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

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/D{sub 0}), 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:
; ;  [1] ;  [2] ;  [3] ;  [1] ;  [4] ;  [5] ;  [6]
  1. Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah (Iran, Islamic Republic of)
  2. School of Energy, Kermanshah University of Technology, Kermanshah (Iran, Islamic Republic of)
  3. Department of Electrical Engineering, Kermanshah University of Technology, Kermanshah (Iran, Islamic Republic of)
  4. Department of Computer Engineering, Islamic Azad University, Kermanshah (Iran, Islamic Republic of)
  5. (Iran, Islamic Republic of)
  6. Department of Electrical Engineering, Razi University, Kermanshah (Iran, Islamic Republic of)
Publication Date:
OSTI Identifier:
22462425
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Dosimetry; Journal Volume: 40; Journal Issue: 1; Other Information: Copyright (c) 2015 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
61 RADIATION PROTECTION AND DOSIMETRY; DISTANCE; FILTERS; FORECASTING; FUZZY LOGIC; NEURAL NETWORKS; RADIATION DOSES; RADIOTHERAPY; THICKNESS