Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Improved Block Sparse Bayesian Learning Method Using K-Nearest Neighbor Strategy for Accurate Tumor Morphology Reconstruction in Bioluminescence Tomography | |
Yin, Lin1,2; Wang, Kun1,2; Tong, Tong1,2; An, Yu1,2; Meng, Hui1,2; Yang, Xin1,2; Tian, Jie1,2,3 | |
发表期刊 | IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING |
ISSN | 0018-9294 |
2020-07-01 | |
卷号 | 67期号:7页码:2023-2032 |
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. |
关键词 | Image reconstruction Bayes methods Tumors Inverse problems Light sources Tomography Morphology Bioluminescence tomography (BLT) block sparse Bayesian learning morphology recovery |
DOI | 10.1109/TBME.2019.2953732 |
关键词[WOS] | FLUORESCENCE MOLECULAR TOMOGRAPHY ; LIGHT ; OPTIMIZATION ; PROPAGATION ; ALGORITHMS ; RECOVERY ; SIGNALS ; MOUSE |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences |
WOS研究方向 | Engineering |
WOS类目 | Engineering, Biomedical |
WOS记录号 | WOS:000544063000020 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
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/173211/40069 |
专题 | 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.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China |
第一作者单位 | 中国科学院分子影像重点实验室 |
通讯作者单位 | 中国科学院分子影像重点实验室 |
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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