Medical & Biological Engineering & Computing

, Volume 46, Issue 8, pp 799–805 | Cite as

Parametric subject-specific model for in vivo 3D reconstruction using bi-planar X-rays: application to the upper femoral extremity

Original Article
First Online:
Received:
Accepted:

Abstract

Although feasibility of accurate 3D reconstruction of the proximal epiphysis of the femur from biplanar X-rays (frontal and lateral) has been assessed, in vivo application is limited due to bone superposition. The aim of this study was to propose a specific algorithm to get accurate and reproducible, low dose in vivo 3D reconstruction. To achieve this goal, a parametric subject-specific model was introduced as a priori knowledge. This geometric model was based on a database based on proximal epiphysis of 60 femurs. The accuracy was estimated using comparisons to CT scans on 13 cadaveric femurs, then in vivo intra- and inter- observer reproducibility was assessed using a set of 23 femurs. The mean for the relative difference was 0.2 mm for the in vitro 3D accuracy. The mean error was 1.0 mm with maximum value of 5.1 mm in ideal conditions (in vitro). The confidence interval for the inter-observer reproducibility was within ±2.2 mm. This method gave us a reproducible tool in order to get in vivo 3D reconstructions of the femur proximal epiphysis from biplanar X-rays.

Keywords

Biplanar X-rays Femur 3D reconstruction Low dose X-ray Model customization 
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Mahfouz MR et al (2003) A robust method for registration of three-dimensional knee implant models to two-dimensional fluoroscopy images. IEEE Trans Med Imaging 22(12):1561–1574CrossRefGoogle Scholar
  2. 2.
    Sato T, Koga Y, Omori G. (2004) Three-dimensional lower extremity alignment assessment system: application to evaluation of component position after total knee arthroplasty. J Arthroplasty 19(5):620–628CrossRefGoogle Scholar
  3. 3.
    Hagio K et al (2004) A novel system of four-dimensional motion analysis after total hip arthroplasty. J Orthop Res 22(3):665–670CrossRefGoogle Scholar
  4. 4.
    Otake Y et al (2005) Four-dimensional model of the lower extremity after total hip arthroplasty. J Biomech 38(12):2397–2405CrossRefGoogle Scholar
  5. 5.
    Bousson V et al (2006) Volumetric quantitative computed tomography of the proximal femur: relationships linking geometric and densitometric variables to bone strength. Role for compact bone. Osteoporos Int 17(6):855–864CrossRefGoogle Scholar
  6. 6.
    Van Sint Jan S et al (2006) Low-dose computed tomography: a solution for in vivo medical imaging and accurate patient-specific 3D bone modeling? Clin Biomech (Bristol, Avon) 21(9):992–998CrossRefGoogle Scholar
  7. 7.
    Gautsch TL, Johnson EE, Seeger LL (1994) True three dimensional stereographic display of 3D reconstructed CT scans of the pelvis and acetabulum. Clin Orthop Relat Res 305:138–151CrossRefGoogle Scholar
  8. 8.
    Mortele KJ, McTavish J, Ros PR (2002) Current techniques of computed tomography. Helical CT, multidetector CT, and 3D reconstruction. Clin Liver Dis 6(1):29–52CrossRefGoogle Scholar
  9. 9.
    Kalifa G et al (1998) Evaluation of a new low-dose digital x-ray device: first dosimetric and clinical results in children. Pediatr Radiol 28(7):557–561CrossRefGoogle Scholar
  10. 10.
    Dubousset J et al (2005) A new 2D and 3D imaging approach to musculoskeletal physiology and pathology with low-dose radiation and the standing position: the EOS system. Bull Acad Natl Med 189(2):287–297 (discussion 297–300)Google Scholar
  11. 11.
    Fleute M, Lavallee S, Julliard R (1999) Incorporating a statistically based shape model into a system for computer-assisted anterior cruciate ligament surgery. Med Image Anal 3(3):209–222CrossRefGoogle Scholar
  12. 12.
    Laporte S et al (2003) A biplanar reconstruction method based on 2D and 3D contours: application to the distal femur. Comput Methods Biomech Biomed Eng 6(1):1–6CrossRefMathSciNetGoogle Scholar
  13. 13.
    Pomero V et al (2004) Fast accurate stereoradiographic 3D-reconstruction of the spine using a combined geometric and statistic model. Clin Biomech (Bristol, Avon) 19(3):240–247CrossRefGoogle Scholar
  14. 14.
    Benameur S et al (2003) 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. Comput Med Imaging Graph 27(5):321–337CrossRefGoogle Scholar
  15. 15.
    Benameur S et al (2002) 3D biplanar statistical reconstruction of scoliotic vertebrae. Stud Health Technol Inform 91:281–285Google Scholar
  16. 16.
    Mitton D et al (2006) 3D reconstruction of the pelvis from bi-planar radiography. Comput Methods Biomech Biomed Eng 9(1):1–5CrossRefMathSciNetGoogle Scholar
  17. 17.
    Le Bras A et al (2004) 3D reconstruction of the proximal femur with low-dose digital stereoradiography. Comput Aided Surg 9(3):51–57CrossRefGoogle Scholar
  18. 18.
    Pomero V et al. (2003) Procede d’imagerie radiographique pour la reconstruction tridimensionnelle, dispositif et programme d’ordinateur pour mettre en œuvre ce procede Radiographic imaging method for three-dimensional reconstruction, device and computer software for carrying out said methodGoogle Scholar
  19. 19.
    Mitton D et al (2000) 3D reconstruction method from biplanar radiography using non-stereocorresponding points and elastic deformable meshes. Med Biol Eng Comput 38(2):133–139CrossRefGoogle Scholar
  20. 20.
    Mitulescu A et al (2001) Validation of the non-stereo corresponding points stereoradiographic 3D reconstruction technique. Med Biol Eng Comput 39(2):152–158CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2008

Authors and Affiliations

  1. 1.Laboratoire de Biomécanique (UMR CNRS 8005)ENSAM-CNRSParisFrance
  2. 2.Laboratoire de Recherche en Imagerie et Orthopédie de MontréalETS-CRCHUMMontrealCanada

Personalised recommendations