Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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 | |
发表期刊 | IEEE Transactions on Medical Imaging |
ISSN | 0278-0062 |
2019 | |
卷号 | 38期号:12页码:2726-2734 |
通讯作者 | Wang, Kun(kun.wang@ia.ac.cn) ; Tian, Jie(tian@ieee.org) |
产权排序 | 1 |
摘要 | 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. |
关键词 | Fluorescence Tomography Multi-modality Fusion Brain |
DOI | 10.1109/TMI.2019.2912222 |
关键词[WOS] | OPTIMIZATION ; SYSTEM ; LIGHT |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Ministry 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] |
项目资助者 | Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences |
WOS研究方向 | Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000510688600002 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/25830 |
专题 | 中国科学院分子影像重点实验室 |
通讯作者 | Kun Wang; Jie Tian |
推荐引用方式 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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