CASIA OpenIR  > 中国科学院分子影像重点实验室
Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions
Cheng, Jiaju1; Zhang, Peng2,3; Liu, Fei4; Liu, Jie2; Hui, Hui3; Tian, Jie3,5; Luo, Jianwen1
发表期刊BIOMEDICAL OPTICS EXPRESS
ISSN2156-7085
2022-09-01
卷号13期号:9页码:4693-4705
通讯作者Luo, Jianwen(luo_jianwen@tsinghua.edu.cn)
摘要A time-domain fluorescence molecular tomography in reflective geometry (TD-rFMT) has been proposed to circumvent the penetration limit and reconstruct fluorescence distribution within a 2.5-cm depth regardless of the object size. In this paper, an end-to-end encoder-decoder network is proposed to further enhance the reconstruction performance of TD-rFMT. The network reconstructs both the fluorescence yield and lifetime distributions directly from the time-resolved fluorescent signals. According to the properties of TD-rFMT, proper noise was added to the simulation training data and a customized loss function was adopted for self-supervised and supervised joint training. Simulations and phantom experiments demonstrate that the proposed network can significantly improve the spatial resolution, positioning accuracy, and accuracy of lifetime values.(c) 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
DOI10.1364/BOE.466349
关键词[WOS]MOLECULAR TOMOGRAPHY ; MODEL
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[61871022] ; National Natural Science Foundation of China[61871251] ; National Natural Science Foundation of China[62027901]
项目资助者National Natural Science Foundation of China
WOS研究方向Biochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目Biochemical Research Methods ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:000863048100006
出版者Optica Publishing Group
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/50383
专题中国科学院分子影像重点实验室
通讯作者Luo, Jianwen
作者单位1.Tsinghua Univ, Dept Biomed Engn, Sch Med, Beijing 100084, Peoples R China
2.Beijing Jiaotong Univ, Dept Biomed Engn, Sch Comp & Informat Technol, Beijing 100044, Peoples R China
3.Chinese Acad Sci, CAS Key Lab Mol Imaging, Inst Automat, Beijing 100190, Peoples R China
4.Beijing Informat Sci & Technol Univ, Beijing Adv Informat & Ind Technol Res Inst, Beijing 100192, Peoples R China
5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China
推荐引用方式
GB/T 7714
Cheng, Jiaju,Zhang, Peng,Liu, Fei,et al. Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions[J]. BIOMEDICAL OPTICS EXPRESS,2022,13(9):4693-4705.
APA Cheng, Jiaju.,Zhang, Peng.,Liu, Fei.,Liu, Jie.,Hui, Hui.,...&Luo, Jianwen.(2022).Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions.BIOMEDICAL OPTICS EXPRESS,13(9),4693-4705.
MLA Cheng, Jiaju,et al."Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions".BIOMEDICAL OPTICS EXPRESS 13.9(2022):4693-4705.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Cheng, Jiaju]的文章
[Zhang, Peng]的文章
[Liu, Fei]的文章
百度学术
百度学术中相似的文章
[Cheng, Jiaju]的文章
[Zhang, Peng]的文章
[Liu, Fei]的文章
必应学术
必应学术中相似的文章
[Cheng, Jiaju]的文章
[Zhang, Peng]的文章
[Liu, Fei]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。