente M, Schkommodau E, Lutz P, Neuss M, Wirtz DC, Radermacher K (2005) 3D reconstruction and navigated removal of femoral bone cement in revision THR based on few fluoroscopic images. In: Proceedings of computer assisted radiology and surgery (CARS 2004); 2005 June 23–26; Chicago, USA, pp 626–631Google Scholar
  7. 7.
    Zheng G, Gollmer S, Schumann S, Dong X, Feilkas T, González Ballester MA (2009) 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(6):883–99CrossRefPubMedGoogle Scholar
  8. 8.
    Baka N, Kaptein BL, de Bruijne M, van Walsum T, Giphart JE, Niessen WJ, Lelieveldt BP (2011) 2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med Image Anal 15(6):840–850CrossRefPubMedGoogle Scholar
  9. 9.
    Tang T, Ellis R (2005) 2D/3D deformable registration using a hybrid atlas. In: Proceedings of medical image computing and computer-assisted intervention (MICCAI 2005); 2005 Oct 26–29; Palm Springs, CA, USA. Springer, Berlin, pp 223–230Google Scholar
  10. 10.
    Benameur S, Mignotte M, Parent S, Labelle H, Skalli W, de Guise J (2003) 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. Comput Med Imaging Graph 27(5):321–337CrossRefPubMedGoogle Scholar
  11. 11.
    Zhu Z, Li G (2011) Construction of 3D human distal femoral surface models using a 3D statistical deformable model. J Biomech 44(13):2368–2362Google Scholar
  12. 12.
    Fleute M, Lavallée S (1999) Nonrigid 3-D/2-D registration of images using statistical models. In: Proceedings of the second international conference on medical image computing and computer-assisted intervention (MICCAI ’99), 1999, pp 138–147Google Scholar
  13. 13.
    Hraiech N, Boichon C, Rochette M, Marchal T, Horner M (2010) Statistical shape modeling of femurs using morphing and principal component analysis. J Med Devices 4(2):027534–027534Google Scholar
  14. 14.
    Bredbenner TL, Eliason TD, Potter RS, Mason RL, Havill LM, Nicolella DP (2010) Statistical shape modeling describes variation in tibia and femur surface geometry between Control and Incidence groups from the osteoarthritis initiative database. J Biomech 43(9):1780–1786CrossRefPubMedCentralPubMedGoogle Scholar
  15. 15.
    Ehlke M, Ramm H, Lamecker H, Hege HC, Zachow S (2013) Fast generation of virtual X-ray images for reconstruction of 3D anatomy. IEEE Trans Vis Comp Graph 19(12):2673–2682Google Scholar
  16. 16.
    Laporte S, Skalli W, de Guise JA, Lavaste F, Mitton D (2003) A biplanar reconstruction method based on 2D and 3D contours: application to the distal femur. Comput Methods Biomech Biomed Eng 6(1):1–6Google Scholar
  17. 17.
    Le Bras A, Laporte S, Bousson V, Mitton D, De Guise JA, Laredo JD, Skalli W (2004) 3D reconstruction of the proximal femur with low-dose digital stereoradiography. Comput Aided Surg 9(3):51–57Google Scholar
  18. 18.
    Gunay M, Shim MB, Shimada K (2007) Cost- and time-effective three-dimensional bone-shape reconstruction from X-ray images. Int J Med Robot 3(4):323–335CrossRefPubMedGoogle Scholar
  19. 19.
    Lee MK, Lee SH, Kim A, Youn I, Lee TS, Hur N, Choi K (2008) The study of femoral 3D reconstruction process based on anatomical parameters using a numerical method. J Biomech Sci Eng 3(3):443–451CrossRefGoogle Scholar
  20. 20.
    Filippi S, Motyl B, Bandera C (2008) Analysis of existing methods for 3D modeling of femurs starting from two orthogonal images and development of a script for a commercial software package. Comput Methods Prog Biomed 89(1):76–82CrossRefGoogle Scholar
  21. 21.
    Koh K, Kim YH, Kim K, Park WM (2011) Reconstruction of patient-specific femurs using X-ray and sparse CT images. Comput Biol Med 41(7):421–426CrossRefPubMedGoogle Scholar
  22. 22.
    Feng J, Shao J, Jin X, Peng Q, Forrest AR (2006) Multiresolution free-form deformation with subdivision surface of arbitrary topology. Visual Comput 22(1):28–42CrossRefGoogle Scholar
  23. 23.
    Gamage P, Xie, SQ, Delmas, P.; Xu, P (2009) 3D reconstruction of patient specific bone models from 2D radiographs for image guided orthopedic surgery. In: Proceedings of international conference on digital image computing: techniques and applications (DICTA 2009), 2009 Dec 1–3; Melbourne, VIC, pp 212–216Google Scholar
  24. 24.
    Masuda H, Yoshioka Y, Furukawa Y (2007) Preserving form features in interactive mesh deformation. Comput Aided Design 39(5):361–368 Google Scholar
  25. 25.
    Zhang S, Huang J, Metaxax D (2011) Robust mesh editing using Laplacian coordinates. Graph Model 73(1):10–19CrossRefGoogle Scholar
  26. 26.
    Zhang S, Xiaoxu W, Metaxas D, Ting C, Axel L (2009) LV surface reconstruction from sparse TMRI using Laplacian surface deformation and optimization. In Proceedings of international symposium of biomedical imaging: from nano to macro (ISBI 2009) 2009 Jun 28–Jul 1, Boston, pp 698–701Google Scholar
  27. 27.
    Heimann T, Meinzer HP (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13(4):543–563CrossRefPubMedGoogle Scholar
  28. 28.
    Kohonen T (1982) Self-organised formation of topologically correct feature maps. Biol Cybern 43:59–69CrossRefGoogle Scholar
  29. 29.
    Ferrarini L, Olofsen H, Palm WM, van Buchem MA, Reiber JH, Admiraal-Behloul F (2007) GAMEs: growing and adaptive meshes for fully automatic shape modeling and analysis. Med Image Anal 11(3):302–314CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2014

Authors and Affiliations

  1. 1.OrthoCAD Lab, Department of Mechanical EngineeringIndian Institute of Technology BombayPowai, MumbaiIndia

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