Computed Tomography

  • Oliver Taubmann
  • Martin Berger
  • Marco Bögel
  • Yan Xia
  • Michael Balda
  • Andreas Maier
Open Access
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11111)


CT is doubtlessly one of the most important technologies in medical imaging and offers us views inside the human body that are as valuable to physicians as they are fascinating (cf. Fig. 8.1).


  1. 1.
    M. Balda. Quantitative Computed Tomography. PhD thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg, 2011Google Scholar
  2. 2.
    Martin Berger et al. “Marker-free motion correction in weight-bearing cone-beam CT of the knee joint". In: Medical Physics 43.3 (2016), pp. 1235–1248. doi:
  3. 3.
    T. M. Buzug. Computed Tomography: From Photon Statistics to Modern Cone-Beam CT. Springer, 2008. isbn: 9783540394082Google Scholar
  4. 4.
    Frank Dennerlein and Andreas Maier. “Approximate truncation robust computed tomography - ATRACT". In: Physics in Medicine and BioloagyGoogle Scholar
  5. 5.
    Joachim Hornegger, Andreas Maier, and Markus Kowarschik. “CT Image Reconstruction Basics". In: MR and CT Perfusion and Pharmacokinetic Imaging: Clinical Applications and Theoretical Principles. Ed. by Roland Bammer. 1st ed. Alphen aan den Rijn, Netherlands, 2016, pp. 01–09. isbn: 9781451147155Google Scholar
  6. 6.
    W. A. Kalender. Computed Tomography: Fundamentals, System Technology, Image Quality, Applications. Wiley, 2011. isbn: 9783895786440Google Scholar
  7. 7.
    B. Keck. High Performance Iterative X-Ray CT with Application in 3-D Mammography and Interventional C-arm Imaging Systems. PhD thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014Google Scholar
  8. 8.
    Yanye Lu et al. “Material Decomposition Using Ensemble Learning for Spectral X-ray Imaging". In: IEEE Transactions on Radiation and Plasma Medical Sciences (2018). to appearGoogle Scholar
  9. 9.
    Andreas Maier and Rebecca Fahrig. “GPU Denoising for Computed Tomography". In: Graphics Processing Unit-Based High Performance Computing in Radiation Therapy. Ed. by Xun Jia and Jiang Steve. 1st ed. Vol. 1. 2015. isbn: 978-1-4822-4478-6. doi:
  10. 10.
    Andreas Maier et al. “Fast Simulation of X-ray Projections of Splinebased Surfaces using an Append Buffer". In: Physics in Medicine and Biology 57.19 (2012), pp. 6193–6210Google Scholar
  11. 11.
    A. Maier et al. “CONRAD - A software framework for cone-beam imaging in radiology". In: Medical Physics 40.11 (2013), pp. 111914-1–111914-8Google Scholar
  12. 12.
    M. Manhart. Dynamic Interventional Perfusion Imaging: Reconstruction Algorithms and Clinical Evaluation. PhD thesis, Friedrich-Alexander- Universität Erlangen-Nürnberg, 2014Google Scholar
  13. 13.
    K. Müller. 3-D Imaging of the Heart Chambers with C-arm CT. PhD thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg, 2014Google Scholar
  14. 14.
    Kerstin Müller et al. “Image artefact propagation in motion estimation and reconstruction in interventional cardiac C-arm CT". In: Physics in Medicine and Biology 59.12 (2014), pp. 3121–3138Google Scholar
  15. 15.
    Haibo Wu et al. “Spatial-temporal Total Variation Regularization (STTVR) for 4D-CT Reconstruction". In: Proceedings of SPIE Medical Imaging 2012. Ed. by Norbert J. Pelc. Town & Country Resort and Convention Center, San Diego, CA, USA, 2012Google Scholar
  16. 16.
    Yan Xia et al. “Towards Clinical Application of a Laplace Operatorbased Region of Interest Reconstruction Algorithm in C-arm CT". In:IEEE Trans Med Imaging 33/2014.3 (2014), pp. 593–606. doi:
  17. 17.
    Y. Xia et al. “Patient-bounded extrapolation using low-dose priors for volume-of-interest imaging in C-arm CT". In: Medical Physics 42.4 (2015), pp. 1787–1796Google Scholar
  18. 18.
    Z. Yu et al. “Line plus arc source trajectories and their R-line coverage for long-object cone-beam imaging with a C-arm system". In: Physics in Medicine and Biology 56.12 (2011), pp. 3447–3471Google Scholar
  19. 19.
    G. Zeng. Medical Image Reconstruction: A Conceptual Tutorial. Springer, 2010. isbn: 9783642053689Google Scholar

Copyright information

© The Author(s) 2018

<SimplePara><Emphasis Type="Bold">Open Access</Emphasis> This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.</SimplePara> <SimplePara>The images or other third party material in this book are included in the book's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.</SimplePara>

Authors and Affiliations

  • Oliver Taubmann
    • 1
  • Martin Berger
    • 1
  • Marco Bögel
    • 1
  • Yan Xia
    • 1
  • Michael Balda
    • 1
  • Andreas Maier
    • 1
  1. 1.Pattern Recognition LabErlangenGermany

Personalised recommendations