TY - JOUR
T1 - High-Resolution Breast MRI Reconstruction Using a Deep Convolutional Generative Adversarial Network
AU - Sun, Kun
AU - Qu, Liangqiong
AU - Lian, Chunfeng
AU - Pan, Yongsheng
AU - Hu, Dan
AU - Xia, Bingqing
AU - Li, Xinyue
AU - Chai, Weimin
AU - Yan, Fuhua
AU - Shen, Dinggang
N1 - Funding Information:
This work is supported in part by National Natural Science Foundation of China, with grant number 81801651.
Publisher Copyright:
© 2020 International Society for Magnetic Resonance in Medicine
PY - 2020/12
Y1 - 2020/12
N2 - 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 HRpre and HRpost images based on their corresponding low-resolution (LR) images (LRpre and LRpost). 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 HRpre and HRpost were significantly higher than those of the original LRpre and LRpost (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 HRpre and HRpost were significantly higher than those of the original LRpre and LRpost (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.
AB - 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 HRpre and HRpost images based on their corresponding low-resolution (LR) images (LRpre and LRpost). 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 HRpre and HRpost were significantly higher than those of the original LRpre and LRpost (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 HRpre and HRpost were significantly higher than those of the original LRpre and LRpost (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.
KW - MRI
KW - breast
KW - generative adversarial network
UR - http://www.scopus.com/inward/record.url?scp=85087743930&partnerID=8YFLogxK
U2 - 10.1002/jmri.27256
DO - 10.1002/jmri.27256
M3 - Article
C2 - 32656955
AN - SCOPUS:85087743930
SN - 1053-1807
VL - 52
SP - 1852
EP - 1858
JO - Journal of Magnetic Resonance Imaging
JF - Journal of Magnetic Resonance Imaging
IS - 6
ER -