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
发表期刊IEEE Transactions on Medical Imaging
ISSN0278-0062
2020
卷号期号:页码:
通讯作者Wang, Kun(kun.wang@ia.ac.cn) ; Tian, Jie(tian@ieee.org)
产权排序1
文章类型期刊论文
摘要

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.
 

关键词Fluorescence Tomography Machine Learning Brain
DOI10.1109/TMI.2020.2984557
关键词[WOS]TOTAL VARIATION REGULARIZATION ; LAPLACE PRIOR REGULARIZATION ; OPTIMIZATION ; REGISTRATION ; LIGHT
收录类别SCI
语种英语
资助项目Science 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]
项目资助者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:000574745800004
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类医学影像处理与分析
引用统计
被引频次:29[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/173211/38532
专题中国科学院分子影像重点实验室
通讯作者Wang, Kun
作者单位1.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
推荐引用方式
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.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
TMI2984557.pdf(4698KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Meng, Hui]的文章
[Gao, Yuan]的文章
[Yang, Xin]的文章
百度学术
百度学术中相似的文章
[Meng, Hui]的文章
[Gao, Yuan]的文章
[Yang, Xin]的文章
必应学术
必应学术中相似的文章
[Meng, Hui]的文章
[Gao, Yuan]的文章
[Yang, Xin]的文章
相关权益政策
暂无数据
收藏/分享
文件名: TMI2984557.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。