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TOPAS: An innovative proton Monte Carlo platform for research and clinical applications
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1. H. Paganetti, “Range uncertainties in proton therapy and the role of Monte Carlo simulations,” Phys. Med. Biol. 57, R99R117 (2012).
2. M. Fippel and M. Soukup, “A Monte Carlo dose calculation algorithm for proton therapy,” Med. Phys. 31, 22632273 (2004).
3. L. Waters, “MCNPX user's manual,” Los Alamos National Laboratory, 2002.
4. A. Ferrari, P. R. Sala, A. Fasso, and J. Ranft, “FLUKA: A multi-particle transport code,” CERN Yellow Report No. CERN 2005-10; INFN/TC 05/11, SLAC-R-773 (CERN, Geneva, 2005).
5. S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce, M. Asai, D. Axen, S. Banerjee, G. Barrand, F. Behner, L. Bellagamba, J. Boudreau, L. Broglia, A. Brunengo, H. Burkhardt, S. Chauvie, J. Chuma, R. Chytracek, G. Cooperman, G. Cosmo, P. Degtyarenko, A. Dell’Acqua, G. Depaola, D. Dietrich, R. Enami, A. Feliciello, C. Ferguson, H. Fesefeldt, G. Folger, F. Foppiano, A. Forti, S. Garelli, S. Giani, R. Giannitrapani, D. Gibin, J. J. Gomez Cadenas, I. Gonzalez, G. Gracia Abril, G. Greeniaus, W. Greiner, V. Grichine, A. Grossheim, S. Guatelli, P. Gumplinger, R. Hamatsu, K. Hashimoto, H. Hasui, A. Heikkinen, A. Howard, V. Ivanchenko, A. Johnson, F. W. Jones, J. Kallenbach, N. Kanaya, M. Kawabata, Y. Kawabata, M. Kawaguti, S. Kelner, P. Kent, A. Kimura, T. Kodama, R. Kokoulin, M. Kossov, H. Kurashige, E. Lamanna, T. Lampen, V. Lara, V. Lefebure, F. Lei, M. Liendl, W. Lockman, F. Longo, S. Magni, M. Maire, E. Medernach, K. Minamimoto, P. Mora de Freitas, Y. Morita, K. Murakami, M. Nagamatu, R. Nartallo, P. Nieminen, T. Nishimura, K. Ohtsubo, M. Okamura, S. O’Neale, Y. Oohata, K. Paech, J. Perl, A. Pfeiffer, M. G. Pia, F. Ranjard, A. Rybin, S. Sadilov, E. Di Salvo, G. Santin, T. Sasaki, N. Savvas, Y. Sawada, S. Scherer, S. Sei, V. Sirotenko, D. Smith, N. Starkov, H. Stoecker, J. Sulkimo, M. Takahata, S. Tanaka, E. Tcherniaev, E. Safai Tehrani, M. Tropeano, P. Truscott, H. Uno, L. Urban, P. Urban, M. Verderi, A. Walkden, W. Wander, H. Weber, J.P. Wellisch, T. Wenaus, D. C. Williams, D. Wright, T. Yamada, H. Yoshida, and D. Zschiesche, “Geant4: A simulation toolkit,” Nucl. Instrum. Methods Phys. Res. A 506, 250303 (2003).
6. J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce Dubois, M. Asai, G. Barrand, R. Capra, S. Chauvie, R. Chytracek, G. A. P. Cirrone, G. Cooperman, G. Cosmo, G. Cuttone, G. G. Daquino, M. Donszelmann, M. Dressel, G. Folger, F. Foppiano, J. Generowicz, V. Grichine, S. Guatelli, P. Gumplinger, A. Heikkinen, I. Hrivnacova, A. Howard, S. Incerti, V. Ivanchenko, T. Johnson, F. Jones, T. Koi, R. Kokoulin, M. Kossov, H. Kurashige, V. Lara, S. Larsson, F. Lei, O. Link, F. Longo, M. Maire, A. Mantero, B. Mascialino, I. McLaren, P. Mendez Lorenzo, K. Minamimoto, K. Murakami, P. Nieminen, L. Pandola, S. Parlati, L. Peralta, J. Perl, A. Pfeiffer, M. G. Pia, A. Ribon, P. Rodrigues, G. Russo, S. Sadilov, G. Santin, T. Sasaki, D. Smith, N. Starkov, S. Tanaka, E. Tcherniaev, B. Tomé, A. Trindade, P. Truscott, L. Urban, M. Verderi, A. Walkden, J. P. Wellisch, D. C. Williams, D. Wright, and H. Yoshida, “Geant4: Developments and Applications,” IEEE Trans. Nucl. Sci. 53, 270278 (2006).
7. R. Gotta, “Monte Carlo simulation with Geant4 of a 3D dosimeter for therapeutical proton beams,” MS thesis, University of Torino, 1999.
8. H. Paganetti and B. Gottschalk, “Test of Geant3 and Geant4 nuclear models for 160 MeV protons stopping in CH2,” Med. Phys. 30, 19261931 (2003).
9. D. W. O. Rogers, B. A. Faddegon, G. X. Ding, C.-M. Ma, J. Wei, and T. R. Mackie, “BEAM: A Monte Carlo code to simulate radiotherapy treatment units,” Med. Phys. 22, 503524 (1995).
10. I. Kawrakow, “Accurate condensed history Monte Carlo simulation of electron transport: I. EGSnrc, the new EGS4 version,” Med. Phys. 27, 485498 (2000).
11.OPERA-3D Reference Manual,” Vector Fields Limited, Oxford, England, 2004.
