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Monte Carlo calculated CT numbers for improved heavy ion treatment planning Cover

Monte Carlo calculated CT numbers for improved heavy ion treatment planning

Nukleonika
Volume 59 (2014): Issue 1 (March 2014)
By: ,   and    
Open Access
|Mar 2014
References

References

  1. 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.
    Open DOISearch in Google Scholar
  2. 2. Verhaegen, F., & Devic, S. (2005). Sensitivity study for CT images in Monte Carlo treatment planning. Phys. Med.Biol., 50, 937-946.
    Open DOISearch in Google Scholar
  3. 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.
    Open DOISearch in Google Scholar
  4. 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.
    Open DOISearch in Google Scholar
  5. 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.
    Open DOISearch in Google Scholar
  6. 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.
    Open DOISearch in Google Scholar
  7. 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.
    Open DOISearch in Google Scholar
  8. 8. Atherton, J. V., & Huda, W. (1995). CT dose in cylindrical phantoms. Phys. Med. Biol., 40, 891-911.
    Open DOISearch in Google Scholar
  9. 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.
    Open DOISearch in Google Scholar
  10. 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.
    Open DOISearch in Google Scholar
  11. 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.
    Open DOISearch in Google Scholar
  12. 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.
    Search in Google Scholar
  13. 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.
    Open DOISearch in Google Scholar
  14. 14. Gammex-RMI. (2004). Electron density CT phantom.Catalogue. Retrieved from http://www.gammex.com/ace-files/Gammex_Catalog.pdf.
    Search in Google Scholar
  15. 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.
    Open DOISearch in Google Scholar
  16. 16. Kawrakow, I. (2000). Accurate condensed history Monte Carlo simulation of electron transport. EGSnrc, the new EGS4 version. Med. Phys., 27, 485-498.
    Open DOISearch in Google Scholar
  17. 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).
    Search in Google Scholar
  18. 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).
    Search in Google Scholar
  19. 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.
    Open DOISearch in Google Scholar
  20. 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.
    Open DOISearch in Google Scholar
  21. 21. Romanchikova, M. (2006). Monte Carlo Simulation des Röntgenspektrums einer computertomographischen Röntgenröhre. Unpublished Master’s thesis, University of Heidelberg, Germany.
    Search in Google Scholar
  22. 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.
    Search in Google Scholar
  23. 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.
    Open DOISearch in Google Scholar
  24. 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.
    Open DOISearch in Google Scholar
  25. 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.
    Open DOISearch in Google Scholar
  26. 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.
    Open DOISearch in Google Scholar
  27. 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.
    Search in Google Scholar
  28. 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.
    Open DOISearch in Google Scholar
  29. 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.
    Open DOISearch in Google Scholar
  30. 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.
    Search in Google Scholar
DOI: https://doi.org/10.2478/nuka-2014-0002 | Journal eISSN: 1508-5791 (formerly 0029-5922) | Journal ISSN: 0029-5922
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Language: English
Page range: 15 - 23
Published on: Mar 25, 2014
Published by: Institute of Nuclear Chemistry and Technology
In partnership with: Paradigm Publishing Services
Keywords:
X-ray tomography,
Monte Carlo (MC) method,
treatment planning,
hadrontherapy
Related subjects:
Chemistry,
Nuclear chemistry

© 2014 Sima Qamhiyeh, Anna Wysocka-Rabin, Oliver Jäkel, published by Institute of Nuclear Chemistry and Technology
This work is licensed under the Creative Commons License.

Volume 59 (2014): Issue 1 (March 2014)
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