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Non-convex sparse regularization approach framework for high multiple-source resolution in Cerenkov luminescence tomography

文献类型：期刊论文

作者Guo, Hongbo1,2; Hu, Zhenhua2,3,4,5; He, Xiaowei1; Zhang, Xiaojun6; Liu, Muhan2; Zhang, Zeyu2; Shi, Xiaojing2; Zheng, Sheng2; Tian, Jie2,3,4,5
刊名OPTICS EXPRESS
出版日期2017-11-13
卷号25期号:23页码:28068-28085
关键词Multiple-source Resolution
DOI10.1364/OE.25.028068
文献子类Article
英文摘要With the help of the clinical application of CLI in tumour and lymph node imaging, Cerenkov luminescence tomography (CLT) has the potential to be used for cancer staging. If staging cancer based on optical image of tumour, node and metastasis, one of the critical issues is multiple-source resolution. Because of the ill-posedness of the inverse problem and the diversity of tumor biological characteristics, the multiple-source resolution is a meaningful but challenge problem. In this paper, based on the compression perception theory, a non-convex sparse regularization algorithm (nCSRA) framework was proposed to improve the capacity of multiple-source resolving. Two typical algorithms (homotopy and iterative shrinkage-thresholding algorithm) were explored to test the performance of nCSRA. In numerical simulations and in vivo imaging experiments, the comparison results showed that the proposed nCSRA framework can significantly enhance the multiple-source resolution capability in aspect of spatial resolution, intensity resolution, and size resolution. (C) 2017 Optical Society of America
WOS关键词FLUORESCENCE MOLECULAR TOMOGRAPHY ; BIOLUMINESCENCE TOMOGRAPHY ; IMAGING-SYSTEM ; LYMPH-NODES ; RADIOTRACERS ; RECONSTRUCTION ; FEASIBILITY ; ENDOSCOPY ; ALGORITHM ; ALLOWS
WOS研究方向Optics
语种英语
WOS记录号WOS:000415136700008
资助机构National Natural Science Foundation of China (NSFC)(81227901 ; National Key Research and Development Program of China(2016YFC0102600) ; Chinese Academy of Sciences(XDB02060010 ; International Innovation Team of CAS(20140491524) ; Beijing Municipal Science & Technology Commission(Z161100002616022) ; 81527805 ; YZ201672) ; 61231004 ; 11571012 ; 61622117 ; 81671759 ; 61302024 ; 61372046)
源URL[http://ir.ia.ac.cn/handle/173211/19552]  
专题自动化研究所_中国科学院分子影像重点实验室
作者单位1.Northwest Univ Xian, Sch Informat Sci & Technol, Xian 710069, Shaanxi, Peoples R China
2.Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
4.State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100080, Peoples R China
6.Chinese Peoples Liberat Army Gen Hosp, Dept Nucl Med, Beijing 100853, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Hongbo,Hu, Zhenhua,He, Xiaowei,et al. Non-convex sparse regularization approach framework for high multiple-source resolution in Cerenkov luminescence tomography[J]. OPTICS EXPRESS,2017,25(23):28068-28085.
APA Guo, Hongbo.,Hu, Zhenhua.,He, Xiaowei.,Zhang, Xiaojun.,Liu, Muhan.,...&Tian, Jie.(2017).Non-convex sparse regularization approach framework for high multiple-source resolution in Cerenkov luminescence tomography.OPTICS EXPRESS,25(23),28068-28085.
MLA Guo, Hongbo,et al."Non-convex sparse regularization approach framework for high multiple-source resolution in Cerenkov luminescence tomography".OPTICS EXPRESS 25.23(2017):28068-28085.

入库方式： OAI收割

来源：自动化研究所

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