CLC number: TP391.41
On-line Access: 2018-02-06
Received: 2017-04-30
Revision Accepted: 2017-08-09
Crosschecked: 2017-12-22
Cited: 0
Clicked: 5924
Yong Ding, Tuo Hu. Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(12): 2001-2008.
@article{title="Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing",
author="Yong Ding, Tuo Hu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="12",
pages="2001-2008",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700287"
}
%0 Journal Article
%T Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing
%A Yong Ding
%A Tuo Hu
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 12
%P 2001-2008
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700287
TY - JOUR
T1 - Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse dictionary learning for post-processing
A1 - Yong Ding
A1 - Tuo Hu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 12
SP - 2001
EP - 2008
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700287
Abstract: Recently, low-dose computed tomography (CT) has become highly desirable because of the growing concern for the potential risks of excessive radiation. For low-dose CT imaging, it is a significant challenge to guarantee image quality while reducing radiation dosage. Compared with classical filtered backprojection algorithms, compressed sensing-based iterative reconstruction has achieved excellent imaging performance, but its clinical application is hindered due to its computational inefficiency. To promote low-dose CT imaging, we propose a promising reconstruction scheme which combines total-variation minimization and sparse dictionary learning to enhance the reconstruction performance, and properly schedule them with an adaptive iteration stopping strategy to boost the reconstruction speed. Experiments conducted on a digital phantom and a physical phantom demonstrate a superior performance of our method over other methods in terms of image quality and computational efficiency, which validates its potential for low-dose CT imaging.
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