3D femur model reconstruction from biplane X-ray images: a novel method based on Laplacian surface deformation
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.
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.
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.
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.
Keywords3D reconstruction X-ray Laplacian mesh deformation Self-organizing maps Medical modeling software
- 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.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.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
- 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
- 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.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.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.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.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.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.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
- 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.Masuda H, Yoshioka Y, Furukawa Y (2007) Preserving form features in interactive mesh deformation. Comput Aided Design 39(5):361–368 Google Scholar
- 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