CASIA OpenIR  > 中国科学院分子影像重点实验室
System matrix recovery based on deep image prior in magnetic particle imaging
Yin, Lin1,2,3; Guo, Hongbo4; Zhang, Peng5; Li, Yimeng6; Hui, Hui1,2,3; Du, Yang1,2,3; Tian, Jie1,2,3,6
Source PublicationPHYSICS IN MEDICINE AND BIOLOGY
ISSN0031-9155
2023-02-07
Volume68Issue:3Pages:14
Corresponding AuthorTian, Jie(tian@ieee.org)
AbstractObjective. Magnetic particle imaging (MPI) is an emerging tomography imaging technique with high specificity and temporal-spatial resolution. MPI reconstruction based on the system matrix (SM) is an important research content in MPI. However, SM is usually obtained by measuring the response of an MPI scanner at all positions in the field of view. This process is very time-consuming, and the scanner will overheat in a long period of continuous operation, which is easy to generate thermal noise and affects MPI imaging performance. Approach. In this study, we propose a deep image prior-based method that prominently decreases the time of SM calibration. It is an unsupervised method that utilizes the neural network structure itself to recover a high-resolution SM from a downsampled SM without the need to train the network using a large amount of training data. Main results. Experiments on the Open MPI data show that the time of SM calibration can be greatly reduced with only slight degradation of image quality. Significance. This study provides a novel method for obtaining SM in MPI, which shows the potential to achieve SM recovery at a high downsampling rate. It is expected that this study will increase the practicability of MPI in biomedical applications and promote the development of MPI in the future.
Keywordmagnetic particle imaging deep image prior system matrix recovery
DOI10.1088/1361-6560/acaf47
WOS KeywordRECONSTRUCTION ; RESOLUTION
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFA0205200] ; National Key Research and Development Program of China[2017YFA0700401] ; National Key Research and Development Program of China[2016YFC0103803] ; National Natural Science Foundation of China[62027901] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[82230067] ; National Natural Science Foundation of China[62201570] ; CAS Youth Innovation Promotion Association[2018167] ; CAS Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai)[HLHPTP201703]
Funding OrganizationNational Key Research and Development Program of China ; National Natural Science Foundation of China ; CAS Youth Innovation Promotion Association ; CAS Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City (Zhuhai)
WOS Research AreaEngineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectEngineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000918241900001
PublisherIOP Publishing Ltd
Citation statistics
Cited Times:8[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/51330
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie
Affiliation1.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, People's Republ China, Beijing 100190, Peoples R China
2.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Peoples R China
5.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100044, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yin, Lin,Guo, Hongbo,Zhang, Peng,et al. System matrix recovery based on deep image prior in magnetic particle imaging[J]. PHYSICS IN MEDICINE AND BIOLOGY,2023,68(3):14.
APA Yin, Lin.,Guo, Hongbo.,Zhang, Peng.,Li, Yimeng.,Hui, Hui.,...&Tian, Jie.(2023).System matrix recovery based on deep image prior in magnetic particle imaging.PHYSICS IN MEDICINE AND BIOLOGY,68(3),14.
MLA Yin, Lin,et al."System matrix recovery based on deep image prior in magnetic particle imaging".PHYSICS IN MEDICINE AND BIOLOGY 68.3(2023):14.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yin, Lin]'s Articles
[Guo, Hongbo]'s Articles
[Zhang, Peng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yin, Lin]'s Articles
[Guo, Hongbo]'s Articles
[Zhang, Peng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yin, Lin]'s Articles
[Guo, Hongbo]'s Articles
[Zhang, Peng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.