A Prediction Study on Bremsstrahlung Photon Flux of Tungsten as a Radiological Anode Material by using MCNPX and ANN Modeling

H.O. Tekina,b,c, U. Kara b, T. Manici c, E.E. Altunsoy d, T.T. Erguzel e aUskudar University, Vocational School of Health Services, Radiotherapy Department, Istanbul, Turkey bSuleyman Demirel University, Vocational School of Health Service, Medical Imaging Department, Isparta, Turkey cUskudar University, Medical Radiation Research Center (USMERA), Istanbul, Turkey dUskudar University, Vocational School of Health Services, Medical Imaging Department, Istanbul, Turkey eUskudar University, Faculty of Engineering and Natural Sciences, Computer Engineering, Istanbul, Turkey

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Medical imaging is a technique that is mostly known as visual representations of the parts of body for clinical scans and analysis. In imaging process for medical purpose there take part radiologists, radiographers/radiology technicians, medical physicists, sonographers, nurses, and engineers. As an apart issue from the medical imaging devices, we can treat X-rays using devices such as radiography, computed tomography, fluoroscopy, dental cone-beam computed tomography, and mammography. All these devices are to perform: X-ray using during medical imaging process. An X-ray beam is generated in a vacuum tube that is principally composed of an anode and a cathode material to produce X-ray beams, whose name is X-ray tube. The anode represents the component in which the X-ray beam produced that made from a piece of metal. For decades, tungsten (W) has been used as an anode material of various X-ray tubes. Tungsten has high atomic number and high melting point of 3370°C with low rate of volatilization. In this study, we performed Monte Carlo simulation for flux calculations of W target by using MCNP-X general purpose code and considered result as a data set for artificial neural network. It can be concluded that the results agreed well between Monte Carlo simulation and artificial neural network prediction.

DOI: 10.12693/APhysPolA.132.433 topics: artificial neural network, Monte Carlo, medical imaging