A review of advances in imaging methodology in fluorescence molecular tomography

doi:10.1088/1361-6560/ac5ce7

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	A review of advances in imaging methodology in fluorescence molecular tomography
	Zhang, Peng 1; Ma, Chenbin 1; Song, Fan 1; Fan, Guangda 1; Sun, Yangyang 1; Feng, Youdan 1; Ma, Xibo2,3,4 ; Liu, Fei5 ; Zhang, Guanglei 1
发表期刊	PHYSICS IN MEDICINE AND BIOLOGY
ISSN	0031-9155
	2022-05-21
卷号	67 期号:10 页码:25
通讯作者	Liu, Fei(liufei@bistu.edu.cn) ; Zhang, Guanglei(guangleizhang@buaa.edu.cn)
摘要	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.
关键词	fluorescence tomography forward and inverse problem ill-posedness reconstruction method deep learning
DOI	10.1088/1361-6560/ac5ce7
关键词[WOS]	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
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFA0700401] ; National Natural Science Foundation of China[61871022] ; Beijing Natural Science Foundation[7202102] ; 111 Project[B13003]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; 111 Project
WOS研究方向	Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000789661000001
出版者	IOP Publishing Ltd
引用统计	被引频次：21[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48432
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Liu, Fei; Zhang, Guanglei
作者单位	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
推荐引用方式 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.