Experiences with Cell-BE and GPU for Tomography

  • Sander van der Maar
  • Kees Joost Batenburg
  • Jan Sijbers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5657)


Tomography is a powerful technique for three-dimensional imaging, that deals with image reconstruction from a series of projection images, acquired along a range of viewing directions. An important part of any tomograph system is the reconstruction algorithm. Iterative reconstruction algorithms have many advantages over non-iterative methods, yet their running time can be prohibitively long. As these algorithms have high potential for parallelization, multi-core architectures, such as the Cell-BE and GPU, can possibly alleviate this problem.

In this paper, we describe our experiences in mapping the basic operations of iterative reconstruction algorithms onto these platforms. We argue that for this type of problem, the GPU yields superior performance compared to the Cell-BE. Performance results of our implementation demonstrate a speedup of over 40 for a single GPU, compared to a single-core CPU version. By combining eight GPUs and a quad-core CPU in a single system, similar performance to a large cluster consisting of hundreds of CPU cores has been obtained.


Memory Access Iterative Reconstruction Graphic Hardware Iterative Reconstruction Algorithm Forward Projection 
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.


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  1. 1.
    Kak, A.C., Slaney, M.: Principles of Computerized Tomographic Imaging. In: Volume Algorithms for reconstruction with non-diffracting sources, pp. 49–112. IEEP Press, New York (1988)Google Scholar
  2. 2.
    FASTRA GPU SuperPC (2008),
  3. 3.
    Core Facility CalcUA (2008),
  4. 4.
    Feldkamp, L.A., Davis, L.C., Kress, J.W.: Practical cone-beam algorithm. Journal of the Optical Society of America A: Optics, Image Science, and Vision 1(6), 612–619 (1984)CrossRefGoogle Scholar
  5. 5.
    NVIDIA Corporation. NVIDIA CUDA Compute Unified Device Architecture, Programming Guide Version 1.0 (June 2007)Google Scholar
  6. 6.
    Xu, F., Mueller, K.: Real-time 3D computed tomographic reconstruction using commodity graphics hardware. Physics in Medicine and Biology 52, 3405–3419 (2007)CrossRefGoogle Scholar
  7. 7.
    Mueller, K., Xu, F., Neophytou, N.: Why do commodity graphics hardware boards (GPUs) work so well for acceleration of computed tomography? In: SPIE Electronic Imaging (2007)Google Scholar
  8. 8.
    van der Maar, S.: Tomography mapped onto the Cell Broadband Processor. Master’s thesis, Universiteit Leiden, The Netherlands (August 2007)Google Scholar
  9. 9.
    Gschwind, M.: The cell broadband engine: exploiting multiple levels of parallelism in a chip multiprocessor. Int. J. Parallel Program. 35(3), 233–262 (2007)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Sander van der Maar
    • 1
  • Kees Joost Batenburg
    • 1
  • Jan Sijbers
    • 1
  1. 1.IBBT-Vision Lab, Department of PhysicsUniversity of AntwerpBelgium

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