Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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 | |
发表期刊 | BIOMEDICAL OPTICS EXPRESS |
ISSN | 2156-7085 |
2021-12-01 | |
卷号 | 12期号:12页码:7703-7716 |
通讯作者 | Hu, Zhenhua(zhenhua.hu@ia.ac.cn) ; Tian, Jie(tian@ieee.org) |
摘要 | Cerenkov 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 |
DOI | 10.1364/BOE.443517 |
关键词[WOS] | LAPLACE PRIOR REGULARIZATION ; MORPHOLOGICAL RECONSTRUCTION ; MODEL |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National 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 |
项目资助者 | National 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:000726410500002 |
出版者 | OPTICAL SOC AMER |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46570 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Hu, Zhenhua; Tian, Jie |
作者单位 | 1.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 |
推荐引用方式 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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