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

Journal Article · Wed Apr 01 00:00:00 EDT 2015 · Medical Dosimetry

DOI:https://doi.org/10.1016/J.MEDDOS.2014.09.003· OSTI ID:22462425

Dehlaghi, Vahab; Taghipour, Mostafa; Haghparast, Abbas ^[1]; Roshani, Gholam Hossein ^[2]; Rezaei, Abbas ^[3]; Shayesteh, Sajjad Pashootan ^[1]; Adineh-Vand, Ayoub ^[4]

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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)

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.

• OSTI ID: 22462425
• Journal Information: Medical Dosimetry, Vol. 40, 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); ISSN 0958-3947
• Country of Publication: United States
• Language: English

Related Subjects

61 RADIATION PROTECTION AND DOSIMETRY
DISTANCE
FILTERS
FORECASTING
FUZZY LOGIC
NEURAL NETWORKS
RADIATION DOSES
RADIOTHERAPY
THICKNESS