12. J. Schümann, H. Paganetti, J. Shin, B. Faddegon, and J. Perl, “Efficient voxel navigation for proton therapy dose calculation in TOPAS and Geant4,” Phys. Med. Biol. 57, 32813293 (2012).
13. K. L. Brown and R. V. Servranckx, “First- and second-order charged particle optics,” SLAC Report No. SLAC-PUB-3381 (Stanford Linear Accelerator Center, Menlo Park, California, 1984).
14. International Atomic Energy Agency, “Phase-space database for external beam radiotherapy,” IAEA Technical Report No. INDC (NDS)-0484 (IAEA, Vienna, Austria, 2006).
15. C. Zacharatou Jarlskog and H. Paganetti, “Physics settings for using the Geant4 toolkit in proton therapy,” IEEE Trans. Nucl. Sci. 55, 10181025 (2008).
16. L. Urban, “Multiple scattering model in Geant4,” CERN Report No. CERN-OPEN-2002-070 (CERN, Geneva, 2002).
17. H. W. Lewis, “Multiple scattering in an infinite medium,” Phys. Rev. 78, 526529 (1950).
18. B. A. Faddegon, I. Kawrakow, Y. Kubyshin, J. Perl, J. Sempau, and L. Urban, “The accuracy of EGSnrc, Geant4 and PENELOPE Monte Carlo systems for the simulation of electron scatter in external beam radiotherapy,” Phys. Med. Biol. 54, 61516163 (2009).
19. J. F. Janni, “Proton range energy tables, 1 keV–10 GeV,” At. Data Nucl. Data Tables 27, 147529 (1982).
20. H. Bichsel and T. Hiraoka, “Energy loss of 70 MeV protons in elements,” Nucl. Instrum. Methods Phys. Res. B 66, 345351 (1992).
21. ICRU, “Stopping powers and ranges for protons and alpha particles,” ICRU Report No. 49 (International Commission on Radiation Units and Measurements, Bethesda, MD, 1993).
22. Y. Kumazaki, T. Akagi, T. Yanou, D. Suga, Y. Hishikawa, and T. Teshima, “Determination of the mean excitation energy of water from proton beam ranges,” Radiat. Meas. 42, 16831691 (2007).
23. P. Andreo, “On the clinical spatial resolution achievable with protons and heavier charged particle radiotherapy beams,” Phys. Med. Biol. 54, N205N215 (2009).
24. J. Herault, N. Iborra, B. Serrano, and P. Chauvel, “Monte Carlo simulation of a proton therapy platform devoted to ocular melanoma,” Med. Phys. 32, 910919 (2005).
25. A. Stankovskiy, S. Kerhoas-Cavata, R. Ferrand, C. Nauraye, and L. Demarzi, “Monte Carlo modelling of the treatment line of the Proton Therapy Center in Orsay,” Phys. Med. Biol. 54, 23772394 (2009).
26. C. Grassberger, A. Trofimov, A. Lomax, and H. Paganetti, “Variations in linear energy transfer within clinical proton therapy fields and the potential for biological treatment planning,” Int. J. Radiat. Oncol. Biol. Phys. 80, 15591566 (2011).
27. C. Grassberger and H. Paganetti, “Elevated LET components in clinical proton beams,” Phys. Med. Biol. 56, 66776691 (2011).
28. G. Barrand, T. Johnson, and A. Pfeiffer, “AIDA abstract interfaces for data analysis,” 2001 [available URL: http://aida.freehep.org/].
29. R. Brun and F. Rademakers, “ROOT: An object oriented data analysis framework,” Nucl. Instrum. Methods. 389, 8186 (1996).
30. F. Sommerer, F. Cerutti, K. Parodi, A. Ferrari, W. Enghardt, and H. Aiginger, “In-beam PET monitoring of mono-energetic (16)O and (12)C beams: Experiments and FLUKA simulations for homogeneous targets,” Phys. Med. Biol. 54, 39793996 (2009).
31. J. C. Polf, S. Peterson, M. McCleskey, B. T. Roeder, A. Spiridon, S. Beddar, and L. Trache, “Measurement and calculation of characteristic prompt gamma ray spectra emitted during proton irradiation,” Phys. Med. Biol. 54, N519N527 (2009).
32. M. Moteabbed, S. Espana, and H. Paganetti, “Monte Carlo patient study on the comparison of prompt gamma and PET imaging for range verification in proton therapy,” Phys. Med. Biol. 56, 10631082 (2011).
33. T. Li, Z. Liang, J. V. Singanallur, T. J. Satogata, D. C. Williams, and R. W. Schulte, “Reconstruction for proton computed tomography by tracing proton trajectories: A Monte Carlo study,” Med. Phys. 33, 699706 (2006).
34. C. Z. Jarlskog and H. Paganetti, “Sensitivity of different dose scoring methods on organ specific neutron doses calculations in proton therapy,” Phys. Med. Biol. 53, 45234532 (2008).
