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High‐Resolution Breast MRI Reconstruction Using a Deep Convolutional Generative Adversarial Network
Author(s) -
Sun Kun,
Qu Liangqiong,
Lian Chunfeng,
Pan Yongsheng,
Hu Dan,
Xia Bingqing,
Li Xinyue,
Chai Weimin,
Yan Fuhua,
Shen Dinggang
Publication year - 2020
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27256
Subject(s) - wilcoxon signed rank test , generative adversarial network , artificial intelligence , medicine , image quality , deep learning , computer science , breast mri , radiology , pattern recognition (psychology) , image (mathematics) , mammography , breast cancer , mann–whitney u test , cancer
Background A generative adversarial network could be used for high‐resolution (HR) medical image synthesis with reduced scan time. Purpose To evaluate the potential of using a deep convolutional generative adversarial network (DCGAN) for generating HR pre and HR post images based on their corresponding low‐resolution (LR) images (LR pre and LR post ). Study Type This was a retrospective analysis of a prospectively acquired cohort. Population In all, 224 subjects were randomly divided into 200 training subjects and an independent 24 subjects testing set. Field Strength/Sequence Dynamic contrast‐enhanced (DCE) MRI with a 1.5T scanner. Assessment Three breast radiologists independently ranked the image datasets, using the DCE images as the ground truth, and reviewed the image quality of both the original LR images and the generated HR images. The BI‐RADS category and conspicuity of lesions were also ranked. The inter/intracorrelation coefficients (ICCs) of mean image quality scores, lesion conspicuity scores, and Breast Imaging Reporting and Data System (BI‐RADS) categories were calculated between the three readers. Statistical Test Wilcoxon signed‐rank tests evaluated differences among the multireader ranking scores. Results The mean overall image quality scores of the generated HR pre and HR post were significantly higher than those of the original LR pre and LR post (4.77 ± 0.41 vs. 3.27 ± 0.43 and 4.72 ± 0.44 vs. 3.23 ± 0.43, P < 0.0001, respectively, in the multireader study). The mean lesion conspicuity scores of the generated HR pre and HR post were significantly higher than those of the original LR pre and LR post (4.18 ± 0.70 vs. 3.49 ± 0.58 and 4.35 ± 0.59 vs. 3.48 ± 0.61, P < 0.001, respectively, in the multireader study). The ICCs of the image quality scores, lesion conspicuity scores, and BI‐RADS categories had good agreements among the three readers (all ICCs >0.75). Data Conclusion DCGAN was capable of generating HR of the breast from fast pre‐ and postcontrast LR and achieved superior quantitative and qualitative performance in a multireader study. Level of Evidence 3 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2020;52:1852–1858.