CASIA OpenIR  > 中国科学院分子影像重点实验室
A Novel Adaptive Parameter Search Elastic Net Method for Fluorescent Molecular Tomography
Wang, Hanfan1,2; Bian, Chang2,3; Kong, Lingxin2,3; An, Yu2,4; Du, Yang2,3; Tian, Jie2,4,5
Source PublicationIEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN0278-0062
2021-05-01
Volume40Issue:5Pages:1484-1498
Abstract

Fluorescence molecular tomography (FMT) is a new type of medical imaging technology that can quantitatively reconstruct the three-dimensional distribution of fluorescent probes in vivo. Traditional Lp norm regularization techniques used in FMT reconstruction often face problems such as over-sparseness, over-smoothness, spatial discontinuity, and poor robustness. To address these problems, this paper proposes an adaptive parameter search elastic net (APSEN) method that is based on elastic net regularization, using weight parameters to combine the L1 and L2 norms. For the selection of elastic net weight parameters, this approach introduces the L0 norm of valid reconstruction results and the L2 norm of the residual vector, which are used to adjust the weight parameters adaptively. To verify the proposed method, a series of numerical simulation experiments were performed using digital mice with tumors as experimental subjects, and in vivo experiments of liver tumors were also conducted. The results showed that, compared with the state-of-the-art methods with different light source sizes or distances, Gaussian noise of 5%-25%, and the brute-force parameter search method, the APSEN method has better location accuracy, spatial resolution, fluorescence yield recovery ability, morphological characteristics, and robustness. Furthermore, the in vivo experiments demonstrated the applicability of APSEN for FMT.

KeywordImage reconstruction Fluorescence Probes Mathematical model Photonics Molecular imaging Biological tissues Fluorescence molecular tomography adaptive parameter search elastic net
DOI10.1109/TMI.2021.3057704
WOS KeywordRECONSTRUCTION METHOD ; REGULARIZATION ; SELECTION
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] ; Beijing Natural Science Foundation[7212207] ; 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] ; National Natural Science Foundation of Shaanxi Provience[2019JM-459]
Funding OrganizationMinistry of Science and Technology of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences ; National Key R&D Program of China ; National Natural Science Foundation of Shaanxi Provience
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000645866500016
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/173211/44667
Collection中国科学院分子影像重点实验室
Corresponding AuthorAn, Yu; Du, Yang; Tian, Jie
Affiliation1.Xidian Univ, Sch Life Sci & Technol, Xian 710071, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China
4.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med Sci & Engn, Beijing 100191, Peoples R China
5.Xidian Univ, Sch Life Sci & Technol, Minist Educ, Engn Res Ctr Mol & Neuro Imaging, Xian 710071, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Hanfan,Bian, Chang,Kong, Lingxin,et al. A Novel Adaptive Parameter Search Elastic Net Method for Fluorescent Molecular Tomography[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2021,40(5):1484-1498.
APA Wang, Hanfan,Bian, Chang,Kong, Lingxin,An, Yu,Du, Yang,&Tian, Jie.(2021).A Novel Adaptive Parameter Search Elastic Net Method for Fluorescent Molecular Tomography.IEEE TRANSACTIONS ON MEDICAL IMAGING,40(5),1484-1498.
MLA Wang, Hanfan,et al."A Novel Adaptive Parameter Search Elastic Net Method for Fluorescent Molecular Tomography".IEEE TRANSACTIONS ON MEDICAL IMAGING 40.5(2021):1484-1498.
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