핵의학

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  • [Sci Rep.] Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework

    동아의대 / 정영진, 박형석, 강도영*

  • 출처
    Sci Rep.
  • 등재일
    2021 Mar 1
  • 저널이슈번호
    11(1):4825. doi: 10.1038/s41598-021-84358-8
  • 내용

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    Abstract
    Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min into standard-time scanning PET (PET20m) and short-time scanning PET (PET2m) images. We generated a standard-time scanning PET-like image (sPET20m) from a PET2m image using a deep-learning network. We did qualitative and quantitative analyses to assess whether the sPET20m images were available for clinical applications. In our internal validation, sPET20m images showed substantial improvement on all quality metrics compared with the PET2m images. There was a small mean difference between the standardized uptake value ratios of sPET20m and PET20m images. A Turing test showed that the physician could not distinguish well between generated PET images and real PET images. Three nuclear medicine physicians could interpret the generated PET image and showed high accuracy and agreement. We obtained similar quantitative results by means of temporal and external validations. We can generate interpretable PET images from low-quality PET images because of the short scanning time using deep-learning techniques. Although more clinical validation is needed, we confirmed the possibility that short-scanning protocols with a deep-learning technique can be used for clinical applications.

     

     

    Affiliations

    Young Jin Jeong #  1   2 , Hyoung Suk Park #  3 , Ji Eun Jeong  1 , Hyun Jin Yoon  1 , Kiwan Jeon  3 , Kook Cho  4 , Do-Young Kang  5   6   7
    1 Department of Nuclear Medicine, Dong-A University Hospital, Dong-A University College of Medicine, 1, 3ga, Dongdaesin-dong, Seo-gu, Busan, 602-715, South Korea.
    2 Institute of Convergence Bio-Health, Dong-A University, Busan, Republic of Korea.
    3 National Institute for Mathematical Science, Daejeon, Republic of Korea.
    4 College of General Education, Dong-A University, Busan, Republic of Korea.
    5 Department of Nuclear Medicine, Dong-A University Hospital, Dong-A University College of Medicine, 1, 3ga, Dongdaesin-dong, Seo-gu, Busan, 602-715, South Korea. dykang@dau.ac.kr.
    6 Institute of Convergence Bio-Health, Dong-A University, Busan, Republic of Korea. dykang@dau.ac.kr.
    7 Department of Translational Biomedical Sciences, Dong-A University, Busan, Republic of Korea. dykang@dau.ac.kr.
    # Contributed equally.

  • 편집위원

    Amyloid PET 영상에 generative adversarial networks를 이용한 기술적용한 임상연구임. 인공지능을 이용한 핵의학 영상 개선에 대한 연구내용으로 핵의학 영상의 인공적용 적용을 연구하는 연구자 및 신경 핵의학 임상가에게 관심을 끌 연구로 생각됨.

    2021-05-06 15:33:00

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