中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 中国科学院分子影像重点实验室
Magnetic particle imaging deblurring with dual contrastive learning and adversarial framework

文献类型：期刊论文

作者Zhang, Jiaxin7,8,9; Wei, Zechen7,8,9; Wu, Xiangjun1,2,10; Shang, Yaxin3; Tian, Jie1,2,5,6,7,8,9,10; Hui, Hui4,7,8
刊名COMPUTERS IN BIOLOGY AND MEDICINE
出版日期2023-10-01
卷号165页码:11
ISSN号0010-4825
关键词Magnetic particle imaging Deblurring Unpaired data Contrastive learning Adversarial framework
DOI10.1016/j.compbiomed.2023.107461
通讯作者Tian, Jie(jie.tian@ia.ac.cn) ; Hui, Hui(hui.hui@ia.ac.cn)
英文摘要Magnetic particle imaging (MPI) is an emerging medical imaging technique that has high sensitivity, contrast, and excellent depth penetration. In MPI, x-space is a reconstruction method that transforms the measured voltages into particle concentrations. The reconstructed native image can be modeled as a convolution of the magnetic particle concentration with a point-spread function (PSF). The PSF is one of the important parameters in deconvolution. However, accurately measuring or modeling the PSF in the hardware used for deconvolution is challenging due to the various environment and magnetic particle relaxation. The inaccurate PSF estimation may lead to the loss of the content structure of the MPI image, especially in low gradient fields. In this study, we developed a Dual Adversarial Network (DAN) with patch-wise contrastive constraint to deblur the MPI image. This method can overcome the limitations of unpaired data in data acquisition scenarios and remove the blur around the boundary more effectively than the common deconvolution method. We evaluated the performance of the proposed DAN model on simulated and real data. Experimental results confirmed that our model performs favorably against the deconvolution method that is mainly used for deblurring the MPI image and other GAN-based deep learning models.
WOS关键词RECONSTRUCTION ; RESOLUTION
资助项目National Key Research and Development Program of China[2017YFA0700401] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81827808] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; Beijing Natural Science Foundation[JQ22023] ; CAS Youth Innovation Promotion Association[Y2022055]
WOS研究方向Life Sciences & Biomedicine - Other Topics ; Computer Science ; Engineering ; Mathematical & Computational Biology
语种英语
出版者PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号WOS:001080561100001
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; CAS Youth Innovation Promotion Association
源URL[http://ir.ia.ac.cn/handle/173211/53005]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie; Hui, Hui
作者单位1.Beihang Univ, Sch Engn Med, Beijing, Peoples R China
2.Beihang Univ, Sch Biol Sci & Med Engn, Beijing, Peoples R China
3.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Beihang Univ, Sch Engn Med, Beijing 100083, Peoples R China
6.Beihang Univ, Sch Biol Sci & Med Engn, Beijing 100083, Peoples R China
7.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing, Peoples R China
8.Beijing Key Lab Mol Imaging, Beijing, Peoples R China
9.Univ Chinese Acad Sci, Beijing, Peoples R China
10.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Jiaxin,Wei, Zechen,Wu, Xiangjun,et al. Magnetic particle imaging deblurring with dual contrastive learning and adversarial framework[J]. COMPUTERS IN BIOLOGY AND MEDICINE,2023,165:11.
APA Zhang, Jiaxin,Wei, Zechen,Wu, Xiangjun,Shang, Yaxin,Tian, Jie,&Hui, Hui.(2023).Magnetic particle imaging deblurring with dual contrastive learning and adversarial framework.COMPUTERS IN BIOLOGY AND MEDICINE,165,11.
MLA Zhang, Jiaxin,et al."Magnetic particle imaging deblurring with dual contrastive learning and adversarial framework".COMPUTERS IN BIOLOGY AND MEDICINE 165(2023):11.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

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。