Monte Carlo calculated CT numbers for improved heavy ion treatment planning

• Autorzy: Qamhiyeh S. , Wysocka-Rabin A. , Jäkel O.

Identyfikatory

DOI

10.2478/nuka-2014-0002

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

• Wydawca: Institute of Nuclear Chemistry and Technology
• Czasopismo: Nukleonika
• Rocznik: 2014
• Tom: Vol. 59, No. 1
• Strony: 15--23
• Opis fizyczny: Bibliogr. 30 poz., rys.

Twórcy

autor

Qamhiyeh S.

• West German Proton Therapy Centre Essen (WPE), Hufelandstraße 55, 45147 Essen, Germany

autor

Wysocka-Rabin A.

• Division of Accelerator Physics, National Centre for Nuclear Research (NCBJ), 7 Andrzeja Soltana Str., 05-400 Otwock/Świerk, Poland, Tel.: [phone], Fax: [phone]

autor

Jäkel O.

• Heidelberg Ion-Beam Therapy Centre HIT, Im Neuenheimer Feld 450, 69120 Heidelberg, Germany

Bibliografia

• 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 http://www.gammex.com/ace-files/Gammex_Catalog.pdf.
• 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.