35. J. Allison, M. Asai, G. Barrand, M. Donszelmann, K. Minamimoto, J. Perl, S. Tanaka, E. Tcherniaev, and J. Tinslay, “The Geant4 visualisation system,” Comput. Phys. Commun. 178, 331365 (2008).
36. J. Perl, “HepRApp: The original HepRep data browsing application,” 2007 [available URL: http://www.slac.stanford.edu/~perl/HepRApp].
37. A. Saitoh, A. Kimura, S. Tanaka, and T. Sasaki, “gMocren: High-quality volume visualization tool for Geant4 simulation,” Nuclear Science Symposium Conference Record, NSS ‘07 (IEEE, Honolulu, Hawaii, 2007), Vol. 1, pp. 888891.
38. T. Aso, T. Yamashita, T. Akagi, S. Kameoka, T. Nishio, K. Murakami, C. Omachi, T. Ssasaki, K. Amako, A. Kimura, H. Yoshida, H. Kurashige, and M. Kaburagi, “Validation of PTSIM for clinical usage,” in Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), Knoxville, Tennessee, 2010 (IEEE, New York, New York, 2010), pp. 158160.
39. T. Aso, A. Kimura, S. Tanaka, H. Yoshida, N. Kanematsu, T. Sasaki, and T. Akagi, “Verification of the dose distributions with GEANT4 simulation for proton therapy,” IEEE Trans. Nucl. Sci. 52, 896901 (2005).
40. J. Shin, J. Perl, J. Schümann, H. Paganetti, and B. Faddegon, “A consistent, modular framework to handle multiple time-dependent quantities in Monte Carlo simulations as implemented in TOPAS,” Phys. Med. Biol. 57, 32953308 (2012).
41. H. M. Lu and H. Kooy, “Optimization of current modulation function for proton spread-out Bragg peak fields,” Med. Phys. 33, 12811287 (2006).
42. H. Paganetti, H. Jiang, S.-Y. Lee, and H. Kooy, “Accurate Monte Carlo for nozzle design, commissioning, and quality assurance in proton therapy,” Med. Phys. 31, 21072118 (2004).
43. H. Paganetti, H. Jiang, K. Parodi, R. Slopsema, and M. Engelsman, “Clinical implementation of full Monte Carlo dose calculation in proton beam therapy,” Phys. Med. Biol. 53, 48254853 (2008).
44. B. Clasie, A. Wroe, H. Kooy, N. Depauw, J. Flanz, H. Paganetti, and A. Rosenfeld, “Assessment of out-of-field absorbed dose and equivalent dose in proton fields,” Med. Phys. 37, 311321 (2010).
45. B. Gottschalk, R. Platais, and H. Paganetti, “Nuclear interactions of 160 MeV protons stopping in copper: A test of Monte Carlo nuclear models,” Med. Phys. 26, 25972601 (1999).
46. H. Paganetti, “Monte Carlo calculations for absolute dosimetry to determine output factors for proton therapy treatments,” Phys. Med. Biol. 51, 28012812 (2006).
47. S. W. Peterson, J. Polf, M. Bues, G. Ciangaru, L. Archambault, S. Beddar, and A. Smith, “Experimental validation of a Monte Carlo proton therapy nozzle model incorporating magnetically steered protons,” Phys. Med. Biol. 54, 32173229 (2009).
48. I. K. Daftari, T. R. Renner, L. J. Verhey, R. P. Singh, M. Nyman, P. L. Petti, and J. R. Castro, “New UCSF proton ocular beam facility at the Crocker Nuclear Laboratory Cyclotron (UC Davis),” Nucl. Instrum. Methods Phys. Res. A 380, 597612 (1996).
49. W. T. Chu, B. A. Ludewig, and T. R. Renner, “Instrumentation for treatment of cancer using proton and light-ion beams,” Rev. Sci. Instrum. 64, 20552122 (1993).
50. J. Daartz, M. Engelsman, H. Paganetti, and M. R. Bussiere, “Field size dependence of the output factor in passively scattered proton therapy: Influence of range, modulation, air gap, and machine settings,” Med. Phys. 36, 32053210 (2009).
51. B. Bednarz, H. M. Lu, M. Engelsman, and H. Paganetti, “Uncertainties and correction methods when modeling passive scattering proton therapy treatment heads with Monte Carlo,” Phys. Med. Biol. 56, 28372854 (2011).
52. H. M. Kooy, B. M. Clasie, H. M. Lu, T. M. Madden, H. Bentefour, N. Depauw, J. A. Adams, A. V. Trofimov, D. Demaret, T. F. Delaney, and J. B. Flanz, “A case study in proton pencil-beam scanning delivery,” Int. J. Radiat. Oncol., Biol., Phys. 76, 624630 (2010).
53. L. Hong, M. Goitein, M. Bucciolini, R. Comiskey, B. Gottschalk, S. Rosenthal, C. Serago, and M. Urie, “A pencil beam algorithm for proton dose calculations,” Phys. Med. Biol. 41, 13051330 (1996).
View: Figures


