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Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography

文献类型：期刊论文

作者Yin, Lin2,3; Wang, Kun2,3; Tong, Tong2,3; An, Yu2,3; Meng, Hui2,3; Yang, Xin2,3; Tian, Jie1,2,3
刊名IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
出版日期2020-07-01
卷号67期号:7页码:2023-2032
ISSN号0018-9294
关键词Image reconstruction Bayes methods Tumors Inverse problems Light sources Tomography Morphology Bioluminescence tomography (BLT) block sparse Bayesian learning morphology recovery
DOI10.1109/TBME.2019.2953732
通讯作者Tian, Jie(tian@ieee.org)
英文摘要Objective: Bioluminescence tomography (BLT) is a non-invasive technique designed to enable three-dimensional (3D) visualization and quantification of viable tumor cells in living organisms. However, despite the excellent sensitivity and specificity of bioluminescence imaging (BLI), BLT is limited by the photon scattering effect and ill-posed inverse problem. If the complete structural information of a light source is considered when solving the inverse problem, reconstruction accuracy will be improved. Methods: This article proposed a block sparse Bayesian learning method based on K-nearest neighbor strategy (KNN-BSBL), which incorporated several types of a priori information including sparsity, spatial correlations among neighboring points, and anatomical information to balance over-sparsity and morphology preservation in BLT. Furthermore, we considered the Gaussian weighted distance prior in a light source and proposed a KNN-GBSBL method to further improve the performance of KNN-BSBL. Results: The results of numerical simulations and in vivo glioma-bearing mouse experiments demonstrated that KNN-BSBL and KNN-GBSBL achieved superior accuracy for tumor spatial positioning and morphology reconstruction. Conclusion: The proposed method KNN-BSBL incorporated several types of a priori information is an efficient and robust reconstruction method for BLT.
WOS关键词FLUORESCENCE MOLECULAR TOMOGRAPHY ; LIGHT ; OPTIMIZATION ; PROPAGATION ; ALGORITHMS ; RECOVERY ; SIGNALS ; MOUSE
资助项目Ministry of Science and Technology of China[2017YFA0205200] ; Ministry of Science and Technology of China[2017YFA0700401] ; Ministry of Science and Technology of China[2016YFC0103803] ; Ministry of Science and Technology of China[2016YFA0100902] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81871442] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[QYZDJSSW-JSC005] ; Chinese Academy of Sciences[XDBS01030200]
WOS研究方向Engineering
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000544063000020
资助机构Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/40068]  
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Tian, Jie
作者单位1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Yin, Lin,Wang, Kun,Tong, Tong,et al. Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography[J]. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,2020,67(7):2023-2032.
APA Yin, Lin.,Wang, Kun.,Tong, Tong.,An, Yu.,Meng, Hui.,...&Tian, Jie.(2020).Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography.IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,67(7),2023-2032.
MLA Yin, Lin,et al."Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography".IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 67.7(2020):2023-2032.

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来源：自动化研究所

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