Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
| 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
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| Source Publication | PHYSICS IN MEDICINE AND BIOLOGY
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| ISSN | 0031-9155 |
| 2022-05-21 | |
| Volume | 67Issue:10Pages:25 |
| Corresponding Author | Liu, Fei(liufei@bistu.edu.cn) ; Zhang, Guanglei(guangleizhang@buaa.edu.cn) |
| Abstract | Objective. 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. |
| Keyword | fluorescence tomography forward and inverse problem ill-posedness reconstruction method deep learning |
| DOI | 10.1088/1361-6560/ac5ce7 |
| WOS Keyword | DIFFUSE 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 By | SCI |
| Language | 英语 |
| Funding Project | National Key Research and Development Program of China[2017YFA0700401] ; National Natural Science Foundation of China[61871022] ; Beijing Natural Science Foundation[7202102] ; 111 Project[B13003] |
| Funding Organization | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; 111 Project |
| WOS Research Area | Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
| WOS Subject | Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging |
| WOS ID | WOS:000789661000001 |
| Publisher | IOP Publishing Ltd |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://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 Author | Liu, Fei; Zhang, Guanglei |
| Affiliation | 1.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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