Atlas-Based 3D Intensity Volume Reconstruction from 2D Long Leg Standing X-Rays: Application to Hard and Soft Tissues in Lower Extremity

  • Weimin Yu
  • Guoyan Zheng
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1093)


In this chapter, the reconstruction of 3D intensity volumes of femur, tibia, and three muscles around the thigh region from a pair of calibrated X-ray images is addressed. We present an atlas-based 2D-3D intensity volume reconstruction approach by combining a 2D-2D nonrigid registration-based 3D landmark reconstruction procedure with an adaptive regularization step. More specifically, an atlas derived from the CT acquisition of a healthy lower extremity, together with the input calibrated X-ray images, is used to reconstruct those musculoskeletal structures. To avoid the potential penetration of the reconstructed femoral and tibial volumes that might be caused by reconstruction error, we come up with an articulated 2D-3D reconstruction strategy, which can effectively preserve knee joint structure. Another contribution from our work is the application of the proposed 2D-3D reconstruction pipeline to derive the patient-specific volumes of three thigh muscles around the thigh region.


Atlas Intensity volume 2D-3D reconstruction X-ray Lower extremity Soft tissue 



This chapter was modified from the paper published by our group in the MICCAI 2017 International Workshop on Imaging for Patient-Customized Simulation and Systems for Point-of-Care Ultrasound (Yu and Zheng, BIVPCS/POCUS@MICCAI2017: 35-43). The related contents were reused with their permission.


  1. 1.
    Markelj P, Tomaževič D, Likar B, Pernuš F (2012) A review of 3D/2D registration methods for image-guided interventions. Med Image Anal 16:642–661CrossRefPubMedCentralGoogle Scholar
  2. 2.
    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 13:883–899CrossRefPubMedCentralGoogle Scholar
  3. 3.
    Baka N, Kaptein BL, de Bruijne M, van Walsum T, Giphart JE, Niessen WJ, Lelieveldt BP (2011) 2D-3D reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med Image Anal 15:840–850CrossRefPubMedCentralGoogle Scholar
  4. 4.
    Yao J, Taylor RH (2003) Assessing accuracy factors in deformable 2D/3D medical image registration using a statistical pelvis model. In: ICCV 2003, pp 1329–1334Google Scholar
  5. 5.
    Ahmad O, Ramamurthi K, Wilson KE, Engelke K, Prince RL, Taylor RH (2010) Volumetric DXA (VXA) – a new method to extract 3D information from multiple in vivo DXA images. J Bone Miner Res 25: 2468–2475CrossRefGoogle Scholar
  6. 6.
    Zheng G (2011) Personalized X-ray reconstruction of the proximal femur via intensity-based non-rigid 2D-3D registration. In: MICCAI 2011, pp 598–606Google Scholar
  7. 7.
    Yu W, Chu C, Tannast M, Zheng G (2016) Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images. Int J Comput Assist Radiol Surg 11(9):1673–1685CrossRefPubMedCentralGoogle Scholar
  8. 8.
    Klein S, Staring M, Murphy K, Viergever MA, Pluim JP (2010) Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imag 29(1):196–205CrossRefGoogle Scholar
  9. 9.
    Myronenko A, Song X (2009) Adaptive regularization of ill-posed problems: application to non-rigid image registration. arXiv:0906.3323Google Scholar
  10. 10.
    Strang G (1999) The discrete cosine transform. SIAM Rev 41(1):135–147CrossRefGoogle Scholar
  11. 11.
    Chu C, Takao M, Ogawa T, Yokota F, Sato Y, Zheng G (2016) Statistical shape modeling of compound musculoskeletal structures around the thigh region. In: ISBI 2016, pp 885–888Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Institute for Surgical Technology and BiomechanicsUniversity of BernBernSwitzerland

Personalised recommendations