A method of X-ray source spectrum estimation from transmission measurements based on compressed sensing
A new method of X-ray source spectrum estimation based on compressed sensing is proposed in this paper. The algorithm K-SVD is applied for sparse representation. Nonnegative constraints are added by modifying the L1 reconstruction algorithm proposed by Rosset and Zhu. The estimation method is demons...
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doaj-8fee553f5c05474595d1b49f6c4fc5ee2020-11-25T02:25:49ZengElsevierNuclear Engineering and Technology1738-57332020-07-0152714951502A method of X-ray source spectrum estimation from transmission measurements based on compressed sensingBin Liu0Hongrun Yang1Huanwen Lv2Lan Li3Xilong Gao4Jianping Zhu5Futing Jing6Corresponding author.; Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, ChinaScience and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, ChinaA new method of X-ray source spectrum estimation based on compressed sensing is proposed in this paper. The algorithm K-SVD is applied for sparse representation. Nonnegative constraints are added by modifying the L1 reconstruction algorithm proposed by Rosset and Zhu. The estimation method is demonstrated on simulated spectra typical of mammography and CT. X-ray spectra are simulated with the Monte Carlo code Geant4. The proposed method is successfully applied to highly ill conditioned and under determined estimation problems with a good performance of suppressing noises. Results with acceptable accuracies (MSE < 5%) can be obtained with 10% Gaussian white noises added to the simulated experimental data. The biggest difference between the proposed method and the existing methods is that multiple prior knowledge of X-ray spectra can be included in one dictionary, which is meaningful for obtaining the true X-ray spectrum from the measurements.http://www.sciencedirect.com/science/article/pii/S1738573319306370X-ray source spectrum estimationTransmission measurementsCompressed sensing and sparse representation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bin Liu Hongrun Yang Huanwen Lv Lan Li Xilong Gao Jianping Zhu Futing Jing |
spellingShingle |
Bin Liu Hongrun Yang Huanwen Lv Lan Li Xilong Gao Jianping Zhu Futing Jing A method of X-ray source spectrum estimation from transmission measurements based on compressed sensing Nuclear Engineering and Technology X-ray source spectrum estimation Transmission measurements Compressed sensing and sparse representation |
author_facet |
Bin Liu Hongrun Yang Huanwen Lv Lan Li Xilong Gao Jianping Zhu Futing Jing |
author_sort |
Bin Liu |
title |
A method of X-ray source spectrum estimation from transmission measurements based on compressed sensing |
title_short |
A method of X-ray source spectrum estimation from transmission measurements based on compressed sensing |
title_full |
A method of X-ray source spectrum estimation from transmission measurements based on compressed sensing |
title_fullStr |
A method of X-ray source spectrum estimation from transmission measurements based on compressed sensing |
title_full_unstemmed |
A method of X-ray source spectrum estimation from transmission measurements based on compressed sensing |
title_sort |
method of x-ray source spectrum estimation from transmission measurements based on compressed sensing |
publisher |
Elsevier |
series |
Nuclear Engineering and Technology |
issn |
1738-5733 |
publishDate |
2020-07-01 |
description |
A new method of X-ray source spectrum estimation based on compressed sensing is proposed in this paper. The algorithm K-SVD is applied for sparse representation. Nonnegative constraints are added by modifying the L1 reconstruction algorithm proposed by Rosset and Zhu. The estimation method is demonstrated on simulated spectra typical of mammography and CT. X-ray spectra are simulated with the Monte Carlo code Geant4. The proposed method is successfully applied to highly ill conditioned and under determined estimation problems with a good performance of suppressing noises. Results with acceptable accuracies (MSE < 5%) can be obtained with 10% Gaussian white noises added to the simulated experimental data. The biggest difference between the proposed method and the existing methods is that multiple prior knowledge of X-ray spectra can be included in one dictionary, which is meaningful for obtaining the true X-ray spectrum from the measurements. |
topic |
X-ray source spectrum estimation Transmission measurements Compressed sensing and sparse representation |
url |
http://www.sciencedirect.com/science/article/pii/S1738573319306370 |
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