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Monte Carlo simulations for the evaluation of various influence factors on projections in computed tomography

Published online by Cambridge University Press:  29 February 2012

B. Chyba ,
M. Mantler  and
M. Reiter
B. Chyba
Affiliation:
Technische Universität Wien, Vienna, Austria
M. Mantler*
Affiliation:
Technische Universität Wien, Vienna, Austria
M. Reiter
Affiliation:
Fachhochschule Wels, Wels, Austria
*
a)Author to whom correspondence should be addressed. Also at Vienna University of Technology, Wiedner Hauptstrasse 8-10/138 A, 1040 Vienna, Austria. Electronic mail: [email protected]
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Abstract

This paper presents Monte Carlo simulations considering all stages of the creation process of two-dimensional projections in a computed tomography (CT) device: excitation of angle dependent X-ray spectra within the X-ray tube using results from a previous study [Chyba et al. (2008). Powder Diffr. 23, 150–153]; interaction of these X-rays and secondary photoelectrons with a simple inhomogeneous sample; and interaction of X-rays and photoelectrons with the components (thin layers) of a matrix scintillation detector. The simulations were carried out by using custom software running on up to 50 nodes of a computer cluster. Comparative calculations were also made by using the software package MCNP [Booth et al. (2003). MCNP—A general Monte Carlo N-particle transport code, Report LAUR 03-1987, Los Alamos National Laboratory, Los Alamos, NM]. Tube spectra were calculated with algorithms proposed by Ebel [(2006). Adv. X-Ray Anal. 49, 267–273]. Measurements for the chosen setup made with an available CT device were in relatively good agreement with calculated results. It was shown that good knowledge of the tube spectra is of importance, but most differences between resulting projections and measurements are caused by uncertainties concerning detector response due to light yield of the scintillator and to internal scattering effects within the thin detector layers which lead to spreading of a detected point signal within the detector matrix into neighboring matrix elements.

Keywords

computed tomographyMonte Carlo simulationdetector modeling
Type
Technical Articles
Information
Powder Diffraction , Volume 25 , Issue 2 , June 2010 , pp. 165 - 168
Copyright
Copyright © Cambridge University Press 2010

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References

Booth, T. E., Brown, F. B., Bull, J. S., Forster, R. A., Goorley, J. T., Hughes, H. G., Mosteller, R. D., Prael, R. E., Sood, A., Sweezy, J. E., Zukaitis, A., Marsha Boggs, M., and Roger Martz, R. (2003). MCNP—A general Monte Carlo N-particle transport code, Report LAUR 03-1987, Los Alamos National Laboratory, Los Alamos, NM.Google Scholar
Chyba, B., Mantler, M., and Reiter, M. (2008). “Monte-Carlo simulation of projections in computed tomography,” Powder Diffr. PODIE2 23, 150–153.10.1154/1.2919045CrossRefGoogle Scholar
Ebel, H. (2006). “Fundamental parameter programs: Algorithms for the description of K, L, and M spectra of X-ray tubes,” Adv. X-Ray Anal. AXRAAA 49, 267–273.Google Scholar
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