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Adaptive Gaussian Weighted Laplace Prior Regularization Enables Accurate Morphological Reconstruction in Fluorescence Molecular Tomography
Hui Meng; Kun Wang; Yuan Gao; Yushen Jin; Xibo Ma; Jie Tian
Source PublicationIEEE Transactions on Medical Imaging
ISSN0278-0062
2019
Volume38Issue:12Pages:2726-2734
Corresponding AuthorWang, Kun(kun.wang@ia.ac.cn) ; Tian, Jie(tian@ieee.org)
Contribution Rank1
Abstract

Fluorescence molecular tomography (FMT), as a powerful imaging technique in preclinical research, can offer the three-dimensional distribution of biomarkers by detecting the fluorescently labelled probe noninvasively. However, because of the light scattering effect and the ill-pose of inverse problem, it is challenging to develop an efficient reconstruction method, which can provide accurate location and morphology of the fluorescence distribution. In this research, we proposed a novel adaptive Gaussian weighted Laplace prior (AGWLP) regularization method, which assumed the variance of fluorescence intensity between any two voxels had a non-linear correlation with their Gaussian distance. It utilized an adaptive Gaussian kernel parameter strategy to achieve accurate morphological reconstructions in FMT. To evaluate the performance of AGWLP method, we conducted numerical simulation and in vivo experiments. The results were compared with fast iterative shrinkage (FIS) thresholding method, Split Bregman-resolved TV (SBRTV) regularization method and Gaussian weighted Laplace prior (GWLP) regularization method. We validated in vivo imaging results against planar fluorescence images of frozen sections. The results demonstrated that AGWLP method achieved superior performance in both location and shape recovery of fluorescence distribution. This enabled FMT more suitable and practical for in vivo visualization of biomarkers.

KeywordFluorescence Tomography Multi-modality Fusion Brain
DOI10.1109/TMI.2019.2912222
WOS KeywordOPTIMIZATION ; SYSTEM ; LIGHT
Indexed BySCI
Language英语
Funding ProjectMinistry of Science and Technology of China[2017YFA0205200] ; Ministry of Science and Technology of China[2016YFA0100902] ; National Natural Science Foundation of China[61671449] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81471739] ; National Natural Science Foundation of China[81527805] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[KFJ-STS-ZDTP-059] ; Chinese Academy of Sciences[YJKYYQ20180048] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005]
Funding OrganizationMinistry of Science 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:000510688600002
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:7[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25830
Collection中国科学院分子影像重点实验室
Corresponding AuthorKun Wang; Jie Tian
Recommended Citation
GB/T 7714
Hui Meng,Kun Wang,Yuan Gao,et al. Adaptive Gaussian Weighted Laplace Prior Regularization Enables Accurate Morphological Reconstruction in Fluorescence Molecular Tomography[J]. IEEE Transactions on Medical Imaging,2019,38(12):2726-2734.
APA Hui Meng,Kun Wang,Yuan Gao,Yushen Jin,Xibo Ma,&Jie Tian.(2019).Adaptive Gaussian Weighted Laplace Prior Regularization Enables Accurate Morphological Reconstruction in Fluorescence Molecular Tomography.IEEE Transactions on Medical Imaging,38(12),2726-2734.
MLA Hui Meng,et al."Adaptive Gaussian Weighted Laplace Prior Regularization Enables Accurate Morphological Reconstruction in Fluorescence Molecular Tomography".IEEE Transactions on Medical Imaging 38.12(2019):2726-2734.
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