CASIA OpenIR  > 中国科学院分子影像重点实验室
Progressive Pretraining Network for 3D System Matrix Calibration in Magnetic Particle Imaging
Shi, GenY1,2,3; Yin, Lin4; An, Yu1,2,3; Li, Guanghui1,2,3; Zhang, Liwen4; Bian, Zhongwei1,2,3; Chen, Ziwei1,2,3; Zhang, Haoran1,2,3; Hui, Hui4; Tian, Jie1,2,3
Source PublicationIEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN0278-0062
2023-12-01
Volume42Issue:12Pages:3639-3650
Corresponding AuthorHui, Hui(hui.hui@ia.ac.cn) ; Tian, Jie(tian@ieee.org)
AbstractMagnetic particle imaging (MPI) is an emerging technique for determining magnetic nanoparticle distributions in biological tissues. Although system-matrix (SM)-based image reconstruction offers higher image quality than the X-space-based approach, the SM calibration measurement is time-consuming. Additionally, the SM should be recalibrated if the tracer's characteristics or the magnetic field environment change, and repeated SM measurement further increase the required labor and time. Therefore, fast SM calibration is essential for MPI. Existing calibration methods commonly treat each row of the SM as independent of the others, but the rows are inherently related through the coil channel and frequency index. As these two elements can be regarded as additional multimodal information, we leverage the transformer architecture with a self-attention mechanism to encode them. Although the transformer has shown superiority in multimodal fusion learning across several fields, its high complexity may lead to overfitting when labeled data are scarce. Compared with labeled SM (i.e., full size), low-resolution SM data can be easily obtained, and fully using such data may alleviate overfitting. Accordingly, we propose a pseudo-label-based progressive pretraining strategy to leverage unlabeled data. Our method outperforms existing calibration methods on a public real-world OpenMPI dataset and simulation dataset. Moreover, our method improves the resolution of two in-house MPI scanners without requiring full-size SM measurements. Ablation studies confirm the contributions of modeling SM inter-row relations and the proposed pretraining strategy.
KeywordMagnetic particle imaging system matrix multimodal data pretraining strategy
DOI10.1109/TMI.2023.3297173
WOS KeywordSUPERRESOLUTION ; RECONSTRUCTION
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China
Funding OrganizationNational Key Research and Development Program of China
WOS Research AreaComputer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectComputer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:001122030500035
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:9[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/55004
Collection中国科学院分子影像重点实验室
Corresponding AuthorHui, 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, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol China, Beijing 100191, Peoples R China
4.Chinese 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
Shi, GenY,Yin, Lin,An, Yu,et al. Progressive Pretraining Network for 3D System Matrix Calibration in Magnetic Particle Imaging[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2023,42(12):3639-3650.
APA Shi, GenY.,Yin, Lin.,An, Yu.,Li, Guanghui.,Zhang, Liwen.,...&Tian, Jie.(2023).Progressive Pretraining Network for 3D System Matrix Calibration in Magnetic Particle Imaging.IEEE TRANSACTIONS ON MEDICAL IMAGING,42(12),3639-3650.
MLA Shi, GenY,et al."Progressive Pretraining Network for 3D System Matrix Calibration in Magnetic Particle Imaging".IEEE TRANSACTIONS ON MEDICAL IMAGING 42.12(2023):3639-3650.
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