CASIA OpenIR  > 中国科学院分子影像重点实验室
A Fast and Automated FMT/XCT Reconstruction Strategy Based on Standardized Imaging Space
An, Yu1,2,3,4,5; Bian, Chang3,4,5,6; Yan, Daxiang1,2,3,4,5; Wang, Hanfan3,4,5,6; Wang, Yu3,4,5,6; Du, Yang3,4,5,6; Tian, Jie1,2,3,4,5
Source PublicationIEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN0278-0062
2022-03-01
Volume41Issue:3Pages:657-666
Abstract

The traditional finite element method-based fluorescence molecular tomography (FMT)/ X-ray computed tomography (XCT) imaging reconstruction suffers from complicated mesh generation and dual-modality image data fusion, which limits the application of in vivo imaging. To solve this problem, a novel standardized imaging space reconstruction (SISR) method for the quantitative determination of fluorescent probe distributions inside small animals was developed. In conjunction with a standardized dual-modality image data fusion technology, and novel reconstruction strategy based on Laplace regularization and L1-fused Lasso method, the in vivo distribution can be calculated rapidly and accurately, which enables standardized and algorithm-driven data process. We demonstrated the method's feasibility through numerical simulations and quantitatively monitored in vivo programmed death ligand 1 (PD-L1) expression in mouse tumor xenografts, and the results demonstrate that our proposed SISR can increase data throughput and reproducibility, which helps to realize the dynamically and accurately in vivo imaging.

KeywordImaging Image reconstruction Mice In vivo Image segmentation Finite element analysis Surface reconstruction Fluorescence molecular tomography imaging reconstruction standardized imaging space
DOI10.1109/TMI.2021.3120011
WOS KeywordFLUORESCENCE MOLECULAR TOMOGRAPHY ; IMMUNOTHERAPY ; OPPORTUNITIES ; CHALLENGES ; ALGORITHM
Indexed BySCI
Language英语
Funding ProjectMinistry of Science and Technology of the People's Republic of China[2018YFC0910602] ; Ministry of Science and Technology of the People's Republic of China[2017YFA0205200] ; Ministry of Science and Technology of the People's Republic of China[2017YFA0700401] ; Ministry of Science and Technology of the People's Republic of China[2019YFC0120800] ; National Natural Science Foundation of China[61901472] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81871514] ; National Natural Science Foundation of China[81227901] ; Beijing Natural Science Foundation[7212207]
Funding OrganizationMinistry of Science and Technology of the People's Republic of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
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:000766268800014
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48073
Collection中国科学院分子影像重点实验室
Corresponding AuthorAn, Yu; Du, Yang; Tian, Jie
Affiliation1.Beihang Univ, Sch Engn Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
2.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing 100191, Peoples R China
3.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China;  Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
An, Yu,Bian, Chang,Yan, Daxiang,et al. A Fast and Automated FMT/XCT Reconstruction Strategy Based on Standardized Imaging Space[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2022,41(3):657-666.
APA An, Yu.,Bian, Chang.,Yan, Daxiang.,Wang, Hanfan.,Wang, Yu.,...&Tian, Jie.(2022).A Fast and Automated FMT/XCT Reconstruction Strategy Based on Standardized Imaging Space.IEEE TRANSACTIONS ON MEDICAL IMAGING,41(3),657-666.
MLA An, Yu,et al."A Fast and Automated FMT/XCT Reconstruction Strategy Based on Standardized Imaging Space".IEEE TRANSACTIONS ON MEDICAL IMAGING 41.3(2022):657-666.
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