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Zero\u2010filling images from retrospectively undersampled k\u2010space at an acceleration factor of 4 were used as input for C\u2010net, and then output intermediate results which were fed into F\u2010net. During training, a L2 loss function was adopted in C\u2010net, while a function that united L2 loss with proton prior knowledge was used in F\u2010net. The 871 hyperpolarized\n                      <jats:sup>129<\/jats:sup>\n                      Xe 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\u2010tailed Student's\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test and correlation analysis. Furthermore, prospective acquisitions were demonstrated in 5 healthy subjects and 5 asymptomatic smokers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Each image with size of 96 \u00d7 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 (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.932), but had significant correlations (\n                      <jats:italic>r<\/jats:italic>\n                      = 0.975;\n                      <jats:italic>P<\/jats:italic>\n                      &lt; 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\u2010to\u2010noise ratio values.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed algorithm outperformed classical undersampling methods, paving the way for future use of deep learning in real\u2010time and accurate reconstruction of gas MRI.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.27889","type":"journal-article","created":{"date-parts":[[2019,7,19]],"date-time":"2019-07-19T06:31:42Z","timestamp":1563517902000},"page":"2273-2285","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Fast and accurate reconstruction of human lung gas MRI with deep learning"],"prefix":"10.1002","volume":"82","author":[{"given":"Caohui","family":"Duan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences \u2010 Wuhan National Laboratory for Optoelectronics Wuhan P. 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