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
Source PublicationBIOMEDICAL OPTICS EXPRESS
ISSN2156-7085
2022-09-01
Volume13Issue:9Pages:4693-4705
Corresponding AuthorLuo, Jianwen(luo_jianwen@tsinghua.edu.cn)
AbstractA 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
WOS KeywordMOLECULAR TOMOGRAPHY ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61871022] ; National Natural Science Foundation of China[61871251] ; National Natural Science Foundation of China[62027901]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaBiochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectBiochemical Research Methods ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000863048100006
PublisherOptica Publishing Group
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/50383
Collection中国科学院分子影像重点实验室
Corresponding AuthorLuo, Jianwen
Affiliation1.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
Recommended Citation
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.
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