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Attention mechanism-based locally connected network for accurate and stable reconstruction in Cerenkov luminescence tomography
Zhang, Xiaoning1,2; Cai, Meishan2,3; Guo, Lishuang1,2; Zhang, Zeyu1,2; Shen, Biluo2,3; Zhang, Xiaojun4; Hu, Zhenhua2,3; Tian, Jie1,2,3
Source PublicationBIOMEDICAL OPTICS EXPRESS
ISSN2156-7085
2021-12-01
Volume12Issue:12Pages:7703-7716
Corresponding AuthorHu, Zhenhua(zhenhua.hu@ia.ac.cn) ; Tian, Jie(tian@ieee.org)
AbstractCerenkov luminescence tomography (CLT) is a novel and highly sensitive imaging technique, which could obtain the three-dimensional distribution of radioactive probes to achieve accurate tumor detection. However, the simplified radiative transfer equation and ill-conditioned inverse problem cause a reconstruction error. In this study, a novel attention mechanism based locally connected (AMLC) network was proposed to reduce barycenter error and improve morphological restorability. The proposed AMLC network consisted of two main parts: a fully connected sub-network for providing a coarse reconstruction result, and a locally connected sub-network based on an attention matrix for refinement. Both numerical simulations and in vivo experiments were conducted to show the superiority of the AMLC network in accuracy and stability over existing methods (MFCNN, KNN-LC network). This method improved CLT reconstruction performance and promoted the application of machine learning in optical imaging research. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
DOI10.1364/BOE.443517
WOS KeywordLAPLACE PRIOR REGULARIZATION ; MORPHOLOGICAL RECONSTRUCTION ; MODEL
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFA0205200] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[92059207] ; Beijing Municipal Natural Science Foundation[JQ19027] ; Zhuhai High-level Health Personnel Team Project (Zhuhai)[HLHPTP201703] ; innovative research team of high-level local universities in Shanghai
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; Zhuhai High-level Health Personnel Team Project (Zhuhai) ; innovative research team of high-level local universities in Shanghai
WOS Research AreaBiochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectBiochemical Research Methods ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000726410500002
PublisherOPTICAL SOC AMER
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/46570
Collection中国科学院分子影像重点实验室
Corresponding AuthorHu, Zhenhua; Tian, Jie
Affiliation1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing, Peoples R China
2.Chinese Acad Sci, Beijing Key Lab Mol Imaging, CAS Key Lab Mol Imaging, Inst Automat,State Key Lab Management & Control C, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
4.Chinese Peoples Liberat Army Gen Hosp, Dept Nucl Med, Beijing 100853, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Xiaoning,Cai, Meishan,Guo, Lishuang,et al. Attention mechanism-based locally connected network for accurate and stable reconstruction in Cerenkov luminescence tomography[J]. BIOMEDICAL OPTICS EXPRESS,2021,12(12):7703-7716.
APA Zhang, Xiaoning.,Cai, Meishan.,Guo, Lishuang.,Zhang, Zeyu.,Shen, Biluo.,...&Tian, Jie.(2021).Attention mechanism-based locally connected network for accurate and stable reconstruction in Cerenkov luminescence tomography.BIOMEDICAL OPTICS EXPRESS,12(12),7703-7716.
MLA Zhang, Xiaoning,et al."Attention mechanism-based locally connected network for accurate and stable reconstruction in Cerenkov luminescence tomography".BIOMEDICAL OPTICS EXPRESS 12.12(2021):7703-7716.
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