1. An Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction.
- Author
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Lizhe Xie, Yining Hu, Bin Yan, Lin Wang, Benqiang Yang, Wenyuan Liu, Libo Zhang, Limin Luo, Huazhong Shu, and Yang Chen
- Subjects
Medicine ,Science - 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.
- Published
- 2015
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