An Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction

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
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01274316
Contributor : Laurent Jonchère <>
Submitted on : Monday, February 15, 2016 - 4:56:15 PM
Last modification on : Thursday, July 11, 2019 - 2:10:07 PM

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Lizhe Xie, Yining Hu, Bin Yan, Lin Wang, Benqiang Yang, et al.. An Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction. PLoS ONE, Public Library of Science, 2015, 10 (11), pp.e0142184. ⟨10.1371/journal.pone.0142184⟩. ⟨hal-01274316⟩

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