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• Technical Articles •
Research on the method of brain magnetic resonance synthetic DWI generation based on the cycle generative adversarial network
XIA Liang  LIANG Zhipeng  ZHANG Jun 

Cite this article as: XIA L, LIANG Z P, ZHANG J. Research on the method of brain magnetic resonance synthetic DWI generation based on the cycle generative adversarial network[J]. Chin J Magn Reson Imaging, 2023, 14(7): 121-126. DOI:10.12015/issn.1674-8034.2023.07.021.


[Abstract] Objective Based on cycle generative adversarial network (CycleGAN), using unpaired patient head MR image data to achieve mutual conversion between water-suppressed T2WI images and diffusion weighted imaging (DWI) images, and to evaluate the quality of the generated synthetic DWI images.Materials and Methods Brain water-suppressed T2WI images and DWI images of 200 cases were collected. There were 100 cases in the training set and 100 cases in the test set, including 50 cases of acute cerebral infarction. CycleGAN model included two generators and two discriminators. Firstly, two generators were constructed based on convolutional neural networks (CNN). One generator converted water-suppressed T2WI images into synthetic-DWI images, and the other generator converted DWI images into synthetic-T2WI images. Then, two discriminators were constructed based on CNN, which were used to discriminate the real image and the generated synthetic image and update the parameters. The generator and discriminator work alternately to complete the training of CycleGAN model. The image quality of synthetic-DWI was evaluated by MAE, ME, PSNR, SSIM and subjective score. A total of 50 cases of acute cerebral infarction were divided into DWI images and sDWI images, and DICE coefficient was calculated.Results The MAE, ME, PSNR and SSIM values of the synthetic and true DWI images were 34.991±0.989, 15.982±0.978, 26.642±3.428 and 0.927±0.039, respectively. More than 80% of the synthetic DWI images had no or only slight image distortion or artifact. The DICE coefficients of true DWI and synthetic DWI images after infarction segmentation were 0.898±0.324 and 0.849±0.259, respectively.Conclusions The CycleGAN model and unpaired image data can generate high-quality synthetic DWI images and reduce the scanning time for patients who need rapid magnetic resonance imaging.
[Keywords] acute cerebral infarction；cerebral apoplexy；diffusion weighted imaging；cycle generative adversarial network；deep learning；magnetic mesonance imaging

XIA Liang   LIANG Zhipeng*   ZHANG Jun  

Department of Radiology, Sir Run Run Hospital Affiliated to Nanjing Medical University, Nanjing 211000, China

Corresponding author: Liang ZP, E-mail: 410312774@qq.com

Conflicts of interest   None.

Received  2022-10-12
Accepted  2023-06-25
DOI: 10.12015/issn.1674-8034.2023.07.021
Cite this article as: XIA L, LIANG Z P, ZHANG J. Research on the method of brain magnetic resonance synthetic DWI generation based on the cycle generative adversarial network[J]. Chin J Magn Reson Imaging, 2023, 14(7): 121-126. DOI:10.12015/issn.1674-8034.2023.07.021.

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