title: Prediction of the thickness of the compensator filter in radiation therapy using computational intelligence.
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.affiliation: Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran.
authors.forename: Vahab
authors.initials: V
authors.lastname: Dehlaghi
authors.affiliation: Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran.
authors.forename: Mostafa
authors.initials: M
authors.lastname: Taghipour
authors.affiliation: Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran.
authors.forename: Abbas
authors.initials: A
authors.lastname: Haghparast
authors.affiliation: School of Energy, Kermanshah University of Technology, Kermanshah, Iran.
authors.forename: Gholam Hossein
authors.initials: GH
authors.lastname: Roshani
authors.affiliation: Department of Electrical Engineering, Kermanshah University of Technology, Kermanshah, Iran.
authors.forename: Abbas
authors.initials: A
authors.lastname: Rezaei
authors.affiliation: Department of Biomedical Engineering, Kermanshah University of Medical Sciences, Kermanshah, Iran.
authors.forename: Sajjad Pashootan
authors.initials: SP
authors.lastname: Shayesteh
authors.affiliation: Department of Computer Engineering, Islamic Azad University, Kermanshah, Iran; Department of Electrical Engineering, Razi University, Kermanshah, Iran.
authors.forename: Ayoub
authors.initials: A
authors.lastname: Adineh-Vand
authors.affiliation: Department of Electrical Engineering, Razi University, Kermanshah, Iran. Electronic address: ghkarimi@razi.ac.ir.
authors.forename: Gholam Reza
authors.initials: GR
authors.lastname: Karimi
mesh_terms: D004867:Equipment Design / Q000379:methods; D019544:Equipment Failure Analysis; D005374:Filtration / Q000295:instrumentation*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011874:Radiometry / Q000295:instrumentation / Q000379:methods*; D011879:Radiotherapy Dosage; D020266:Radiotherapy, Conformal / Q000295:instrumentation*; D015203:Reproducibility of Results; D012542:Scattering, Radiation; D012680:Sensitivity and Specificity
keywords: Computational intelligence; Filter in radiation therapy; Radiation therapy; Thickness of the compensator