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2014 | 59 | 1 | 15-23
Tytuł artykułu

Monte Carlo calculated CT numbers for improved heavy ion treatment planning

Treść / Zawartość
Warianty tytułu
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.
Wydawca

Czasopismo
Rocznik
Tom
59
Numer
1
Strony
15-23
Opis fizyczny
Daty
wydano
2014-03-01
online
2014-03-25
Twórcy
  • West German Proton Therapy Centre Essen (WPE), Hufelandstraße 55, 45147 Essen, Germany
  • 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, anna.wysocka@ncbj.gov.pl
  • Heidelberg Ion-Beam Therapy Centre HIT, Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
Bibliografia
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  • 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.[Crossref][PubMed]
  • 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.[PubMed][Crossref]
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  • 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.[PubMed][Crossref]
  • 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.
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  • 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.[PubMed][Crossref]
  • 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.[Crossref]
  • 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.[Crossref]
  • 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.[Crossref]
  • 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.[PubMed][Crossref]
  • 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.[Crossref]
  • 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.[Crossref]
  • 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.[Crossref]
  • 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.[Crossref]
  • 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.[PubMed][Crossref]
  • 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.[Crossref][PubMed]
  • 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.[Crossref]
  • 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.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.-psjd-doi-10_2478_nuka-2014-0002
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