Image of FIG. 1.

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FIG. 1.

TOPAS application uses and extends the standard Geant4 simulation toolkit. The only element that the user needs to write is the user parameter file, a simple text file that controls the simulation. The user parameter file may in turn include additional parameter files that the user may write or may obtain from other users at their own institution, from colleagues at other institutions, or from hardware vendors.

Image of FIG. 2.

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FIG. 2.

By the include mechanism, the UserFile pulls in additional parameters defined in the OtherFile which in turn pulls in parameters defined in the DefaultFile. If the same parameter is in more than one file, the value from UserFile overrides the value from the OtherFile, the value from the OtherFile overrides the DefaultFile.

Image of FIG. 3.

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FIG. 3.

Multiple chains of parameter files. The UserFile pulls in parameters from patient, gantry and imager files. Values from the UserFile override values from the other files.

Image of FIG. 4.

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FIG. 4.

The treatment head for scanning beam delivery at MGH shown at four different times during a scan. The proton trajectories through the treatment head are shown along with the much shorter delta ray tracks.

Image of FIG. 5.

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FIG. 5.

UCSF proton beam line used for eye treatment as built in TOPAS. Shown are the exit window (X), wire chamber (WC), ion chambers (IC), rotating propeller (Prop), collimators (Coll), the position of the water column (H2O). The proton trajectories through the treatment head are shown along with the much shorter delta ray tracks.

Image of FIG. 6.

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FIG. 6.

The TOPAS parameter chain for UCSF eye treatment simulation. Default_BeamLine parameter file includes initial beam characteristics and all component description except rotating propellers, which are implemented in separate parameter files, i.e., Propeller_10, Propeller_15, Propeller_20, and Propeller_24. A user parameter file for SOBP simulation needs to include Default_BeamLine and one of those propeller implementations while a user parameter file Bragg peak simulation needs only Default_BeamLine.

Image of FIG. 7.

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FIG. 7.

Water tank expanding over time to facilitate measurement of Bragg peak. The thicknesses are 0.01, 1.0, 1.7, and 5.0 cm, respectively, and the water phantom expands at 5 mm/s in one direction.

Image of FIG. 8.

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FIG. 8.

Time versus average kinetic energy of primary protons at four positions along the beam path; PS1 at downstream exit window, PS2 in between the wire chamber and the first collimator, PS3 downstream of the propeller, PS4 at the isocenter. The propeller rotates once every 150 ms.

Image of FIG. 9.

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FIG. 9.

