Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network | |
Zhang,Zeyu1,2,6; Cai,Meishan2,3,6; Gao,Yuan2,3,6; Shi,Xiaojing2,3; Zhang,Xiaojun4; Hu,Zhenhua2,3,7; Tian,Jie1,2,3,5,7 | |
发表期刊 | Physics in Medicine & Biology |
ISSN | 0031-9155 |
2019-12-01 | |
卷号 | 64期号:2019页码:245010 |
摘要 | Abstract Cerenkov luminescence tomography (CLT) has been proved as an effective tool for various biomedical applications. Because of the severe scattering of Cerenkov luminescence, the performance of CLT remains unsatisfied. This paper proposed a novel CLT reconstruction approach based on a multilayer fully connected neural network (MFCNN). Monte Carlo simulation data was employed to train the MFCNN, and the complex relationship between the surface signals and the true sources was effectively learned by the network. Both simulation and in vivo experiments were performed to validate the performance of MFCNN CLT, and it was further compared with the typical radiative transfer equation (RTE) based method. The experimental data showed the superiority of MFCNN CLT in terms of accuracy and stability. This promising approach for CLT is expected to improve the performance of optical tomography, and to promote the exploration of machine learning in biomedical applications. |
关键词 | Cerenkov luminescence tomography (CLT) optical reconstruction photon propagation neural network inverse problem |
DOI | 10.1088/1361-6560/ab5bb4 |
语种 | 英语 |
资助项目 | innovative research team of high-level local universities in Shanghai ; National Natural Science Foundation of China (NSFC)[81930053] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YZ201672] ; Beijing Nova Program[Z181100006218046] ; National Natural Science Foundation of China (NSFC)[81671759] ; National Natural Science Foundation of China (NSFC)[61622117] ; National Key Research and Development Program of China[2016YFC0102600] ; National Key Research and Development Program of China[2017YFA0205200] ; Chinese Academy of Sciences[GJJSTD20170004] ; Key Research Program of the Chinese Academy of Sciences[KGZD-EW-T03] ; National Natural Science Foundation of China (NSFC)[81527805] ; National Natural Science Foundation of China (NSFC)[81227901] ; National Natural Science Foundation of China (NSFC)[81227901] ; National Natural Science Foundation of China (NSFC)[81527805] ; Key Research Program of the Chinese Academy of Sciences[KGZD-EW-T03] ; Chinese Academy of Sciences[GJJSTD20170004] ; National Key Research and Development Program of China[2017YFA0205200] ; National Key Research and Development Program of China[2016YFC0102600] ; National Natural Science Foundation of China (NSFC)[61622117] ; National Natural Science Foundation of China (NSFC)[81671759] ; Beijing Nova Program[Z181100006218046] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YZ201672] ; National Natural Science Foundation of China (NSFC)[81930053] ; innovative research team of high-level local universities in Shanghai |
WOS记录号 | IOP:0031-9155-64-24-ab5bb4 |
出版者 | IOP Publishing |
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/173211/28621 |
专题 | 中国科学院分子影像重点实验室 |
作者单位 | 1.Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, People’s Republic of China 2.CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China 3.University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China 4.Department of Nuclear Medicine, Chinese PLA General Hospital, Beijing 100853, People’s Republic of China 5.Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, People’s Republic of China 6.These authors contributed equally to this study. 7.Author to whom any correspondence should be addressed. |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang,Zeyu,Cai,Meishan,Gao,Yuan,et al. A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network[J]. Physics in Medicine & Biology,2019,64(2019):245010. |
APA | Zhang,Zeyu.,Cai,Meishan.,Gao,Yuan.,Shi,Xiaojing.,Zhang,Xiaojun.,...&Tian,Jie.(2019).A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network.Physics in Medicine & Biology,64(2019),245010. |
MLA | Zhang,Zeyu,et al."A novel Cerenkov luminescence tomography approach using multilayer fully connected neural network".Physics in Medicine & Biology 64.2019(2019):245010. |
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