High-Resolution Breast MRI Reconstruction Using a Deep Convolutional Generative Adversarial Network

Kun Sun, Liangqiong Qu, Chunfeng Lian, Yongsheng Pan, Dan Hu, Bingqing Xia, Xinyue Li, Weimin Chai, Fuhua Yan, Dinggang Shen

Research output: Contribution to journalArticlepeer-review

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 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.

Original languageEnglish
Pages (from-to)1852-1858
Number of pages7
JournalJournal of Magnetic Resonance Imaging
Volume52
Issue number6
DOIs
Publication statusPublished - 2020 Dec

Keywords

  • MRI
  • breast
  • generative adversarial network

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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