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Article Dans Une Revue PLoS ONE Année : 2015

An Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction

Résumé

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

Dates et versions

hal-01274316 , version 1 (15-02-2016)

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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, 2015, 10 (11), pp.e0142184. ⟨10.1371/journal.pone.0142184⟩. ⟨hal-01274316⟩
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