Prediction of the individual multileaf collimator positional deviations during dynamic IMRT delivery priori with artificial neural network.

Journal: Medical physics
Published Date:

Abstract

PURPOSES: Multileaf collimator (MLC) positional accuracy during dynamic intensity modulation radiotherapy (IMRT) delivery is crucial for safe and accurate patient treatment. The deviations of individual leaf positions from its intended positions can lead to errors in the dose delivered to the patient and hence may adversely affect the treatment outcome. In this study, we propose a state-of-the-art machine learning (ML) method based on an artificial neural network (ANN) for accurately predicting the MLC leaf positional deviations during the dynamic IMRT treatment delivery priori using log file data.

Authors

  • Alexander F I Osman
    Department of Radiation Oncology, American University of Beirut Medical Center, Riad El-Solh, 1107 2020, Beirut, Lebanon.
  • Nabil M Maalej
    Department of Physics, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia.
  • Kunnanchath Jayesh
    Department of Radiation Oncology, American Hospital Dubai, Dubai, United Arab Emirates.