Deep learning-based Monte Carlo dose prediction for heavy-ion online adaptive radiotherapy and fast quality assurance: A feasibility study.

Journal: Medical physics
PMID:

Abstract

BACKGROUND: Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments.

Authors

  • Rui He
    Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China. herui@emails.bjut.edu.cn.
  • Jian Wang
    Veterinary Diagnostic Center, Shanghai Animal Disease Control Center, Shanghai, China.
  • Wei Wu
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Hui Zhang
    Department of Pulmonary Vessel and Thrombotic Disease, Sixth Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Yinuo Liu
    College of Engineering and Technology, Tianjin Agricultural University, Tianjin 300392, China.
  • Ying Luo
    School of Statistics, Beijing Normal University, Beijing, China.
  • Xinyang Zhang
    Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Yuanyuan Ma
    Department of Gynecology, The Second People's Hospital of Shenzhen, Shenzhen, China.
  • Xinguo Liu
    Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Yazhou Li
    Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China; Gansu Provincial Hospital, Lanzhou 730000, China.
  • Haibo Peng
    Oncology Department, Clinical Medical College, The First Affiliated Hospital of Chengdu Medical College, Chengdu, 610500, China.
  • Pengbo He
    Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Heavy Ion Radiation Biology and Medicine of Chinese Academy of Sciences, Lanzhou 730000, China; Key Laboratory of Basic Research on Heavy Ion Radiation Application in Medicine, Gansu Province, Lanzhou 730000, China; University of Chinese Academy of Sciences, Beijing 100049, China.
  • Qiang Li
    Department of Dermatology, Air Force Medical Center, PLA, Beijing, People's Republic of China.