Preferences help
enabled [disable] Abstract
Number of results
2017 | 132 | 3 | 433-435
Article title

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

Title variants
Languages of publication
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.
Physical description
  • [1] E. Massoud, H.M. Diab, J. Cell Sci. Ther. 5, 155 (2014), doi: 10.4172/2157-7013.1000155
  • [2] U. Kara, H.O. Tekin, Conf. Proc. 1, 195 (2016), doi: 10.21175/RadProc.2016.46
  • [3] H.O. Tekin, Sci. Technol., Nucl. Install. 2016, 6547318 (2016), doi: 10.1155/2016/6547318
  • [4] I. Akkurt, H.O. Tekin, A. Mesbahi, Acta. Phys. Pol. A 128, B-332 (2015), doi: 10.12693/APhysPolA.128.B-332
  • [5] H.O. Tekin, U. Kara, J. Commun. Comput. 13, 32 (2016), doi: 10.17265/1548-7709/2016.01.005
  • [6] U. Kara, A. Mesbahi, I. Akkurt, Acta. Phys. Pol. A 128, B-378 (2015), doi: 10.12693/APhysPolA.128.B-378
  • [7] N. Demir, Z.N. Kuluozturk, I. Akkurt, Acta. Phys. Pol. A 128, B-443 (2015), doi: 10.12693/APhysPolA.128.B-443
  • [8] H.O. Tekin, V.P. Singh, T. Manici, J. Polytechn. 19, 617 (2016), doi: 10.2339/2016/19.4.617-622
  • [9] H.O. Tekin, V.P. Singh, T. Manici, Appl. Radiat. Isotop. 121, 122 (2017), doi: 10.1016/j.apradiso.2016.12.040
  • [10] H.O. Tekin, T. Manici, Nucl. Sci. Techn. 28 , 95 (2017), doi: 10.1007/s41365-017-0253-4
  • [11] J. Schmidhuber, Neural Networks 61, 85 (2015), doi: 10.1016/j.neunet.2014.09.003
  • [12] RSICC Computer Code Collection, MCNPX User's Manual, Version 2.4.0., (2002), Monte Carlo N-Particle Transport Code System for Multiple and High Energy Applications
  • [13] I. Akkurt, K. Gunoglu, H.O. Tekin, Z.N. Demirci, G. Yegin, N. Demir, Iran. J. Rad. Res. 10, 63 (2011)
Document Type
Publication order reference
YADDA identifier
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.