Jump to ContentJump to Main Navigation
Show Summary Details
More options …


The Journal of Instytut Chemii i Techniki Jadrowej

4 Issues per year

IMPACT FACTOR 2016: 0.760

CiteScore 2016: 0.55

SCImago Journal Rank (SJR) 2015: 0.205
Source Normalized Impact per Paper (SNIP) 2015: 0.461

Open Access
See all formats and pricing
More options …

Monte Carlo calculated CT numbers for improved heavy ion treatment planning

Sima Qamhiyeh / Anna Wysocka-Rabin
  • Corresponding author
  • Division of Accelerator Physics, National Centre for Nuclear Research (NCBJ), 7 Andrzeja Soltana Str., 05-400 Otwock/Świerk, Poland, Tel.: +48 22 718 0423, Fax: +48 22 779 3481
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Oliver Jäkel
Published Online: 2014-03-25 | DOI: https://doi.org/10.2478/nuka-2014-0002


Better knowledge of CT number values and their uncertainties can be applied to improve heavy ion treatment planning. We developed a novel method to calculate CT numbers for a computed tomography (CT) scanner using the Monte Carlo (MC) code, BEAMnrc/EGSnrc. To generate the initial beam shape and spectra we conducted full simulations of an X-ray tube, filters and beam shapers for a Siemens Emotion CT. The simulation output files were analyzed to calculate projections of a phantom with inserts. A simple reconstruction algorithm (FBP using a Ram-Lak filter) was applied to calculate the pixel values, which represent an attenuation coefficient, normalized in such a way to give zero for water (Hounsfield unit (HU)). Measured and Monte Carlo calculated CT numbers were compared. The average deviation between measured and simulated CT numbers was 4 ± 4 HU and the standard deviation σ was 49 ± 4 HU. The simulation also correctly predicted the behaviour of H-materials compared to a Gammex tissue substitutes. We believe the developed approach represents a useful new tool for evaluating the effect of CT scanner and phantom parameters on CT number values.

Keywords : X-ray tomography; Monte Carlo (MC) method; treatment planning; hadrontherapy


  • 1. Mustafa, A. A., & Jackson, D. F. (1983). The relation between X-ray CT numbers and charged particle stopping powers and its significance for radiotherapy treatment planning. Phys. Med. Biol., 28, 169-176.CrossrefPubMedGoogle Scholar

  • 2. Verhaegen, F., & Devic, S. (2005). Sensitivity study for CT images in Monte Carlo treatment planning. Phys. Med.Biol., 50, 937-946.CrossrefGoogle Scholar

  • 3. Homolka, P., Gahleitner, A., & Nowotny, R. (2002).Temperature dependence of HU values for various water equivalent phantom materials. Phys. Med. Biol., 47, 2917-2923.CrossrefPubMedGoogle Scholar

  • 4. Bhat, M., Pattison, J., Bibbo, G., & Caon, M. (1998).Diagnostic X-ray spectra: a comparison of spectra generated by different computational methods with a measured spectrum. Med. Phys., 25, 114-120.PubMedCrossrefGoogle Scholar

  • 5. Caon, M., Bibbo, G., Pattison, J., & Bhat, M. (1998). The effect on dose to computed tomography phantoms of varying the theoretical X-ray spectrum: a comparison of four diagnostic spectrum calculating codes. Med. Phys., 25, 1021-1027.PubMedCrossrefGoogle Scholar

  • 6. Ay, M. R., Sarkar, S., Shahriari, M., & Zaidi, H. (2005).Assessment of different computational models for generation of X-ray spectra in diagnostic radiology and mammography. Med. Phys., 32, 1660-1675.PubMedCrossrefGoogle Scholar

  • 7. Ay, M. R., Shahriari, M., Sarkar, S., & Zaidi, H. (2004).Monte Carlo simulation of X-ray spectra in diagnostic radiology and mammography using MCNP4C. Phys. Med.Biol., 49, 4897-4917.Google Scholar

  • 8. Atherton, J. V., & Huda, W. (1995). CT dose in cylindrical phantoms. Phys. Med. Biol., 40, 891-911.PubMedCrossrefGoogle Scholar

  • 9. Jarry, G., DeMacro, J. J., Beifuss, U., & Cagnon, C. H. (2003). A Monte Carlo-based method to estimate radiation dose from spiral CT: from phantom testing to patient- -specific models. Phys. Med. Biol., 48, 2645-2663.PubMedCrossrefGoogle Scholar

  • 10. Salvado, M., Lopez, M., Morant, J. J., & Calzado, A. (2005). Monte Carlo calculations of radiation dose in CT examination using phantom and patient tomographic models. Radiat. Prot. Dosim., 114, 364-368.CrossrefGoogle Scholar

  • 11. Tzedakis, A., & Perisnakis, K. (2006). The effect of Z overscanning on radiation burden of pediatric patients undergoing head CT with multidetector scanners: A Monte Carlo study. Med. Phys., 33(7), 2472-2478.CrossrefGoogle Scholar

  • 12. Wysocka-Rabin, A., Qamhiyeh, S., & Jäkel, O. (2011).Simulation of computed tomography (CT) images using a Monte Carlo approach. Nukleonika, 56(4), 299-304.Google Scholar

