Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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 | |
发表期刊 | IEEE TRANSACTIONS ON MEDICAL IMAGING |
ISSN | 0278-0062 |
2022-03-01 | |
卷号 | 41期号:3页码:657-666 |
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. |
关键词 | Imaging Image reconstruction Mice In vivo Image segmentation Finite element analysis Surface reconstruction Fluorescence molecular tomography imaging reconstruction standardized imaging space |
DOI | 10.1109/TMI.2021.3120011 |
关键词[WOS] | FLUORESCENCE MOLECULAR TOMOGRAPHY ; IMMUNOTHERAPY ; OPPORTUNITIES ; CHALLENGES ; ALGORITHM |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | Ministry of Science and Technology of the People's Republic of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation |
WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000766268800014 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48073 |
专题 | 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.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 |
第一作者单位 | 中国科学院分子影像重点实验室; 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院分子影像重点实验室; 中国科学院自动化研究所 |
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. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A_Fast_and_Automated(8285KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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