Accelerating Image Reconstruction in Dual-Head PET System by GPU and Symmetry Properties

Cheng-Ying Chou, Yun Dong, Yukai Hung, Yu-Jiun Kao, Weichung Wang, Chien-Min Kao, Chin-Tu Chen
Department of Bio-Industrial Mechatronics Engineering, National Taiwan University, Taipei, Taiwan
PLoS ONE 7(12): e5054, 2012


   title={Accelerating Image Reconstruction in Dual-Head PET System by GPU and Symmetry Properties},

   author={Chou, C.Y. and Dong, Y. and Hung, Y. and Kao, Y.J. and Wang, W. and Kao, C.M. and Chen, C.T.},

   journal={PloS one},





   publisher={Public Library of Science}


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Positron emission tomography (PET) is an important imaging modality in both clinical usage and research studies. We have developed a compact high-sensitivity PET system that consisted of two large-area panel PET detector heads, which produce more than 224 million lines of response and thus request dramatic computational demands. In this work, we employed a state-of-the-art graphics processing unit (GPU), NVIDIA Tesla C2070, to yield an efficient reconstruction process. Our approaches ingeniously integrate the distinguished features of the symmetry properties of the imaging system and GPU architectures, including block/warp/thread assignments and effective memory usage, to accelerate the computations for ordered subset expectation maximization (OSEM) image reconstruction. The OSEM reconstruction algorithms were implemented employing both CPU-based and GPU-based codes, and their computational performance was quantitatively analyzed and compared. The results showed that the GPU-accelerated scheme can drastically reduce the reconstruction time and thus can largely expand the applicability of the dual-head PET system.
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