Energy spectra of primary protons at isocenter grouped according to time as described in the text, averaged over a 1 × 1 cm2 area (left). The partial SOBP for the full phase space from each time group is shown along with the summed SOBP for the 24 mm propeller (right). The dose was averaged over a 1 cm × 1 cm × 0.05 cm volume. The published measurement for this propeller is also shown (Ref. 48).

Image of FIG. 10.

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FIG. 10.

(Left) STAR Radiosurgery Beamline at MGH (proton beam enters from the left). (Right) SOBP as measured (circles) and simulated (histogram) for the STAR beamline in a water tank.

Image of FIG. 11.

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FIG. 11.

One of the IBA gantry treatment heads at MGH in double-scattering mode.

Image of FIG. 12.

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FIG. 12.

Spread out Bragg peak in water for three different range and modulation width options. The TOPAS simulation (squares) is compared with ion chamber measurement (triangles) from the MGH gantry treatment delivery systems in double-scattering mode. A total of 5 × 106 histories were simulated and the energy deposited in a 3 cm radius around the center of the beam was scored. The mean of the SOBP dose was normalized to unity. The SOBP region is shown in a zoomed view in the lower plots. From left to right, one of the best matches (a) and (b), an average match (c) and (d), and the worst case (e) and (f).

Image of FIG. 13.

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FIG. 13.

A compensator consisting of just a uniform half block of Lexan was placed in the beam path upstream of a water phantom.

Image of FIG. 14.

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FIG. 14.

Profile simulated with TOPAS and measured with an ion chamber. (Left) The simulated dose distribution in the water tank, the white arrow indicates the path of the measurements. (Right) Normalized profiles at Z = 9 cm.

Image of FIG. 15.

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FIG. 15.

Time dependence of X and Y dipole magnet fields, and triangular modulation of X field to compensate for patient motion (left). Number of particles simulated each time interval (right).

Image of FIG. 16.

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FIG. 16.

The fluence distribution at treatment system isocenter. (Left) Applying only the Field X and Field Y time features from Fig. 15. (Right) Applying also the Motion Field X time feature.

Image of FIG. 17.

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FIG. 17.

Comparison between XiO planned dose (left), TOPAS dose calculation (middle), and dose difference distribution (right) for one CT slice in the CTV for two patients, a head and neck (top) and a prostate patient (bottom). Shown are complete plans including all fields. Doses are given in Gy.


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While Monte Carlo particle transport has proven useful in many areas (treatment head design, dose calculation, shielding design, and imaging studies) and has been particularly important for proton therapy (due to the conformal dose distributions and a finite beam range in the patient), the available general purpose Monte Carlo codes in proton therapy have been overly complex for most clinical medical physicists. The learning process has large costs not only in time but also in reliability. To address this issue, we developed an innovative protonMonte Carlo platform and tested the tool in a variety of proton therapy applications.


Our approach was to take one of the already-established general purpose Monte Carlo codes and wrap and extend it to create a specialized user-friendly tool for proton therapy. The resulting tool, TOol for PArticle Simulation (TOPAS), should make Monte Carlo simulation more readily available for research and clinical physicists. TOPAS can model a passive scattering or scanning beam treatment head, model a patient geometry based on computed tomography(CT)images, score dose, fluence, etc., save and restart a phase space, provides advanced graphics, and is fully four-dimensional (4D) to handle variations in beam delivery and patient geometry during treatment. A custom-designed TOPAS parameter control system was placed at the heart of the code to meet requirements for ease of use, reliability, and repeatability without sacrificing flexibility.


We built and tested the TOPAS code. We have shown that the TOPAS parameter system provides easy yet flexible control over all key simulation areas such as geometry setup, particle source setup, scoring setup, etc. Through design consistency, we have insured that user experience gained in configuring one component, scorer or filter applies equally well to configuring any other component, scorer or filter. We have incorporated key lessons from safety management, proactively removing possible sources of user error such as line-ordering mistakes. We have modeled proton therapytreatment examples including the UCSF eye treatment head, the MGH stereotactic alignment in radiosurgery treatment head and the MGH gantry treatment heads in passive scattering and scanning modes, and we have demonstrated dose calculation based on patient-specific CT data. Initial validation results show agreement with measured data and demonstrate the capabilities of TOPAS in simulating beam delivery in 3D and 4D.


We have demonstrated TOPAS accuracy and usability in a variety of proton therapy setups. As we are preparing to make this tool freely available for researchers in medical physics, we anticipate widespread use of this tool in the growing proton therapy community.


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: TOPAS: An innovative proton Monte Carlo platform for research and clinical applications