Fast and accurate reconstruction of human lung gas MRI with deep learning
文献类型:期刊论文
作者 | Duan, Caohui2,3; Deng, He2,3; Xiao, Sa2,3; Xie, Junshuai2,3; Li, Haidong2,3; Sun, Xianping2,3; Ma, Lin1; Lou, Xin1; Ye, Chaohui2,3; Zhou, Xin2,3 |
刊名 | MAGNETIC RESONANCE IN MEDICINE |
出版日期 | 2019-07-19 |
页码 | 13 |
ISSN号 | 0740-3194 |
关键词 | convolutional neural networks deep learning hyperpolarized gas image reconstruction MRI |
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研究方向 | Radiology, Nuclear Medicine & Medical Imaging |
语种 | 英语 |
出版者 | WILEY |
WOS记录号 | WOS:000478153200001 |
源URL | [http://202.127.146.157/handle/2RYDP1HH/7814] |
专题 | 中国科学院武汉植物园 |
通讯作者 | Zhou, Xin |
作者单位 | 1.Chinese Peoples Liberat Army Gen Hosp, Dept Radiol, Beijing, Peoples R China 2.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 3.Univ Chinese Acad Sci, Beijing, 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. |
入库方式: OAI收割
来源:武汉植物园
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