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A cycle generative adversarial network for generating synthetic contrast-enhanced computed tomographic images from non-contrast images in the internal jugular lymph node-bearing area

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Abstract

The objectives of this study were to create a mutual conversion system between contrast-enhanced computed tomography (CECT) and non-CECT images using a cycle generative adversarial network (cycleGAN) for the internal jugular region. Image patches were cropped from CT images in 25 patients who underwent both CECT and non-CECT imaging. Using a cycleGAN, synthetic CECT and non-CECT images were generated from original non-CECT and CECT images, respectively. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were calculated. Visual Turing tests were used to determine whether oral and maxillofacial radiologists could tell the difference between synthetic versus original images, and receiver operating characteristic (ROC) analyses were used to assess the radiologists’ performances in discriminating lymph nodes from blood vessels. The PSNR of non-CECT images was higher than that of CECT images, while the SSIM was higher in CECT images. The Visual Turing test showed a higher perceptual quality in CECT images. The area under the ROC curve showed almost perfect performances in synthetic as well as original CECT images. In conclusion, synthetic CECT images created by cycleGAN appeared to have the potential to provide effective information in patients who could not receive contrast enhancement.

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Data availability

The dataset used in current study is available reasonable request to the corresponding author.

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Acknowledgements

We thank Helen Jeays, BDSc AE, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

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Authors and Affiliations

  1. Department of Oral Radiology, School of Dentistry, Osaka Dental University, 1-5-17 Otemae, Chuo-Ku, Osaka, Japan

    Motoki Fukuda, Shinya Kotaki, Michihito Nozawa & Yoshiko Ariji

  2. Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, Nagoya, Japan

    Chiaki Kuwada, Yoshitaka Kise & Eiichiro Ariji

Authors
  1. Motoki Fukuda
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  2. Shinya Kotaki
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  3. Michihito Nozawa
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  4. Chiaki Kuwada
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  5. Yoshitaka Kise
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  6. Eiichiro Ariji
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  7. Yoshiko Ariji
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Contributions

Motoki Fukuda conducted the full experiment involving the deep learning method, compiled the results, and wrote the full manuscript. Yoshiko Ariji, Shinya Kotaki, and Michihito Nozawa evaluated the CT images as the observers. Chiaki Kuwada, Yoshitaka Kise, and Eiichiro Ariji supervised the whole experiment and provided important instructions and advice. All authors reviewed the manuscript.

Corresponding author

Correspondence to Motoki Fukuda.

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Conflict of interest

All authors declare that they have no conflicts of interest. These include grants, patent licensing arrangements, consultancies, stock or other equity ownership, advisory board memberships, or payments for conducting or publicizing the study.

Human rights

This study was approved by Aichi Gakuin University’s ethics committee (approval no. 586). All procedures were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the 1975 Declaration of Helsinki, as amended in 2008. As this study was a retrospective study, informed consent could not be obtained from all patients. The purposes and methods of this study were made public, and patients were given the right to opt out of participation.

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Fukuda, M., Kotaki, S., Nozawa, M. et al. A cycle generative adversarial network for generating synthetic contrast-enhanced computed tomographic images from non-contrast images in the internal jugular lymph node-bearing area. Odontology (2024). https://doi.org/10.1007/s10266-024-00933-1

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