3D Imaging from Video and Planar Radiography

  • Julien Pansiot
  • Edmond Boyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)


In this paper we consider dense volumetric modeling of moving samples such as body parts. Most dense modeling methods consider samples observed with a moving X-ray device and cannot easily handle moving samples. We propose a novel method that uses a surface motion capture system associated to a single low-cost/low-dose planar X-ray imaging device for dense in-depth attenuation information. Our key contribution is to rely on Bayesian inference to solve for a dense attenuation volume given planar radioscopic images of a moving sample. The approach enables multiple sources of noise to be considered and takes advantage of limited prior information to solve an otherwise ill-posed problem. Results show that the proposed strategy is able to reconstruct dense volumetric attenuation models from a very limited number of radiographic views over time on simulated and in-vivo data.


Optical Flow Attenuation Model Warping Function Attenuation Volume Voxel Grid 
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.



This research was partly funded by the KINOVIS project (ANR-11-EQPX-0024).


  1. 1.
    Bang, T.Q., Jeon, I.: CT reconstruction from a limited number of X-ray projections. World Acad. Sci. Eng. Technol. 5(10), 488–490 (2011)Google Scholar
  2. 2.
    Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. Int. J. Comput. Vis. 61(3), 211–231 (2005)CrossRefGoogle Scholar
  3. 3.
    Dawood, M., Lang, N., Jiang, X., Schafers, K.: Lung motion correction on respiratory gated 3-D PET/CT images. TMI 25(4), 476–485 (2006)Google Scholar
  4. 4.
    Feldkamp, L.A., Davis, L.C., Kress, J.W.: Practical cone-beam algorithm. J. Opt. Soc. Am. (JOSA) A 1(6), 612–619 (1984)CrossRefGoogle Scholar
  5. 5.
    Hutton, B.F., Kyme, A.Z., Lau, Y.H., Skerrett, D.W., Fulton, R.R.: A hybrid 3-D reconstruction/registration algorithm for correction of head motion in emission tomography. IEEE Trans. Nucl. Sci. 49(1), 188–194 (2002)CrossRefGoogle Scholar
  6. 6.
    Liu, C., Sun, D.: On Bayesian adaptive video super resolution. TPAMI 36(2), 346–360 (2014)CrossRefGoogle Scholar
  7. 7.
    McNamara, J.E., Pretorius, P.H., Johnson, K., Mukherjee, J.M., Dey, J., Gennert, M.A., King, M.A.: A flexible multicamera visual-tracking system for detecting and correcting motion-induced artifacts in cardiac SPECT slices. Med. Phys. 36(5), 1913–1923 (2009)CrossRefGoogle Scholar
  8. 8.
    Pansiot, J., Reveret, L., Boyer, E.: Combined visible and X-ray 3D imaging. In: MIUA, London, pp. 13–18, July 2014Google Scholar
  9. 9.
    Sidky, E.Y., Kao, C.M., Pan, X.: Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT. J. X-Ray Sci. Technol. 14(2), 119–139 (2006)Google Scholar
  10. 10.
    Yang, G., Hipwell, J.H., Hawkes, D.J., Arridge, S.R.: A nonlinear least squares method for solving the joint reconstruction and registration problem in digital breast tomosynthesis. In: MIUA, pp. 87–92 (2012)Google Scholar
  11. 11.
    Zhang, Q., Hu, Y.C., Liu, F., Goodman, K., Rosenzweig, K.E., Goodman, K., Mageras, G.S.: Correction of motion artifacts in cone-beam CT using a patient-specific respiratory motion model. Med. Phys. 37(6), 2901–2909 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.LJK, Inria GrenobleGrenobleFrance

Personalised recommendations