Nuclear Science and Techniques

《核技术》(英文版) ISSN 1001-8042 CN 31-1559/TL     2019 Impact factor 1.556

Nuclear Science and Techniques ›› 2018, Vol. 29 ›› Issue (3): 45 doi: 10.1007/s41365-018-0376-2

• NUCLEAR ELECTRONICS AND INSTRUMENTATION • Previous Articles    

Hybrid reconstruction algorithm for computed tomography based on diagonal total variation

Lu-Zhen Deng 1,2 • Peng He 1,3,4 • Shang-Hai Jiang 1 • Mian-Yi Chen 1 • Biao Wei 1,3,4 • Peng Feng 1,3,4   

  1. 1 Key Laboratory of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China
    2 Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
    3 ICT NDT Engineering Research Center, Ministry of Education, Chongqing University, Chongqing 400044, China
    4 Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing 400044, China
  • Contact: Peng He E-mail:penghe@cqu.edu.cn
  • Supported by:

    This work was supported in part by the National Natural Science Foundation of China (No. 61401049), the Chongqing Foundation and Frontier Research Project (Nos. cstc2016jcyjA0473, cstc2013jcyjA0763), the Graduate Scientific Research and Innovation Foundation of Chongqing, China (No. CYB16044), the Strategic Industry Key Generic Technology Innovation Project of Chongqing (No. cstc2015zdcy-ztzxX0002), China Scholarship Council, and the Fundamental Research Funds for the Central Universities Nos. CDJZR14125501, 106112016CDJXY120003, 10611CDJXZ238826.

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Lu-Zhen Deng, Peng He, Shang-Hai Jiang, Mian-Yi Chen, Biao Wei, Peng Feng. Hybrid reconstruction algorithm for computed tomography based on diagonal total variation.Nuclear Science and Techniques, 2018, 29(3): 45     doi: 10.1007/s41365-018-0376-2
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Abstract:

Inspired by total variation (TV), this paper represents a new iterative algorithm based on diagonal total variation (DTV) to address the computed tomography image reconstruction problem. To improve the quality of a reconstructed image, we used DTV to sparsely represent images when iterative convergence of the reconstructed algorithm with TV-constraint had no effect during the reconstruction process. To investigate our proposed algorithm, the numerical and experimental studies were performed, and rootmean- square error (RMSE) and structure similarity (SSIM) were used to evaluate the reconstructed image quality. The results demonstrated that the proposed method could effectively reduce noise, suppress artifacts, and reconstruct highquality image from incomplete projection data.

Key words: Computed tomography (CT), Sparse-view reconstruction, Diagonal total variation (DTV), Compressive sensing (CS)