GAN-based Denoising for Scan Time Reduction and Motion Correction of 18F FP-CIT PET/CT: A Multicenter External Validation Study.

Journal: Clinical nuclear medicine
Published Date: Jul 1, 2025

Abstract

PURPOSE: AI-driven scan time reduction is rapidly transforming medical imaging with benefits such as improved patient comfort and enhanced efficiency. A Dual Contrastive Learning Generative Adversarial Network (DCLGAN) was developed to predict full-time PET scans from shorter, noisier scans, improving challenges in imaging patients with movement disorders.

Authors

  • Hyunkyung Han
    Departments of Artificial Intelligence.
  • Kyobin Choo
    Department of Computer Science, Yonsei University, Seoul, Republic of Korea.
  • Tae Joo Jeon
    Division of Gastroenterology, Department of Internal Medicine, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea.
  • Sangwon Lee
    Urban Robotics Laboratory (URL), Dept. Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 305-338, Korea. lsw618@gmail.com.
  • Seungbeom Seo
    Yonsei University College of Medicine.
  • Dongwoo Kim
    From the Department of Nuclear Medicine, Yonsei University College of Medicine.
  • Sun Jung Kim
    Department of Nuclear Medicine, National Health Insurance Service Hospital, Gyeonggi-do.
  • Suk Hyun Lee
    Department of Radiology, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea.
  • Mijin Yun
    Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea. YUNMIJIN@yuhs.ac.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals