LGEANet: LSTM-global temporal convolution-external attention network for respiratory motion prediction.

Journal: Medical physics
Published Date:

Abstract

PURPOSE: To develop a deep learning network that treats the three-dimensional respiratory motion signals as a whole and considers the inter-dimensional correlation between signals of different directions for accurate respiratory tumor motion prediction.

Authors

  • Kunpeng Zhang
    Department of Decision, Operations & Information Technologies, University of Maryland, College Park MD, United States of America. Electronic address: kpzhang@umd.edu.
  • Jiahong Yu
    School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou, China.
  • Jia Liu
    Department of Colorectal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin, China.
  • Qian Li
    Emergency and Critical Care Center, Department of Emergency Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, Zhejiang, China.
  • Shuang Jin
    Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China; School of Automation, Chongqing University, Chongqing 400044, China.
  • Zhe Su
    School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, People's Republic of China. su_zhe@126.com.
  • Xiaotong Xu
    School of Biomedical Engineering, Southern Medical University, Guangdong, Guangzhou, China.
  • Zhenhui Dai
    Department of Radiation Oncology, Guangdong Province Traditional Medical Hospital, Guangzhou, 510000, Guangdong, China.
  • Xuetao Wang
    The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510120 People's Republic of China.
  • Hua Zhang
    School of Clinical Medicine, Hangzhou Medical College, Hangzhou, China.