Automated quantification of brain PET in PET/CT using deep learning-based CT-to-MR translation: a feasibility study.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Feb 18, 2025

Abstract

PURPOSE: Quantitative analysis of PET images in brain PET/CT relies on MRI-derived regions of interest (ROIs). However, the pairs of PET/CT and MR images are not always available, and their alignment is challenging if their acquisition times differ considerably. To address these problems, this study proposes a deep learning framework for translating CT of PET/CT to synthetic MR images (MR) and performing automated quantitative regional analysis using MR-derived segmentation.

Authors

  • Daesung Kim
    Department of Artificial Intelligence, Yonsei University, Seoul, Republic of Korea.
  • Kyobin Choo
    Department of Computer Science, 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.
  • Seongjin Kang
    Department of Nuclear Medicine, Yonsei 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.
  • Jaewon Yang
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California.

Keywords

External Resources

Popular Topics
Recent Journals