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An Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction

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Listed:
  • Lizhe Xie
  • Yining Hu
  • Bin Yan
  • Lin Wang
  • Benqiang Yang
  • Wenyuan Liu
  • Libo Zhang
  • Limin Luo
  • Huazhong Shu
  • Yang Chen

Abstract

Projection and back-projection are the most computationally intensive parts in Computed Tomography (CT) reconstruction, and are essential to acceleration of CT reconstruction algorithms. Compared to back-projection, parallelization efficiency in projection is highly limited by racing condition and thread unsynchronization. In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU). Texture fetching is also used utilized to further accelerate the interpolations in both projection and back-projection. We validate the performance of this FSNP approach using both simulated and real cone-beam CT data. Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction.

Suggested Citation

  • Lizhe Xie & Yining Hu & Bin Yan & Lin Wang & Benqiang Yang & Wenyuan Liu & Libo Zhang & Limin Luo & Huazhong Shu & Yang Chen, 2015. "An Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-17, November.
    • Handle: RePEc:plo:pone00:0142184
    DOI: 10.1371/journal.pone.0142184
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