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A Monte Carlo investigation of low-Z target image quality generated in a
linear accelerator using Varian's VirtuaLinac
2. E. J. Orton and J. L. Robar, “Megavoltage image contrast with low-atomic number target materials and amorphous silicon electronic portal imagers,” Phys. Med. Biol. 54(5), 1275–1289 (2009).
3. T. Connell and J. L. Robar, “Low-Z target optimization for spatial resolution improvement in megavoltage imaging,” Med. Phys. 37(1), 124–131 (2010).
4. A. Tsechanski, A. F. Bielajew, S. Faermann, and Y. Krutman, “A thin target approach for portal imaging in medical accelerators,” Phys. Med. Biol. 43(8), 2221–2236 (1998).
5. D. Parsons and J. L. Robar, “Beam generation and planar imaging at energies below 2.40 MeV with carbon and aluminum linear accelerator targets,” Med. Phys. 39(7), 4568–4578 (2012).
6. D. Parsons and J. L. Robar, “The effect of copper conversion plates on low-Z target image quality,” Med. Phys. 39(9), 5362–5371 (2012).
7. S. Flampouri, P. M. Evans, F. Verhaegen, A. E. Nahum, E. Spezi, and M. Partridge, “Optimization of accelerator target and detector for portal imaging using Monte Carlo simulation and experiment,” Phys. Med. Biol. 47(18), 3331–3349 (2002).
8. D. A. Roberts, V. N. Hansen, A. C. Niven, M. G. Thompson, J. Seco, and P. M. Evans, “A low Z linac and flat panel imager: Comparison with the conventional imaging approach,” Phys. Med. Biol. 53(22), 6305–6319 (2008).
9. D. A. Roberts et al., “Kilovoltage energy imaging with a radiotherapy linac with a continuously variable energy range,” Med. Phys. 39(3), 1218–1226 (2012).
10. O. Z. Ostapiak, P. F. O’Brien, and B. A. Faddegon, “Megavoltage imaging with low Z targets: Implementation and characterization of an investigational system,” Med. Phys. 25(10), 1910–1918 (1998).
11. B. A. Faddegon, V. Wu, J. Pouliot, B. Gangadharan, and A. Bani-Hashemi, “Low dose megavoltage cone beam computed tomography with an unflattened 4 MV beam from a carbon target,” Med. Phys. 35(12), 5777–5786 (2008).
13. D. W. Rogers, B. A. Faddegon, G. X. Ding, C. M. Ma, J. We, and T. R. Mackie, “BEAM: A Monte Carlo code to simulate radiotherapy treatment units,” Med. Phys. 22(5), 503–524 (1995).
15. M. Constantin, D. E. Constantin, P. J. Keall, A. Narula, M. Svatos, and J. Perl, “Linking computer-aided design (CAD) to Geant4-based Monte Carlo simulations for precise implementation of complex treatment head geometries,” Phys. Med. Biol. 55(8), N211–N220 (2010).
16. M. Constantin et al., “Modeling the TrueBeam linac using a CAD to Geant4 geometry implementation: Dose and IAEA-compliant phase space calculations,” Med. Phys. 38(7), 4018–4024 (2011).
17. E. Gete et al., “A Monte Carlo approach to validation of FFF VMAT treatment plans for the TrueBeam linac,” Med. Phys. 40(2), 021707 (13pp.) (2013).
18. INDC International Nuclear Data Committee, Phase-Space Database for External Beam Radiotherapy Summary Report of a Consultants’ Meeting (International Atomic Energy Agency, Vienna, Austria, 2006).
20. Z. Chang et al., “Commissioning and dosimetric characteristics of TrueBeam system: Composite data of three TrueBeam machines,” Med. Phys. 39(11), 6981–7018 (2012).
21. C. M. Ma and D. W. O. Rogers, BEAMdp Users Manual NRCC Report PIRS-0509(C)revA (NRCC, Ottawa, Canada, 2009).
22. S. Lang, J. Hrbacek, A. Leong, and S. Klöck, “Ion-recombination correction for different ionization chambers in high dose rate flattening-filter-free photon beams,” Phys. Med. Biol. 57(9), 2819–2827 (2012).
23. I. Kawrakow, “Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version,” Med. Phys. 27(3), 485–498 (2000).
24. P. Munro and D. C. Bouius, “X-ray quantum limited portal imaging using amorphous silicon flat-panel arrays,” Med. Phys. 25(5), 689–702 (1998).
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The focus of this work was the demonstration and validation of VirtuaLinac with
beams and to
investigate the implementation of low-Z targets in a TrueBeam linear accelerator
VirtuaLinac, a cloud based web application utilizing Geant4 Monte Carlo code, was used to
treatment head components. Particles were propagated through the lower portion of the
treatment head using BEAMnrc. Dose distributions and spectral distributions were
calculated using DOSXYZnrc and BEAMdp, respectively. For validation, 6 MV flattened and
flattening filter free (FFF) photon
generated and compared to measurement for square fields, 10 and 40 cm wide and at
dmax for diagonal profiles. Two low-Z targets were investigated: a 2.35 MeV
and the proposed 2.50 MeV commercial imaging target for the TrueBeam platform. A 2.35 MeV
was also simulated in a 2100EX Clinac using BEAMnrc. Contrast simulations were
made by scoring the dose in the phosphor layer of an IDU20 aSi detector after propagating through a 4
or 20 cm thick phantom composed of water and ICRP bone.
Measured and modeled depth dose curves for 6 MV flattened and FFF beams agree within 1% for
98.3% of points at depths greater than 0.85 cm. Ninety three percent or greater of
points analyzed for the diagonal profiles had a gamma value less than one for the
criteria of 1.5 mm and 1.5%. The two low-Z target photon spectra produced in
TrueBeam are harder than that from the carbon target in the Clinac. Percent dose at depth 10 cm
is greater by 3.6% and 8.9%; the fraction of photons in the diagnostic energy range (25–150 keV) is
lower by 10% and 28%; and contrasts are lower by factors of 1.1 and 1.4 (4 cm thick phantom)
and 1.03 and 1.4 (20 cm thick phantom), for the TrueBeam 2.35 MV/carbon and commercial
VirtuaLinac is a promising new tool for Monte Carlo
novel target designs. A significant spectral difference is observed between the low-Z
target beam on
the Clinac platform and the proposed imaging
beam line on
TrueBeam, with the former providing greater diagnostic energy content.
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