Open AccessAn Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction
Author(s) -
Lizhe Xie,
Yaoping Hu,
Bin Yan,
Lin Wang,
Benqiang Yang,
Wenyuan Liu,
Libo Zhang,
Limin Luo,
Shu Hu,
Yang Chen
Publication year - 2015
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0142184
Subject(s) - projection (relational algebra) , computer science , cuda , tomography , graphics processing unit , iterative reconstruction , computer vision , algorithm , artificial intelligence , parallel computing , optics , physics
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.