Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
K-nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography | |
Meng, Hui1,2,3; Gao, Yuan1,2,3; Yang, Xin1,2,3; Wang, Kun1,2,3; Tian, Jie1,3,4,5,6 | |
发表期刊 | IEEE Transactions on Medical Imaging |
ISSN | 0278-0062 |
2020 | |
卷号 | 无期号:无页码:无 |
通讯作者 | Wang, Kun(kun.wang@ia.ac.cn) ; Tian, Jie(tian@ieee.org) |
产权排序 | 1 |
文章类型 | 期刊论文 |
摘要 | Fluorescence molecular tomography (FMT) is a highly sensitive and noninvasive imaging modality for three-dimensional visualization of fluorescence probe distribution in small animals. However, the simplified photon propagation model and ill-posed inverse problem limit the |
关键词 | Fluorescence Tomography Machine Learning Brain |
DOI | 10.1109/TMI.2020.2984557 |
关键词[WOS] | TOTAL VARIATION REGULARIZATION ; LAPLACE PRIOR REGULARIZATION ; OPTIMIZATION ; REGISTRATION ; LIGHT |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Science and Technology of China[2017YFA0205200] ; Science and Technology of China[2015CB755500] ; Science and Technology of China[2016YFA0100902] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81871442] ; National Natural Science Foundation of China[81527805] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[XDB32030200] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] |
项目资助者 | Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences |
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:000574745800004 |
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/173211/38532 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Wang, Kun |
作者单位 | 1.the CAS Key Laboratory ofMolecular Imaging, Institute of Automation, Beijing 100190, China 2.the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China 3.the Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China 4.the Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China 5.the Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, China 6.the Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing 100191, China |
推荐引用方式 GB/T 7714 | Meng, Hui,Gao, Yuan,Yang, Xin,et al. K-nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography[J]. IEEE Transactions on Medical Imaging,2020,无(无):无. |
APA | Meng, Hui,Gao, Yuan,Yang, Xin,Wang, Kun,&Tian, Jie.(2020).K-nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography.IEEE Transactions on Medical Imaging,无(无),无. |
MLA | Meng, Hui,et al."K-nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography".IEEE Transactions on Medical Imaging 无.无(2020):无. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
TMI2984557.pdf(4698KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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