Fast and accurate reconstruction of human lung gas MRI with deep learning

doi:10.1002/mrm.27889

	Fast and accurate reconstruction of human lung gas MRI with deep learning
	Duan, Caohui 1,3; Deng, He 1,3; Xiao, Sa 1,3; Xie, Junshuai 1,3; Li, Haidong 1,3; Sun, Xianping 1,3; Ma, Lin 2; Lou, Xin 2; Ye, Chaohui 1,3; Zhou, Xin 1,3
刊名	MAGNETIC RESONANCE IN MEDICINE
	2019-07-19
页码	13
关键词	convolutional neural networks deep learning hyperpolarized gas image reconstruction MRI
ISSN号	0740-3194
DOI	10.1002/mrm.27889
英文摘要	Purpose To fast and accurately reconstruct human lung gas MRI from highly undersampled k-space using deep learning. Methods The scheme was comprised of coarse-to-fine nets (C-net and F-net). Zero-filling images from retrospectively undersampled k-space at an acceleration factor of 4 were used as input for C-net, and then output intermediate results which were fed into F-net. During training, a L2 loss function was adopted in C-net, while a function that united L2 loss with proton prior knowledge was used in F-net. The 871 hyperpolarized Xe-129 pulmonary ventilation images from 72 volunteers were randomly arranged as training (90%) and testing (10%) data. Ventilation defect percentage comparisons were implemented using a paired 2-tailed Student's t-test and correlation analysis. Furthermore, prospective acquisitions were demonstrated in 5 healthy subjects and 5 asymptomatic smokers. Results Each image with size of 96 x 84 could be reconstructed within 31 ms (mean absolute error was 4.35% and structural similarity was 0.7558). Compared with conventional compressed sensing MRI, the mean absolute error decreased by 17.92%, but the structural similarity increased by 6.33%. For ventilation defect percentage, there were no significant differences between the fully sampled and reconstructed images through the proposed algorithm (P = 0.932), but had significant correlations (r = 0.975; P < 0.001). The prospectively undersampled results validated a good agreement with fully sampled images, with no significant differences in ventilation defect percentage but significantly higher signal-to-noise ratio values. Conclusion The proposed algorithm outperformed classical undersampling methods, paving the way for future use of deep learning in real-time and accurate reconstruction of gas MRI.
资助项目	National Natural Science Foundation of China[81625011] ; National Natural Science Foundation of China[81771917] ; National Natural Science Foundation of China[91859206] ; National Natural Science Foundation of China[81730048] ; National Natural Science Foundation of China[81825012] ; National Key R&D Program of China[2016YFC1304700] ; Key Research Program of Frontier Sciences, CAS[QYZDY-SSW-SLH018] ; Hubei Provincial Natural Science Foundation of China[2017CFA013] ; Hubei Provincial Natural Science Foundation of China[2018ACA143]
WOS关键词	HYPERPOLARIZED HE-3 ; NEURAL-NETWORKS ; ACQUISITION ; VENTILATION ; HEALTHY ; SPARSE ; XENON
WOS研究方向	Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	WILEY
WOS记录号	WOS:000478153200001
资助机构	National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; Key Research Program of Frontier Sciences, CAS ; Key Research Program of Frontier Sciences, CAS ; Hubei Provincial Natural Science Foundation of China ; Hubei Provincial Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; Key Research Program of Frontier Sciences, CAS ; Key Research Program of Frontier Sciences, CAS ; Hubei Provincial Natural Science Foundation of China ; Hubei Provincial Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; Key Research Program of Frontier Sciences, CAS ; Key Research Program of Frontier Sciences, CAS ; Hubei Provincial Natural Science Foundation of China ; Hubei Provincial Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; Key Research Program of Frontier Sciences, CAS ; Key Research Program of Frontier Sciences, CAS ; Hubei Provincial Natural Science Foundation of China ; Hubei Provincial Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.wipm.ac.cn/handle/112942/14682]
专题	中国科学院武汉物理与数学研究所
通讯作者	Zhou, Xin
作者单位	1.Univ Chinese Acad Sci, Beijing, Peoples R China 2.Chinese Peoples Liberat Army Gen Hosp, Dept Radiol, Beijing, Peoples R China 3.Chinese Acad Sci, State Key Lab Magnet Resonance & Atom & Mol Phys, Natl Ctr Magnet Resonance Wuhan, Wuhan Natl Lab Optoelect,Wuhan Inst Phys & Math, Wuhan, Hubei, Peoples R China
推荐引用方式 GB/T 7714	Duan, Caohui,Deng, He,Xiao, Sa,et al. Fast and accurate reconstruction of human lung gas MRI with deep learning[J]. MAGNETIC RESONANCE IN MEDICINE,2019:13.
APA	Duan, Caohui.,Deng, He.,Xiao, Sa.,Xie, Junshuai.,Li, Haidong.,...&Zhou, Xin.(2019).Fast and accurate reconstruction of human lung gas MRI with deep learning.MAGNETIC RESONANCE IN MEDICINE,13.
MLA	Duan, Caohui,et al."Fast and accurate reconstruction of human lung gas MRI with deep learning".MAGNETIC RESONANCE IN MEDICINE (2019):13.