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Department of Research and Development, Shanghai United Imaging Intelligence Co.,Ltd., Shanghai, China.
Published: December 2020
AI Article Synopsis
Article Abstract
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 and HR images based on their corresponding low-resolution (LR) images (LR and LR ).
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 and HR were significantly higher than those of the original LR and LR (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 and HR were significantly higher than those of the original LR and LR (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.
