Concurrent CT Reconstruction and Visual Analysis Using Hybrid Multi-resolution Raycasting in a Cluster Environment

  • Steffen Frey
  • Christoph Müller
  • Magnus Strengert
  • Thomas Ertl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5875)


GPU clusters nowadays combine enormous computational resources of GPUs and multi-core CPUs. This paper describes a distributed program architecture that leverages all resources of such a cluster to incrementally reconstruct, segment and render 3D cone beam computer tomography (CT) data with the objective to provide the user with results as quickly as possible at an early stage of the overall computation. As the reconstruction of high-resolution data sets requires a significant amount of time, our system first creates a low-resolution preview volume on the head node of the cluster, which is then incrementally supplemented by high-resolution blocks from the other cluster nodes using our multi-resolution renderer. It is further used for graphically choosing reconstruction priority and render modes of sub-volume blocks. The cluster nodes use their GPUs to reconstruct and render sub-volume blocks, while their multi-core CPUs are used to segment already available blocks.


Projection Image Volume Rendering Direct Volume Rendering Eurographics Symposium Eurographics Association 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Feldkamp, L.A., Davis, L.C., Kress, J.W.: Practical cone-beam algorithm. J. Opt. Soc. Am. 1, 612–619 (1984)CrossRefGoogle Scholar
  2. 2.
    Turbell, H.: Cone-Beam Reconstruction Using Filtered Backprojection. PhD thesis, Linköping University, Sweden, Dissertation No. 672 (2001)Google Scholar
  3. 3.
    Cabral, B., Cam, N., Foran, J.: Accelerated Volume Rendering and Tomographic Reconstruction using Texture Mapping Hardware. In: Proceedings of the Symposium on Volume Visualization, pp. 91–98 (1994)Google Scholar
  4. 4.
    Xu, F., Mueller, K.: Accelerating popular tomographic reconstruction algorithms on commodity pc graphics hardware. IEEE Transactions on Nuclear Science, 654–663 (2005)Google Scholar
  5. 5.
    Scherl, H., Keck, B., Kowarschik, M., Hornegger, J.: Fast gpu-based ct reconstruction using the common unified device architecture (cuda). SIAM Journal of Applied Mathematics (2007)Google Scholar
  6. 6.
    Molnar, S., Cox, M., Ellsworth, D., Fuchs, H.: A sorting classification of parallel rendering. IEEE Computer Graphics and Applications 14, 23–32 (1994)CrossRefGoogle Scholar
  7. 7.
    Krüger, J., Westermann, R.: Acceleration Techniques for GPU-based Volume Rendering. In: Proceedings of IEEE Visualization 2003, pp. 287–292 (2003)Google Scholar
  8. 8.
    Stegmaier, S., Strengert, M., Klein, T., Ertl, T.: A Simple and Flexible Volume Rendering Framework for Graphics-Hardware–based Raycasting. In: Proceedings of the International Workshop on Volume Graphics 2005, pp. 187–195 (2005)Google Scholar
  9. 9.
    Wang, C., Gao, J., Shen, H.W.: Parallel Multiresolution Volume Rendering of Large Data Sets with Error-Guided Load Balancing. In: Eurographics Symposium on Parallel Graphics and Visualization, pp. 23–30 (2004)Google Scholar
  10. 10.
    Marchesin, S., Mongenet, C., Dischler, J.M.: Dynamic Load Balancing for Parallel Volume Rendering. In: Eurographics Symposium on Parallel Graphics and Visualization, Eurographics Association, pp. 51–58 (2006)Google Scholar
  11. 11.
    Müller, C., Strengert, M., Ertl, T.: Optimized Volume Raycasting for Graphics-Hardware-based Cluster Systems. In: Eurographics Symposium on Parallel Graphics and Visualization, Eurographics Association, pp. 59–66 (2006)Google Scholar
  12. 12.
    Crassin, C., Neyret, F., Lefebvre, S., Eisemann, E.: Gigavoxels: Ray-guided streaming for efficient and detailed voxel rendering. In: ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D), Boston, MA, Etats-Unis. ACM Press, New York (to appear, 2009)Google Scholar
  13. 13.
    Ljung, P., Lundström, C., Ynnerman, A.: Multiresolution interblock interpolation in direct volume rendering. In: Santos, B.S., Ertl, T., Joy, K.I. (eds.) EuroVis, pp. 259–266. Eurographics Association (2006)Google Scholar
  14. 14.
    Guthe, S., Strasser, W.: Advanced techniques for high quality multiresolution volume rendering. In: Computers and Graphics, pp. 51–58. Elsevier Science, Amsterdam (2004)Google Scholar
  15. 15.
    Heinzl, C.: Analysis and visualization of industrial ct data (2009)Google Scholar
  16. 16.
    Bullitt, E., Aylward, S.R.: Volume rendering of segmented image objects. IEEE Transactions on Medical Imaging 21, 200–202 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Steffen Frey
    • 1
  • Christoph Müller
    • 1
  • Magnus Strengert
    • 1
  • Thomas Ertl
    • 1
  1. 1.Visualisierungsinstitut der Universität Stuttgart 

Personalised recommendations