Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy.

Journal: Radiation oncology (London, England)
Published Date: Jan 14, 2021

Abstract

BACKGROUND: Surface-guided radiation therapy can be used to continuously monitor a patient's surface motions during radiotherapy by a non-irradiating, noninvasive optical surface imaging technique. In this study, machine learning methods were applied to predict external respiratory motion signals and predict internal liver motion in this therapeutic context.

Authors

  • Guangyu Wang
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China.
  • Zhibin Li
    Epidemiology Research Unit, The First Affiliated Hospital of Xiamen University, Xiamen, China, zhibinli33@163.com.
  • Guangjun Li
    Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China. gjnick829@sina.com.
  • Guyu Dai
    Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
  • Qing Xiao
    Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
  • Long Bai
    State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China. bailong@cqu.edu.cn.
  • Yisong He
    Department of Radiotherapy, West China Hospital of Sichuan University, Chengdu, 610041.
  • Yaxin Liu
    Industrial Research Institute of Robotics and Intelligent Equipment, Harbin Institute of Technology, Weihai 264209, China. liuyaxin@hit.edu.cn.
  • Sen Bai
    Department of Radiotherapy, West China Hospital, Sichuan University, Chengdu, PR China. Electronic address: baisen@scu.edu.cn.

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