CASIA OpenIR  > 中国科学院分子影像重点实验室
K-nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography
Meng, Hui1,2,3; Gao, Yuan1,2,3; Yang, Xin1,2,3; Wang, Kun1,2,3; Tian, Jie1,3,4,5,6
Source PublicationIEEE Transactions on Medical Imaging
ISSN0278-0062
2020
VolumeIssue:Pages:
Corresponding AuthorWang, Kun(kun.wang@ia.ac.cn) ; Tian, Jie(tian@ieee.org)
Contribution Rank1
Subtype期刊论文
Abstract

Fluorescence molecular tomography (FMT) is a highly sensitive and noninvasive imaging modality for three-dimensional visualization of fluorescence probe distribution in small animals. However, the simplified photon propagation model and ill-posed inverse problem limit the
improvement of FMT reconstruction. In this work, we proposed a novel K-nearest neighbor based locally connected (KNN-LC) network to improve the performance of morphological reconstruction in FMT. It directly builds the inverse process of photon transmission by learning the mapping
relation between the surface photon intensity and the distribution of fluorescent source. KNN-LC network cascades a fully connected (FC) sub-network with a locally connected (LC) sub-network, where the FC part provides a coarse reconstruction result and LC part fine-tunes the
morphological quality of reconstructed result. To assess the performance of our proposed network, we implemented both numerical simulation and in vivo studies. Furthermore, split Bregman-resolved total variation (SBRTV) regularization method and inverse problem simulation (IPS)
method were utilized as baselines in all comparisons. The results demonstrated that KNN-LC network achieved accurate reconstruction in both source localization and morphology recovery in a short time. This promoted the in vivo application of FMT for visualizing the distribution of biomarkers inside biological tissue.
 

KeywordFluorescence Tomography Machine Learning Brain
DOI10.1109/TMI.2020.2984557
WOS KeywordTOTAL VARIATION REGULARIZATION ; LAPLACE PRIOR REGULARIZATION ; OPTIMIZATION ; REGISTRATION ; LIGHT
Indexed BySCI
Language英语
Funding ProjectScience and Technology of China[2017YFA0205200] ; Science and Technology of China[2015CB755500] ; Science and Technology of China[2016YFA0100902] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81871442] ; National Natural Science Foundation of China[81527805] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[XDB32030200] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005]
Funding OrganizationScience and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences
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:000574745800004
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/38532
Collection中国科学院分子影像重点实验室
Corresponding AuthorWang, Kun
Affiliation1.the CAS Key Laboratory ofMolecular Imaging, Institute of Automation, Beijing 100190, China
2.the School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
3.the Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
4.the Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China
5.the Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, China
6.the Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing 100191, China
Recommended Citation
GB/T 7714
Meng, Hui,Gao, Yuan,Yang, Xin,et al. K-nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography[J]. IEEE Transactions on Medical Imaging,2020,无(无):无.
APA Meng, Hui,Gao, Yuan,Yang, Xin,Wang, Kun,&Tian, Jie.(2020).K-nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography.IEEE Transactions on Medical Imaging,无(无),无.
MLA Meng, Hui,et al."K-nearest Neighbor Based Locally Connected Network for Fast Morphological Reconstruction in Fluorescence Molecular Tomography".IEEE Transactions on Medical Imaging 无.无(2020):无.
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