Monte Carlo calculated CT numbers for improved heavy ion treatment planning

Open access


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.


  • 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.

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

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

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

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 14. Gammex-RMI. (2004). Electron density CT phantom.Catalogue. Retrieved from

  • 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.

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

  • 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).

  • 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).

  • 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.

  • 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.

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 Unpublished Master’s thesis, University of Heidelberg, Germany.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.


The Journal of Instytut Chemii i Techniki Jadrowej

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IMPACT FACTOR 2016: 0.760

CiteScore 2016: 0.55

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