CASIA OpenIR  > 中国科学院分子影像重点实验室
PGNet: Projection generative network for sparse-view reconstruction of projection-based magnetic particle imaging
Wu, Xiangjun1,2,3,4; He, Bingxi1,2,3; Gao, Pengli1,2,3; Zhang, Peng5; Shang, Yaxin5; Zhang, Liwen4,6; Zhong, Jing7; Jiang, Jingying1,2,3; Hui, Hui4,6; Tian, Jie1,2,3,4,8
Source PublicationMEDICAL PHYSICS
ISSN0094-2405
2022-10-23
Pages18
Corresponding AuthorJiang, Jingying(jingyingjiang@buaa.edu.cn) ; Hui, Hui(hui.hui@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractBackground Magnetic particle imaging (MPI) is a novel tomographic imaging modality that scans the distribution of superparamagnetic iron oxide nanoparticles. However, it is time-consuming to scan multiview two-dimensional (2D) projections for three-dimensional (3D) reconstruction in projection MPI, such as computed tomography (CT). An intuitive idea is to use the sparse-view projections for reconstruction to improve the temporal resolution. Tremendous progress has been made toward addressing the sparse-view problem in CT, because of the availability of large data sets. For the novel tomography of MPI, to the best of our knowledge, studies on the sparse-view problem have not yet been reported. Purpose The acquisition of multiview projections for 3D MPI imaging is time-consuming. Our goal is to only acquire sparse-view projections for reconstruction to improve the 3D imaging temporal resolution of projection MPI. Methods We propose to address the sparse-view problem in projection MPI by generating novel projections. The data set we constructed consists of three parts: simulation data set (including 3000 3D data), four phantoms data, and an in vivo mouse data. The simulation data set is used to train and validate the network, and the phantoms and in vivo mouse data are used to test the network. When the number of novel generated projections meets the requirements of filtered back projection, the streaking artifacts will be absent from MPI tomographic imaging. Specifically, we propose a projection generative network (PGNet), that combines an attention mechanism, adversarial training strategy, and a fusion loss function and can generate novel projections based on sparse-view real projections. To the best of our knowledge, we are the first to propose a deep learning method to attempt to overcome the sparse-view problem in projection MPI. Results We compare our method with several sparse-view methods on phantoms and in vivo mouse data and validate the advantages and effectiveness of our proposed PGNet. Our proposed PGNet enables the 3D imaging temporal resolution of projection MPI to be improved by 6.6 times, while significantly suppressing the streaking artifacts. Conclusion We proposed a deep learning method operated in projection domain to address the sparse-view reconstruction of MPI, and the data scarcity problem in projection MPI reconstruction is alleviated by constructing a sparse-dense simulated projection data set. By our proposed method, the number of acquisitions of real projections can be reduced. The advantage of our method is that it prevents the generation of streaking artifacts at the source. Our proposed sparse-view reconstruction method has great potential for application to time-sensitive in vivo 3D MPI imaging.
Keyworddeep learning field-free line magnetic particle imaging sparse view
DOI10.1002/mp.16048
WOS KeywordPERFORMANCE
Indexed BySCI
Language英语
Funding ProjectNational 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[81971662] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[81527805] ; CAS Youth Innovation Promotion Association[2018167] ; CAS Key Technology Talent Program ; Natural Science Foundation of Beijing City[7202105] ; Project of High-Level Talents Team Introduction in Zhuhai City[ZhuhaiHLHPTP201703]
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 ; Natural Science Foundation of Beijing City ; Project of High-Level Talents Team Introduction in Zhuhai City
WOS Research AreaRadiology, Nuclear Medicine & Medical Imaging
WOS SubjectRadiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000871435600001
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/50490
Collection中国科学院分子影像重点实验室
Corresponding AuthorJiang, Jingying; Hui, Hui; Tian, Jie
Affiliation1.Beihang Univ, Sch Engn Med, Beijing 100191, Peoples R China
2.Beihang Univ, Sch Biol Sci & Med Engn, Beijing 100191, Peoples R China
3.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Dept Biomed Engn, Beijing, Peoples R China
6.Univ Chinese Acad Sci, Beijing, Peoples R China
7.Beihang Univ, Sch Instrumentat & Optoelect Engn, Beijing, Peoples R China
8.Jinan Univ, Zhuhai Peoples Hosp, Zhuhai Precis Med Ctr, Zhuhai, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wu, Xiangjun,He, Bingxi,Gao, Pengli,et al. PGNet: Projection generative network for sparse-view reconstruction of projection-based magnetic particle imaging[J]. MEDICAL PHYSICS,2022:18.
APA Wu, Xiangjun.,He, Bingxi.,Gao, Pengli.,Zhang, Peng.,Shang, Yaxin.,...&Tian, Jie.(2022).PGNet: Projection generative network for sparse-view reconstruction of projection-based magnetic particle imaging.MEDICAL PHYSICS,18.
MLA Wu, Xiangjun,et al."PGNet: Projection generative network for sparse-view reconstruction of projection-based magnetic particle imaging".MEDICAL PHYSICS (2022):18.
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