  • 13. Heismann, B. J., Leppert, J., & Stierstorfer, K. (2003).Density and atomic number measurements with spectral X-ray attenuation method. J. Appl. Phys., 94, 2073-2079.CrossrefGoogle Scholar

  • 14. Gammex-RMI. (2004). Electron density CT phantom.Catalogue. Retrieved from http://www.gammex.com/ace-files/Gammex_Catalog.pdf.Google Scholar

  • 15. Jäkel, O., Jacob, C., Schardt, D., Karger, C., & Hartmann, G. H. (2001). Relation between carbon ion ranges and X-ray CT numbers. Med. Phys., 28(4), 701-703.PubMedCrossrefGoogle Scholar

  • 16. Kawrakow, I. (2000). Accurate condensed history Monte Carlo simulation of electron transport. EGSnrc, the new EGS4 version. Med. Phys., 27, 485-498.Google Scholar

  • 17. Kawrakow, I., & Rogers, D. W. O. (2003). The EGSnrc cod system: Monte Carlo simulation of electron and photon transports. Ottawa: National Research Council of Canada. (PRIS-701).Google Scholar

  • 18. Rogers, D. W. O., Ma, C. M., Walters, B., Ding, G. X., Sheikh-Bagheri, D., & Zhang, G. (2001). BEAMnrc Users manual. Ottawa: National Research Council of Canada. (PRIS-0509(A) rev. G).Google Scholar

  • 19. Verhaegen, F. (2002). Evaluation of the EGSnrc Monte Carlo code for interference near high-Z media exposed to kilovolt and 60Co photons. Phys. Med. Biol., 47, 1691-1705.CrossrefGoogle Scholar

  • 20. Verhaegen, F., Nahum, A. E., Van de Putte, S., & Namito, Y. (1999). Monte Carlo modelling of radiotherapy kV X-ray units. Phys. Med. Biol., 44, 1767-1789.CrossrefGoogle Scholar

  • 21. Romanchikova, M. (2006). Monte Carlo Simulation des Röntgenspektrums einer computertomographischen Röntgenröhre. Unpublished Master’s thesis, University of Heidelberg, Germany.Google Scholar

  • 22. Qamhiyeh, S. (2007). A Monte Carlo study of the accuracy of CT numbers for range calculations in Carbon ion therapy. Unpublished PhD thesis, University of Heidelberg, Germany.Google Scholar

  • 23. Kachelrieß, M., & Kalender, W. (2005). Improving PET/ CT attenuation correction with iterative CT beam hardening corrections. In 2005 IEEE Nuclear Science Symposium Conference Record, 23-29 October 2005. (Vol. 4).IEEE. DOI: 10.1109/NSSMIC.2005.1596704.CrossrefGoogle Scholar

  • 24. Kachelrieß, M., Sourbelle, K., & Kalender, W. (2006). Empirical cupping corrections: a first-order raw data precorrection for cone beam computed tomography. Phys. Med.Biol., 33, 1269-1274.CrossrefGoogle Scholar

  • 25. Sennst, D. A., Kachelriess, M., Leidercker, C., Schmidt, B., Watzke, O., & Kalender, W. A. (2004). An extensible software-based platform for reconstruction and evaluation of CT images. Radiographics, 24(2), 601-613.PubMedCrossrefGoogle Scholar

  • 26. Ay, M. R., & Zaidi, H. (2005). Development and validation of MCNP4C-based Monte Carlo simulator for fan and cone beam X-ray CT. Phys. Med. Biol., 50, 4863-3885.Google Scholar

  • 27. Qamhiyeh, S., Wysocka-Rabin, A., Ellerbrock, M., & Jäkel, O. (2007). Effect of voltage of CT scanner, phantom size and phantom material on CT calibration and carbon range.Radiother. Oncol., 84(S1), S232.Google Scholar

  • 28. Bazalova, M., Carrier, J. F., Beaulieu, L., & Verhaegen, F. (2008). Tissue segmentation in Monte Carlo treatment planning: a simulation study using dual-energy CT images.Radiother. Oncol., 86(1), 93-98.CrossrefPubMedGoogle Scholar

  • 29. Hünemohr, N., Krauss, B., Dinkel, J., Gillmann, C., Ackermann, B., Jäkel, O., & Greilich, S. (2013). Ion range estimation by using dual energy computed tomography. Z. Med. Phys., 23(4), 300-313.CrossrefGoogle Scholar

  • 30. Wysocka-Rabin, A. (2013) Advances in conformal radiotherapy using Monte Carlo Code to design new IMRT and IORT Accelerators and interpret CT numbers. (CERN- -WUT Editorial series on “Accelerator Science”. Vol. 17). Warsaw: Institute of Electronic Systems, Warsaw University of Technology. Google Scholar

About the article

Published Online: 2014-03-25

Published in Print: 2014-03-01

Citation Information: Nukleonika, Volume 59, Issue 1, Pages 15–23, ISSN (Online) 0029-5922, DOI: https://doi.org/10.2478/nuka-2014-0002.

Export Citation

This content is open access.

Comments (0)

Please log in or register to comment.
Log in