Open AccessOrdered subset image reconstruction studied by means of total variation minimization and fast first-order method in low dose computed tomograhpy
Author(s) -
Baolin Mao,
Xiaozhao Chen,
Di Xiao,
Shuxiang Fan,
Teng Yue-yang,
Yan Kang
Publication year - 2014
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.138701
Subject(s) - minification , projection (relational algebra) , iterative reconstruction , computer science , convergence (economics) , algorithm , mathematical optimization , image (mathematics) , rate of convergence , regular polygon , iterative method , convex optimization , mathematics , artificial intelligence , geometry , economics , computer network , channel (broadcasting) , economic growth
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.