Attention mechanism-based locally connected network for accurate and stable reconstruction in Cerenkov luminescence tomography

doi:10.1364/BOE.443517

	Attention mechanism-based locally connected network for accurate and stable reconstruction in Cerenkov luminescence tomography
	Zhang, Xiaoning 1,2; Cai, Meishan2,3 ; Guo, Lishuang 1,2; Zhang, Zeyu1,2 ; Shen, Biluo2,3 ; Zhang, Xiaojun 4; Hu, Zhenhua2,3 ; Tian, Jie 1,2,3
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	被引频次：7[WOS] [WOS记录] [WOS相关记录]
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.