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A Fast and Automated FMT/XCT Reconstruction Strategy Based on Standardized Imaging Space

文献类型：期刊论文

作者An, Yu2,3,4,5,6; Bian, Chang1,2,3,4; Yan, Daxiang2,3,4,5,6; Wang, Hanfan1,2,3,4; Wang, Yu1,2,3,4; Du, Yang1,2,3,4; Tian, Jie2,3,4,5,6
刊名IEEE TRANSACTIONS ON MEDICAL IMAGING
出版日期2022-03-01
卷号41期号:3页码:657-666
关键词Imaging Image reconstruction Mice In vivo Image segmentation Finite element analysis Surface reconstruction Fluorescence molecular tomography imaging reconstruction standardized imaging space
ISSN号0278-0062
DOI10.1109/TMI.2021.3120011
通讯作者An, Yu(yuan1989@buaa.edu.cn) ; Du, Yang(yang.du@ia.ac.cn) ; Tian, Jie(tian@ieee.org)
英文摘要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.
WOS关键词FLUORESCENCE MOLECULAR TOMOGRAPHY ; IMMUNOTHERAPY ; OPPORTUNITIES ; CHALLENGES ; ALGORITHM
资助项目Ministry 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]
WOS研究方向Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000766268800014
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Ministry of Science and Technology of the People's Republic of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/48072]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者An, Yu; Du, Yang; Tian, Jie
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing 100191, Peoples R China
6.Beihang Univ, Sch Engn Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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