Open AccessReview of sparse optimization-based computed tomography image reconstruction from few-view projections
Author(s) -
Linyuan Wang,
Hongkui Liu,
Lei Li,
Bin Yan,
Hanming Zhang,
Ailong Cai,
Jianlin Chen,
Guoen Hu
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.63.208702
Subject(s) - computer science , iterative reconstruction , spare part , artificial intelligence , image (mathematics) , computed tomography , point (geometry) , computer vision , industrial computed tomography , algorithm , mathematics , radiology , medicine , geometry , marketing , business
Computed tomography (CT) is a technology widely used in medicine and industrial non-destructive testing, and the image reconstruction algorithm is a core technology of CT. Now, the image reconstruction from few-view projections is a hot point in the study of reconstruction algorithm. With the advancements in theories and algorithms, the sparse optimization has recently been applied to few-view reconstruction for CT image, and shown to have a good performance in both accuracy and speed. In this paper, basic conclusions and classical algorithms in sparse optimization are introduced. Furthermore, the spare optimization based few-view reconstruction algorithms for CT image, in particular the main results and the values of spare optimization, are summarized. Finally, the future research direction of sparse optimization based few-view reconstruction for CT image is discussed.