Prediction of the thickness of the compensator filter in radiation therapy using computational intelligence.

Journal: Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Published Date: Dec 11, 2014

Abstract

In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) are investigated to predict the thickness of the compensator filter in radiation therapy. In the proposed models, the input parameters are field size (S), off-axis distance, and relative dose (D/D0), and the output is the thickness of the compensator. The obtained results show that the proposed ANN and ANFIS models are useful, reliable, and cheap tools to predict the thickness of the compensator filter in intensity-modulated radiation therapy.

Authors

  • Vahab Dehlaghi
    Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Mostafa Taghipour
    Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Abbas Haghparast
    Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Gholam Hossein Roshani
    School of Energy, Kermanshah University of Technology, Kermanshah, Iran.
  • Abbas Rezaei
    Department of Electrical Engineering, Kermanshah University of Technology, Kermanshah, Iran.
  • Sajjad Pashootan Shayesteh
    Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran.
  • Ayoub Adineh-Vand
    Department of Computer Engineering, Islamic Azad University, Kermanshah, Iran; Department of Electrical Engineering, Razi University, Kermanshah, Iran.
  • Gholam Reza Karimi
    Department of Electrical Engineering, Razi University, Kermanshah, Iran. Electronic address: ghkarimi@razi.ac.ir.

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