RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning.

Journal: International journal of radiation oncology, biology, physics
Published Date: Nov 1, 2020

Abstract

PURPOSE: Detailed and accurate absorbed dose calculations from radiation interactions with the human body can be obtained with the Monte Carlo (MC) method. However, the MC method can be slow for use in the time-sensitive clinical workflow. The aim of this study was to provide a solution to the accuracy-time trade-off for Ir-based high-dose-rate brachytherapy by using deep learning.

Authors

  • Ximeng Mao
    Department of Computer Science, McGill University, Montréal, Québec, Canada; Medical Physics Unit, McGill University, Montréal, Québec, Canada; Mila-Quebec Artificial Intelligence Institute, Montréal, Québec, Canada. Electronic address: ximeng.mao@mail.mcgill.ca.
  • Joelle Pineau
    Department of Computer Science, McGill University, Montréal, Québec, Canada; Mila-Quebec Artificial Intelligence Institute, Montréal, Québec, Canada.
  • Roy Keyes
    Medical Physics, Kabibo Research, Houston, Texas.
  • Shirin A Enger
    Medical Physics Unit, McGill University, Montréal, Québec, Canada; Department of Oncology, McGill University, Montréal, Québec, Canada; Research Institute of the McGill University Health Centre, Montréal, Québec, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.

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