Estimation of the central-axis-reference percent depth dose in a water phantom using artificial intelligence

Aird, 1996 Alamaniotis, 2015, Hybrid fuzzy-genetic approach integrating peak identification and spectrum fitting for complex gamma-ray spectra analysis, IEEE Transactions on Nuclear Science, 62, 1262, 10.1109/TNS.2015.2432098 Andreo, 2006 Apipunyasopon, 2013, An investigation of the depth dose in the build-up region, and surface dose for a 6-mv therapeutic photon beam: Monte Carlo simulation and measurements, Journal of Radiation Research, 54, 374, 10.1093/jrr/rrs097 Attix, 1991 Bakkali, 2016, Behaviors of the percentage depth dose curves along the beam axis of a phantom filled with different clinical to objects, a montecarlo geant4 study, Radiation Physics and Chemistry, 125, 199, 10.1016/j.radphyschem.2016.04.013 Baumgartner, 2009, Simulation of photon energy spectra from varian 2100c and 2300c/d linacs: Simplified estimates with penelope monte carlo models, Applied Radiation and Isotopes, 67, 2007, 10.1016/j.apradiso.2009.07.010 Brahme, 1984, Dosimetric precision requirements in radiation therapy, Acta Radiologica: Oncology, 23, 379, 10.3109/02841868409136037 Brun, 2018 Das, 2007, Small fields: Nonequilibrium radiation dosimetry, Medical Physics, 35, 206, 10.1118/1.2815356 d´Errico, 2001, Depth dose-equivalent and effective energies of photo neutrons generated by 6-18 mv x-ray beams for radiotherapy, Health Physics, 80, 4, 10.1097/00004032-200101000-00003 Faiz, 2014 Fleckenstein, 2013, Development of a geant4 based monte carlo algorithm to evaluate the monaco vmat treatment accuracy, Medical Physics, 23, 33 Freeman, 1991 Hamed Abd El-Kader, 2014, Dosimetry measurements of radiation fields, Research & Reviews in BioSciencies, 8, 302 IAEA (2004). Dosimetry codes of practice and worksheets - International atomic energy agency Kandlakunta, 2019, Characterizing a geant4 monte carlo model of a multileaf collimator for a truebeam„¢ linear accelerator, Physica Medica, 59, 1, 10.1016/j.ejmp.2019.02.008 Knoll, 2010 Kumar, 2015, A new approach to nuclear reactor design optimization using genetic algorithms and regression analysis, Annals of Nuclear Energy, 85, 27, 10.1016/j.anucene.2015.04.028 Langdon, 2002 Li, 2011, Photon energy spectrum reconstruction based on monte carlo and measured percentage depth dose in accurate radiotherapy, Progress in Nuclear Science and Technology, 2, 160, 10.15669/pnst.2.160 Medhat, 2012, Artificial intelligence methods applied for quantitative analysis of natural radioactive sources, Annals of Nuclear Energy, 45, 73, 10.1016/j.anucene.2012.02.013 Mesbahi, 2006, Development and commissioning of a monte carlo photon beam model for varian clinic 2100ex linear accelerator, Applied Radiation and Isotopes, 64, 656, 10.1016/j.apradiso.2005.12.012 Mijnheer, 1987, What degree of accuracy is required and can be achieved in photon and neutron therapy?, Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology, 8, 237, 10.1016/S0167-8140(87)80247-5 Pal, 2002, Estimation of percentage depth dose distributions for therapeutic machines, Radiation Physics and Chemistry, 65, 589, 10.1016/S0969-806X(02)00270-0 Panettieri, 2009, Aaa and pbc calculation accuracy in the surface build-up region in tangential beam treatments. phantom and breast case study with the monte carlo code penelope, Radiotherapy and Oncology, 93, 94, 10.1016/j.radonc.2009.05.010 Pilato, 1999, Application of neural networks to quantitative spectrometry analysis, Nuclear Instruments and Methods in Physics Research, 422, 423, 10.1016/S0168-9002(98)01110-3 Pinheiro, 2018, Genetic programming applied to the identification of accidents of a pwr nuclear power plant, Annals of Nuclear Energy, 124, 335, 10.1016/j.anucene.2018.09.039 Podgorsak, 2006 Pozzi, 2000, Evaluation of genetic programming and neural networks techniques for nuclear material identification, Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation, 2, 590 Price, 1964 Ramirez, 2011, Dosimetry of small radiation field in inhomogeneous medium using alanine/epr minidosimeters and penelope monte carlo simulation, Radiation Measurements, 46, 941, 10.1016/j.radmeas.2011.06.008 Reza, 2018, Calibration of therapy level ionization chamber at 60co teletherapy beam used for radiation therapy, international letters of chemistry physics and astronomy, International Letters of Chemistry, Physics and Astronomy, 79, 1, 10.18052/www.scipress.com/ILCPA.79.1 Santos, 2012, Unfolding neutron spectra obtained from bs tld system using genetic algorithm, Applied Radiation and Isotopes, 71, 81, 10.1016/j.apradiso.2012.06.031 Sawakuchi, 2010, An mcnpx monte carlo model of a discrete spot scanning proton beam therapy nozzle, Medical Physics, 37, 4960, 10.1118/1.3476458 Vega-Carrillo, 2006, Neutron spectrometry using artificial neural networks, Radiation Measurements, 41, 425, 10.1016/j.radmeas.2005.10.003 Welcome to gplearn’s documentation, — gplearn 0.4.1 documentation. (2019). Ed. Trevor Stephens. https://gplearn.readthedocs.io/en/stable/ Wood, 1974 Wu, 2000, A neural network regression model for relative dose computation, Physics in Medicine and Biology, 45, 913, 10.1088/0031-9155/45/4/307 Yoshida, 2002, Application of neural networks for the analysis of gamma-ray spectra measured with a ge spectrometer, Nuclear Instruments and Methods in Physics Research, 484, 557, 10.1016/S0168-9002(01)01962-3 Zhang, 2006, Dosimetric validation of the mcnpx monte carlo simulation for radiobiologic studies of megavoltage grid radiotherapy, International Journal of Radiation Oncology Biology Physics, 66, 1576, 10.1016/j.ijrobp.2006.08.059