Artificial intelligence-based radiotherapy machine parameter optimization using reinforcement learning.

Journal: Medical physics
Published Date: Nov 5, 2020

Abstract

PURPOSE: To develop and evaluate a volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) approach based on deep-Q reinforcement learning (RL) capable of finding an optimal machine control policy using previous prostate cancer patient CT scans and contours, and applying the policy to new cases to rapidly produce deliverable VMAT plans in a simplified beam model.

Authors

  • William Thomas Hrinivich
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, 21287, USA.
  • Junghoon Lee
    Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.

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