Isocenter Determination from Projection Matrices of a C-Arm CBCT

  • Ahmed AmriEmail author
  • Bastian Bier
  • Jennifer Maier
  • Andreas Maier
Conference paper
Part of the Informatik aktuell book series (INFORMAT)


An accurate position of the isocenter of a cone-beam CT trajectory is mandatory for accurate image reconstruction. For analytical backprojection algorithms, it is assumed that the X-ray source moves on a perfectly circular trajectory, which is not true for most practical clinical trajectories due to mechanical instabilities. Besides, the exibility of novel robotic C-arm systems enables new trajectories where the computation of the isocenter might not be straight forward. An inaccurate isocenter position directly affects the computation of the redundancy weights and consequently affects the reconstructions immediately. In this work, we compare different methods for computing the isocenter of a non-ideal circular scan trajectory and evaluate their robustness in the presence of noise. The best results were achieved using a method based on a least-square-based fit. Furthermore, we show that an inaccurate isocenter computation can lead to artifacts in the reconstruction result. Therefore, this work highlights the importance of an accurate isocenter computation with the background of novel upcoming clinical trajectories.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Maier A, Choi JH, Keil A, et al.; International Society for Optics; Photonics. Analysis of vertical and horizontal circular C-arm trajectories. Phys Med Imaging. 2011;7961:796123.Google Scholar
  2. 2.
    Jia F, Li Y, Xu H, et al.; IEEE. A simple method to calibrate projection matrix of c-arm cone-beam ct. Proc Biomed Eng Biotech. 2012; p. 682–685.Google Scholar
  3. 3.
    Feldkamp LA, Davis L, Kress JW. Practical cone-beam algorithm. Josa A. 1984;1(6):612–619.CrossRefGoogle Scholar
  4. 4.
    Parker DL. Optimal short scan convolution reconstruction for fan beam CT. Med Phys. 1982;9(2):254–257.CrossRefGoogle Scholar
  5. 5.
    Maier J, Black M, Bonaretti S, et al. Comparison of different approaches for measuring tibial cartilage thickness. J Integ Bioinf. 2017;14(2).Google Scholar
  6. 6.
    Navab N, Bani-Hashemi A, Nadar MS, et al.; Springer. 3D reconstruction from projection matrices in a C-arm based 3D-angiography system. Int Conf Med Image Comput Comput-Assist Interv. 1998; p. 119–129.Google Scholar
  7. 7.
    Hartley R, Zisserman A. Multiple View Geometry in Computer Vision. Cambridge University Press; 2003.Google Scholar
  8. 8.
    Choi JH, Maier A, Keil A, et al. Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II. experiment. Med Phys. 2014;41.Google Scholar
  9. 9.
    Fieselmann A, Ritschl L. Isocenter determination for arbitrary planar cone-beam CT scan trajectories. Proc Intl Mtg Image Form. 2016; p. 241–4.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Ahmed Amri
    • 1
    Email author
  • Bastian Bier
    • 1
  • Jennifer Maier
    • 1
  • Andreas Maier
    • 1
  1. 1.Department of Computer Science 5, Pattern RecognitionFAU Erlangen-NürnbergErlangenDeutschland

Personalised recommendations