Deep Learning-Based Precontrast CT Parcellation for MRI-Free Brain Amyloid PET Quantification.

Journal: Clinical nuclear medicine
PMID: 39876079

Abstract

PURPOSE: This study aimed to develop a deep learning (DL) model for brain region parcellation using CT data from PET/CT scans to enable accurate amyloid quantification in 18 F-FBB PET/CT without relying on high-resolution MRI.

Authors

  • Kyobin Choo
    Department of Computer Science, Yonsei University, Seoul, Republic of Korea.
  • Jaehoon Joo
    Department of Artificial Intelligence, Yonsei University, Seoul, Republic of 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.
  • Daesung Kim
    Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea.
  • Hyunkeong Lim
    Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Dongwoo Kim
    From the Department of Nuclear Medicine, Yonsei University College of Medicine.
  • Seongjin Kang
    Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Seong Jae Hwang
    Dept. of Computer Sciences, Univ. of Wisconsin-Madison.
  • Mijin Yun
    Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea. YUNMIJIN@yuhs.ac.

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