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Reconstruction for Fluorescence Molecular Tomography via Adaptive Group Orthogonal Matching Pursuit
Kong, Lingxin1,2; An, Yu1,2; Liang, Qian1,2; Yin, Lin1,2; Du, Yang1,2; Tian, Jie1,2,3,4
Source PublicationIEEE Transactions on Biomedical Engineering
ISSN0018-9294
2020-01
Volume67Issue:10.1109/TBME.2019.2963815Pages:1-12
Corresponding AuthorDu, Yang(yang.du@ia.ac.cn) ; Tian, Jie(tian@ieee.org)
SubtypeArticle
Abstract

Objective: Fluorescence molecular tomography (FMT) is a promising medical imaging technology aimed at the non-invasive, specific, and sensitive detection of the distribution of fluorophore. Conventional sparsity prior-based methods of FMT commonly face problems such as over-sparseness, spatial discontinuity, and poor robustness, due to the neglect of the interrelation within the local subspace. To address this, we propose an adaptive group orthogonal matching pursuit (AGOMP) method. Methods: AGOMP is based on a novel local spatial-structured sparse regularization, which leverages local spatial interrelations as group sparsity without the hard prior of the tumor region. The adaptive grouped subspace matching pursuit method was adopted to enhance the interrelatedness of elements within a group, which alleviates the over-sparsity problem to some extent and improves the accuracy, robustness, and morphological similarity of FMT reconstruction. A series of numerical simulation experiments, based on digital mouse with both one and several tumors, were conducted, as well as in vivo mouse experiments. Results: The results demonstrated that the proposed AGOMP method achieved better location accuracy, fluorescent yield reconstruction, relative sparsity, and morphology than state-of-the-art methods under complex conditions for levels of Gaussian noise ranging from 5–25%. Furthermore, the in vivo mouse experiments demonstrated the practical application of FMT with AGOMP. Conclusion: The proposed AGOMP can improve the accuracy and robustness for FMT reconstruction in biomedical application.

Keywordadaptive group orthogonal matching pursuit fluorescence molecular tomography local spatial structured sparsity regularization inverse problem
DOI10.1109/TBME.2019.2963815
WOS KeywordLAPLACE PRIOR REGULARIZATION ; LIGHT ; STRATEGY
Indexed BySCI
Language英语
Funding ProjectMinistry of Science and Technology of China[2017YFA0205] ; National Natural Science Foundation of China[81871514] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81470083] ; National Natural Science Foundation of China[91859119] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[61901472] ; National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences[2018PT32003] ; National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences[2017PT32004] ; National Key R&D Program of China[2018YFC0910602] ; National Key R&D Program of China[2017YFA0205200] ; National Key R&D Program of China[2017YFA0700401] ; National Key R&D Program of China[2016YFA0100902] ; National Key R&D Program of China[2016YFC0103702] ; China Postdoctoral Science Foundation[2017M620952]
Funding OrganizationMinistry of Science and Technology of China ; National Natural Science Foundation of China ; National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences ; National Key R&D Program of China ; China Postdoctoral Science Foundation
WOS Research AreaEngineering
WOS SubjectEngineering, Biomedical
WOS IDWOS:000562053800012
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39021
Collection中国科学院分子影像重点实验室
Corresponding AuthorDu, Yang; Tian, Jie
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
3.北航大数据精准医学研究中心
4.西电分子与神经影像工程研究中心
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Kong, Lingxin,An, Yu,Liang, Qian,et al. Reconstruction for Fluorescence Molecular Tomography via Adaptive Group Orthogonal Matching Pursuit[J]. IEEE Transactions on Biomedical Engineering,2020,67(10.1109/TBME.2019.2963815):1-12.
APA Kong, Lingxin,An, Yu,Liang, Qian,Yin, Lin,Du, Yang,&Tian, Jie.(2020).Reconstruction for Fluorescence Molecular Tomography via Adaptive Group Orthogonal Matching Pursuit.IEEE Transactions on Biomedical Engineering,67(10.1109/TBME.2019.2963815),1-12.
MLA Kong, Lingxin,et al."Reconstruction for Fluorescence Molecular Tomography via Adaptive Group Orthogonal Matching Pursuit".IEEE Transactions on Biomedical Engineering 67.10.1109/TBME.2019.2963815(2020):1-12.
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