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Ordered subset image reconstruction studied by means of total variation minimization and fast first-order method in low dose computed tomograhpy

Mao Bao-Lin Chen Xiao-Zhao Xiao Da-Yu Fan Sheng-Yu Teng Yue-Yang Kang Yan

Ordered subset image reconstruction studied by means of total variation minimization and fast first-order method in low dose computed tomograhpy

Mao Bao-Lin, Chen Xiao-Zhao, Xiao Da-Yu, Fan Sheng-Yu, Teng Yue-Yang, Kang Yan
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  • Received Date:  31 March 2014
  • Accepted Date:  05 May 2014
  • Published Online:  05 July 2014

Ordered subset image reconstruction studied by means of total variation minimization and fast first-order method in low dose computed tomograhpy

  • 1. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110819, China;
  • 2. Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang 110819, China
Fund Project:  Project supported by the National Natural Science Foundation of China (Grant Nos. 61372014, 61201053, 61302013), the Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20110042110036), and the Fundamental Research Project of Northeastern University, China (Grant No. 110619001).

Abstract: Low-dose computed tomography(CT) has an advantage to reduce X-rays that are harmful to the body. This paper considers the issue of reconstructing high-quality low-dose CT images from incomplete projection data. Generally, this can be done by statistical image reconstruction methods. However, the huge number of iterations of the statistical reconstruction algorithms leads to long computing time, making them difficult to be of practical value. To solve this problem, we propose a method to alleviate the issue by using total variation minimization and fast first-order method for the ordered subsets. We use Split Bregman alternating direction method to solve the optimization problem. Then, the projection onto convex sets method is used to speed up the convergence rate of the iterative method. Numerical experiments show that the relative reconstruction error of the proposed method can decrease faster than the first-order method of ordered subsets with the same iterative number.

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