Reconstruction based on adaptive group least angle regression for fluorescence molecular tomography
An, Yu3; Wang, Hanfan1; Li, Jiaqian3; Li, Guanghui3; Ma, Xiaopeng2; Du, Yang1; Tian, Jie1,3
刊名BIOMEDICAL OPTICS EXPRESS
2023-05-01
卷号14期号:5页码:2225-2239
ISSN号2156-7085
DOI10.1364/BOE.486451
通讯作者Ma, Xiaopeng(xiaopeng.ma@sdu.edu.cn) ; Du, Yang(yang.du@ia.ac.cn) ; Tian, Jie(tian@ieee.org)
英文摘要Fluorescence molecular tomography can combine two-dimensional fluorescence imaging with anatomical information to reconstruct three-dimensional images of tumors. Recon-struction based on traditional regularization with tumor sparsity priors does not take into account that tumor cells form clusters, so it performs poorly when multiple light sources are used. Here we describe reconstruction based on an "adaptive group least angle regression elastic net" (AGLEN) method, in which local spatial structure correlation and group sparsity are integrated with elastic net regularization, followed by least angle regression. The AGLEN method works iteratively using the residual vector and a median smoothing strategy in order to adaptively obtain a robust local optimum. The method was verified using numerical simulations as well as imaging of mice bearing liver or melanoma tumors. AGLEN reconstruction performed better than state-of-the-art methods with different sizes of light sources at different distances from the sample and in the presence of Gaussian noise at 5-25%. In addition, AGLEN-based reconstruction accurately imaged tumor expression of cell death ligand-1, which can guide immunotherapy.
资助项目National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[61901472] ; National Natural Science Foundation of China[81871514] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81470083] ; National Natural Science Foundation of China[91859119] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81930053] ; Beijing Municipal Natural Science Foundation[7212207] ; Beijing Municipal Natural Science Foundation[4232058] ; National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences[2017PT32004] ; National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences[2018PT32003] ; Project of High-Level Talents Team Introduction in Zhuhai City[HLHPTP201703] ; Key Research and Development Program of Shandong[2022CXGC010501]
WOS研究方向Biochemistry & Molecular Biology ; Optics ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者Optica Publishing Group
WOS记录号WOS:000994433200004
资助机构National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation ; National Public Welfare Basic Scientific Research Program of Chinese Academy of Medical Sciences ; Project of High-Level Talents Team Introduction in Zhuhai City ; Key Research and Development Program of Shandong
内容类型期刊论文
源URL[http://ir.ia.ac.cn/handle/173211/53341]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Ma, Xiaopeng; Du, Yang; Tian, Jie
作者单位1.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
2.Shandong Univ, Sch Control Sci & Engn, Jinan 250061, Shandong, Peoples R China
3.Beihang Univ, Sch Engn Med, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol Peoples Republ China, Beijing 100191, Peoples R China
推荐引用方式
GB/T 7714
An, Yu,Wang, Hanfan,Li, Jiaqian,et al. Reconstruction based on adaptive group least angle regression for fluorescence molecular tomography[J]. BIOMEDICAL OPTICS EXPRESS,2023,14(5):2225-2239.
APA An, Yu.,Wang, Hanfan.,Li, Jiaqian.,Li, Guanghui.,Ma, Xiaopeng.,...&Tian, Jie.(2023).Reconstruction based on adaptive group least angle regression for fluorescence molecular tomography.BIOMEDICAL OPTICS EXPRESS,14(5),2225-2239.
MLA An, Yu,et al."Reconstruction based on adaptive group least angle regression for fluorescence molecular tomography".BIOMEDICAL OPTICS EXPRESS 14.5(2023):2225-2239.
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