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Deep learning for improving the spatial resolution of magnetic particle imaging

文献类型：期刊论文

作者Shang, Yaxin4,5,6; Liu, Jie6; Zhang, Liwen4,5; Wu, Xiangjun3; Zhang, Peng6; Yin, Lin4,5; Hui, Hui2,4,5; Tian, Jie1,3,4,5
刊名PHYSICS IN MEDICINE AND BIOLOGY
出版日期2022-06-21
卷号67期号:12页码:14
关键词deep learning magnetic particle imaging spatial resolution superparamagnetic iron oxide nanoparticles
ISSN号0031-9155
DOI10.1088/1361-6560/ac6e24
通讯作者Liu, Jie(jieliu@bjtu.edu.cn) ; Hui, Hui(hui.hui@ia.ac.cn) ; Tian, Jie(tian@ieee.org)
英文摘要Objective. Magnetic particle imaging (MPI) is a new medical, non-destructive, imaging method for visualizing the spatial distribution of superparamagnetic iron oxide nanoparticles. In MPI, spatial resolution is an important indicator of efficiency; traditional techniques for improving the spatial resolution may result in higher costs, lower sensitivity, or reduced contrast. Approach. Therefore, we propose a deep-learning approach to improve the spatial resolution of MPI by fusing a dual-sampling convolutional neural network (FDS-MPI). An end-to-end model is established to generate high-spatial-resolution images from low-spatial-resolution images, avoiding the aforementioned shortcomings. Main results. We evaluate the performance of the proposed FDS-MPI model through simulation and phantom experiments. The results demonstrate that the FDS-MPI model can improve the spatial resolution by a factor of two. Significance. This significant improvement in MPI could facilitate the preclinical application of medical imaging modalities in the future.
WOS关键词LOW-DOSE CT ; MRI ; SENSITIVITY ; NETWORK ; MPI
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[81527805] ; National Natural Science Foundation of China[81571836] ; National Natural Science Foundation of China[81671851] ; National Natural Science Foundation of China[KKA309004533] ; National Natural Science Foundation of China[81227901] ; CAS Youth Innovation Promotion Association[2018167] ; CAS Key Technology Talent Program ; Project of High-Level Talents Team Introduction in Zhuhai City[Zhuhai HLHPTP201703]
WOS研究方向Engineering ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:000808275200001
出版者IOP Publishing Ltd
资助机构National 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
源URL[http://ir.ia.ac.cn/handle/173211/49572]  
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Liu, Jie; Hui, Hui; Tian, Jie
作者单位1.Jinan Univ, Zhuhai Precis Med Ctr, Zhuhai Peoples Hosp, Zhuhai 519000, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100080, Peoples R China
3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100083, Peoples R China
4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
6.Beijing Jiaotong Univ, Sch Comp & Informat Technol, Beijing 100069, Peoples R China
推荐引用方式
GB/T 7714		 Shang, Yaxin,Liu, Jie,Zhang, Liwen,et al. Deep learning for improving the spatial resolution of magnetic particle imaging[J]. PHYSICS IN MEDICINE AND BIOLOGY,2022,67(12):14.
APA Shang, Yaxin.,Liu, Jie.,Zhang, Liwen.,Wu, Xiangjun.,Zhang, Peng.,...&Tian, Jie.(2022).Deep learning for improving the spatial resolution of magnetic particle imaging.PHYSICS IN MEDICINE AND BIOLOGY,67(12),14.
MLA Shang, Yaxin,et al."Deep learning for improving the spatial resolution of magnetic particle imaging".PHYSICS IN MEDICINE AND BIOLOGY 67.12(2022):14.

入库方式： OAI收割

来源：自动化研究所

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