Deep learning for improving the spatial resolution of magnetic particle imaging

doi:10.1088/1361-6560/ac6e24

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Deep learning for improving the spatial resolution of magnetic particle imaging
	Shang, Yaxin 4,5,6; Liu, Jie 6; Zhang, Liwen 4,5; Wu, Xiangjun 3; Zhang, Peng 6; Yin, Lin 4,5; Hui, Hui 2,4,5; Tian, Jie 1,3,4,5
刊名	PHYSICS IN MEDICINE AND BIOLOGY
	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
DOI	10.1088/1361-6560/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.
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关键词	LOW-DOSE CT ; MRI ; SENSITIVITY ; NETWORK ; MPI
WOS研究方向	Engineering ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	IOP Publishing Ltd
WOS记录号	WOS:000808275200001
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
内容类型	期刊论文
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.