Accelerate treatment planning process using deep learning generated fluence maps for cervical cancer radiation therapy.

Journal: Medical physics
Published Date: Feb 25, 2022

Abstract

PURPOSE: This study aims to develop a deep learning method that skips the time-consuming inverse optimization process for automatic generation of machine-deliverable intensity-modulated radiation therapy (IMRT) plans.

Authors

  • Zengtai Yuan
    Department of Engineering and Applied Physics, University of Science and Technology of China, Anhui, China.
  • Yuxiang Wang
    Hefei Ion Medical Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui, China.
  • Panpan Hu
    Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Duoer Zhang
    Department of Engineering and Applied Physics, University of Science and Technology of China, Anhui, China.
  • Bing Yan
    Department of Otolaryngology Head and Neck Surgery, the First Affiliated Hosipital of Xiamen University, Xiamen, China.
  • Hsiao-Ming Lu
    Hefei Ion Medical Center, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Anhui, China.
  • Hongyan Zhang
    Beijing Tongren Eye Center, Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and visual Sciences, National Engineering Research Center for Ophthalmology, Beijing, China.
  • Yidong Yang
    Department of Engineering and Applied Physics, University of Science and Technology of China, Anhui, China.

Keywords

External Resources

Popular Topics
Recent Journals