CASIA OpenIR  > 中国科学院分子影像重点实验室
Non-Negative Iterative Convex Refinement Approach for Accurate and Robust Reconstruction in Cerenkov Luminescence Tomography
Cai, Meishan1,2; Zhang, Zeyu3; Shi, Xiaojing1,2; Yang, Junying4; Hu, Zhenhua1,2; Tian, Jie1,2,3,5
Source PublicationIEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN0278-0062
2020-10-01
Volume39Issue:10Pages:3207-3217
Abstract

Cerenkov luminescence tomography (CLT) is a promising imaging tool for obtaining three-dimensional (3D) non-invasive visualization of the in vivo distribution of radiopharmaceuticals. However, the reconstruction performance remains unsatisfactory for biomedical applications because the inverse problem of CLT is severely ill-conditioned and intractable. In this study, therefore, a novel non-negative iterative convex refinement (NNICR) approach was utilized to improve the CLT reconstruction accuracy, robustness as well as the shape recovery capability. The spike and slab prior information was employed to capture the sparsity of Cerenkov source, which could be formalized as a non-convex optimization problem. The NNICR approach solved this non-convex problem by refining the solutions of the convex sub-problems. To evaluate the performance of the NNICR approach, numerical simulations and in vivo tumor-bearing mice models experiments were conducted. Conjugated gradient based Tikhonov regularization approach (CG-Tikhonov), fast iterative shrinkage-thresholding algorithm based Lasso approach (Fista-Lasso) and Elastic-Net regularization approach were used for the comparison of the reconstruction performance. The results of these experiments demonstrated that the NNICR approach obtained superior reconstruction performance in terms of location accuracy, shape recovery capability, robustness and in vivo practicability. It was believed that this study would facilitate the preclinical and clinical applications of CLT in the future.

KeywordImage reconstruction Imaging Mathematical model Shape Slabs Iterative methods Luminescence Cerenkov luminescence tomography sparse reconstruction inverse problem tumor
DOI10.1109/TMI.2020.2987640
WOS KeywordLAPLACE PRIOR REGULARIZATION ; VARIABLE SELECTION ; RESOLUTION ; ALGORITHM ; SPIKE ; LIGHT
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFA0205200] ; National Key Research and Development Program of China[2016YFC0102600] ; National Natural Science Foundation of China (NSFC)[81930053] ; National Natural Science Foundation of China (NSFC)[61622117] ; National Natural Science Foundation of China (NSFC)[81671759] ; National Natural Science Foundation of China (NSFC)[81227901] ; Chinese Academy of Sciences[GJJSTD20170004] ; Scientific Instrument Developing Project of the Chinese Academy of Sciences[YZ201672] ; Key Research Program of the Chinese Academy of Sciences[KGZD-EW-T03] ; Beijing Natural Science Foundation[JQ19027] ; Beijing Nova Program[Z181100006218046] ; innovative research team of highlevel local universities in Shanghai ; Zhuhai High-level Health Personnel Team Project (Zhuhai)[HLHPTP201703]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences ; Scientific Instrument Developing Project of the Chinese Academy of Sciences ; Key Research Program of the Chinese Academy of Sciences ; Beijing Natural Science Foundation ; Beijing Nova Program ; innovative research team of highlevel local universities in Shanghai ; Zhuhai High-level Health Personnel Team Project (Zhuhai)
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000574745800020
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/42026
Collection中国科学院分子影像重点实验室
Corresponding AuthorHu, Zhenhua; Tian, Jie
Affiliation1.Chinese Acad Sci, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, Inst Automat,State Key Lab Management & Control C, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Sch Life Sci & Technol, Xian 710071, Peoples R China
4.Southern Med Univ, Dept Hepatobiliary Surg, Zhujiang Hosp, Guangzhou 510280, Peoples R China
5.Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing 100191, Peoples R China
Recommended Citation
GB/T 7714
Cai, Meishan,Zhang, Zeyu,Shi, Xiaojing,et al. Non-Negative Iterative Convex Refinement Approach for Accurate and Robust Reconstruction in Cerenkov Luminescence Tomography[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2020,39(10):3207-3217.
APA Cai, Meishan,Zhang, Zeyu,Shi, Xiaojing,Yang, Junying,Hu, Zhenhua,&Tian, Jie.(2020).Non-Negative Iterative Convex Refinement Approach for Accurate and Robust Reconstruction in Cerenkov Luminescence Tomography.IEEE TRANSACTIONS ON MEDICAL IMAGING,39(10),3207-3217.
MLA Cai, Meishan,et al."Non-Negative Iterative Convex Refinement Approach for Accurate and Robust Reconstruction in Cerenkov Luminescence Tomography".IEEE TRANSACTIONS ON MEDICAL IMAGING 39.10(2020):3207-3217.
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