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Progressive Pretraining Network for 3D System Matrix Calibration in Magnetic Particle Imaging

文献类型：期刊论文

作者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
刊名IEEE TRANSACTIONS ON MEDICAL IMAGING
出版日期2023-12-01
卷号42期号:12页码:3639-3650
ISSN号0278-0062
关键词Magnetic particle imaging system matrix multimodal data pretraining strategy
DOI10.1109/TMI.2023.3297173
通讯作者Hui, Hui(hui.hui@ia.ac.cn) ; Tian, Jie(tian@ieee.org)
英文摘要Magnetic 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.
WOS关键词SUPERRESOLUTION ; RECONSTRUCTION
资助项目National Key Research and Development Program of China
WOS研究方向Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001122030500035
资助机构National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/55003]  
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Hui, Hui; Tian, Jie
作者单位1.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
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

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