CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术
A review of advances in imaging methodology in fluorescence molecular tomography
Zhang, Peng1; Ma, Chenbin1; Song, Fan1; Fan, Guangda1; Sun, Yangyang1; Feng, Youdan1; Ma, Xibo2,3,4; Liu, Fei5; Zhang, Guanglei1
Source PublicationPHYSICS IN MEDICINE AND BIOLOGY
ISSN0031-9155
2022-05-21
Volume67Issue:10Pages:25
Corresponding AuthorLiu, Fei(liufei@bistu.edu.cn) ; Zhang, Guanglei(guangleizhang@buaa.edu.cn)
AbstractObjective. Fluorescence molecular tomography (FMT) is a promising non-invasive optical molecular imaging technology with strong specificity and sensitivity that has great potential for preclinical and clinical studies in tumor diagnosis, drug development and therapeutic evaluation. However, the strong scattering of photons and insufficient surface measurements make it very challenging to improve the quality of FMT image reconstruction and its practical application for early tumor detection. Therefore, continuous efforts have been made to explore more effective approaches or solutions in the pursuit of high-quality FMT reconstructions. Approach. This review takes a comprehensive overview of advances in imaging methodology for FMT, mainly focusing on two critical issues in FMT reconstructions: improving the accuracy of solving the forward physical model and mitigating the ill-posed nature of the inverse problem from a methodological point of view. More importantly, numerous impressive and practical strategies and methods for improving the quality of FMT reconstruction are summarized. Notably, deep learning methods are discussed in detail to illustrate their advantages in promoting the imaging performance of FMT thanks to large datasets, the emergence of optimized algorithms and the application of innovative networks. Main results. The results demonstrate that the imaging quality of FMT can be effectively promoted by improving the accuracy of optical parameter modeling, combined with prior knowledge, and reducing dimensionality. In addition, the traditional regularization-based methods and deep neural network-based methods, especially end-to-end deep networks, can enormously alleviate the ill-posedness of the inverse problem and improve the quality of FMT image reconstruction. Significance. This review aims to illustrate a variety of effective and practical methods for the reconstruction of FMT images that may benefit future research. Furthermore, it may provide some valuable research ideas and directions for FMT in the future, and could promote, to a certain extent, the development of FMT and other methods of optical tomography.
Keywordfluorescence tomography forward and inverse problem ill-posedness reconstruction method deep learning
DOI10.1088/1361-6560/ac5ce7
WOS KeywordDIFFUSE OPTICAL TOMOGRAPHY ; TOTAL VARIATION REGULARIZATION ; SIMPLIFIED SPHERICAL-HARMONICS ; RADIATIVE-TRANSFER EQUATION ; L-P REGULARIZATION ; ILL-POSED PROBLEMS ; IN-VIVO ; BIOLUMINESCENCE TOMOGRAPHY ; RECONSTRUCTION ALGORITHM ; STRUCTURAL PRIORS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFA0700401] ; National Natural Science Foundation of China[61871022] ; Beijing Natural Science Foundation[7202102] ; 111 Project[B13003]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; 111 Project
WOS Research AreaEngineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectEngineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000789661000001
PublisherIOP Publishing Ltd
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48432
Collection模式识别国家重点实验室_生物识别与安全技术
Corresponding AuthorLiu, Fei; Zhang, Guanglei
Affiliation1.Beihang Univ, Beijing Adv Innovat Ctr Biomed Engn, Sch Biol Sci & Med Engn, Beijing 100191, Peoples R China
2.Chinese Acad Sci, Inst Automat, CBSR, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.Beijing Informat Sci & Technol Univ, Beijing Adv Informat & Ind Technol Res Inst, Beijing 100192, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Peng,Ma, Chenbin,Song, Fan,et al. A review of advances in imaging methodology in fluorescence molecular tomography[J]. PHYSICS IN MEDICINE AND BIOLOGY,2022,67(10):25.
APA Zhang, Peng.,Ma, Chenbin.,Song, Fan.,Fan, Guangda.,Sun, Yangyang.,...&Zhang, Guanglei.(2022).A review of advances in imaging methodology in fluorescence molecular tomography.PHYSICS IN MEDICINE AND BIOLOGY,67(10),25.
MLA Zhang, Peng,et al."A review of advances in imaging methodology in fluorescence molecular tomography".PHYSICS IN MEDICINE AND BIOLOGY 67.10(2022):25.
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