TOPAS: an innovative proton Monte Carlo platform for research and clinical applications.

PURPOSE 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 proton Monte Carlo platform and tested the tool in a variety of proton therapy applications. METHODS 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. RESULTS 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 therapy treatment 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. CONCLUSIONS 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.

[1]  Harald Paganetti,et al.  Variations in linear energy transfer within clinical proton therapy fields and the potential for biological treatment planning. , 2011, International journal of radiation oncology, biology, physics.

[2]  H Paganetti,et al.  A modular method to handle multiple time-dependent quantities in Monte Carlo simulations , 2012, Physics in medicine and biology.

[3]  K Parodi,et al.  In-beam PET monitoring of mono-energetic 16O and 12C beams: experiments and FLUKA simulations for homogeneous targets , 2009, Physics in medicine and biology.

[4]  F. Rademakers,et al.  ROOT — An object oriented data analysis framework , 1997 .

[5]  S Beddar,et al.  Measurement and calculation of characteristic prompt gamma ray spectra emitted during proton irradiation , 2009, Physics in medicine and biology.

[6]  C. Ma,et al.  BEAM: a Monte Carlo code to simulate radiotherapy treatment units. , 1995, Medical physics.

[7]  Hanne M Kooy,et al.  A case study in proton pencil-beam scanning delivery. , 2010, International journal of radiation oncology, biology, physics.

[8]  S. Incerti,et al.  Geant4 developments and applications , 2006, IEEE Transactions on Nuclear Science.

[9]  H. W. Lewis Multiple Scattering in an Infinite Medium , 1950 .

[10]  B. A. Ludewigt,et al.  Instrumentation for Treatment of Cancer Using Proton and Light-Ion Beams , 1993 .

[11]  Harald Paganetti,et al.  Monte Carlo calculations for absolute dosimetry to determine machine outputs for proton therapy fields , 2006, Physics in medicine and biology.

[12]  Karl L. Brown,et al.  First‐ and second‐order charged particle optics , 1984 .

[13]  H Paganetti,et al.  Efficient voxel navigation for proton therapy dose calculation in TOPAS and Geant4 , 2012, Physics in medicine and biology.

[14]  H. Paganetti,et al.  Physics Settings for Using the Geant4 Toolkit in Proton Therapy , 2008, IEEE Transactions on Nuclear Science.

[15]  H Paganetti,et al.  Elevated LET components in clinical proton beams , 2011, Physics in medicine and biology.

[16]  Koichi Murakami,et al.  Validation of PTSIM for clinical usage , 2010, IEEE Nuclear Science Symposuim & Medical Imaging Conference.

[17]  Harald Paganetti,et al.  Field size dependence of the output factor in passively scattered proton therapy: influence of range, modulation, air gap, and machine settings. , 2009, Medical physics.

[18]  P Chauvel,et al.  Monte Carlo simulation of a protontherapy platform devoted to ocular melanoma. , 2005, Medical physics.

[19]  Anatoly Rosenfeld,et al.  Assessment of out-of-field absorbed dose and equivalent dose in proton fields. , 2009, Medical physics.

[20]  L. Urb Multiple scattering model in Geant4 , 2002 .

[21]  H Paganetti,et al.  Monte Carlo patient study on the comparison of prompt gamma and PET imaging for range verification in proton therapy , 2011, Physics in medicine and biology.

[22]  Takeshi Hiraoka,et al.  Energy loss of 70 MeV protons in elements , 1992 .

[23]  Harald Paganetti,et al.  Uncertainties and correction methods when modeling passive scattering proton therapy treatment heads with Monte Carlo , 2011, Physics in medicine and biology.

[24]  H Paganetti,et al.  Accurate Monte Carlo simulations for nozzle design, commissioning and quality assurance for a proton radiation therapy facility. , 2004, Medical physics.

[25]  M Goitein,et al.  A pencil beam algorithm for proton dose calculations. , 1996, Physics in medicine and biology.

[26]  T. Sasaki,et al.  Verification of the dose distributions with GEANT4 simulation for proton therapy , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[27]  G Ciangaru,et al.  Experimental validation of a Monte Carlo proton therapy nozzle model incorporating magnetically steered protons , 2009, Physics in medicine and biology.

[28]  Joseph F. Janni,et al.  Energy loss, range, path length, time-of-flight, straggling, multiple scattering, and nuclear interaction probability , 1982 .

[29]  I. Kawrakow Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version. , 2000, Medical physics.

[30]  Pedro Andreo,et al.  On the clinical spatial resolution achievable with protons and heavier charged particle radiotherapy beams , 2009, Physics in medicine and biology.

[31]  B. Faddegon,et al.  The accuracy of EGSnrc, Geant4 and PENELOPE Monte Carlo systems for the simulation of electron scatter in external beam radiotherapy , 2009, Physics in medicine and biology.

[32]  H Paganetti,et al.  Nuclear interactions of 160 MeV protons stopping in copper: a test of Monte Carlo nuclear models. , 1999, Medical physics.

[33]  J F Ziegler,et al.  Comments on ICRU report no. 49: stopping powers and ranges for protons and alpha particles. , 1999, Radiation research.

[34]  Hsiao-Ming Lu,et al.  Optimization of current modulation function for proton spread-out Bragg peak fields. , 2006, Medical physics.

[35]  C Nauraye,et al.  Monte Carlo modelling of the treatment line of the Proton Therapy Center in Orsay , 2009, Physics in medicine and biology.

[36]  H. Paganetti Range uncertainties in proton therapy and the role of Monte Carlo simulations , 2012, Physics in medicine and biology.

[37]  H Paganetti,et al.  Test of GEANT3 and GEANT4 nuclear models for 160 MeV protons stopping in CH2. , 2003, Medical physics.

[38]  M. Fippel,et al.  A Monte Carlo dose calculation algorithm for proton therapy. , 2004, Medical physics.

[39]  Takashi Akagi,et al.  Determination of the mean excitation energy of water from proton beam ranges , 2007 .

[40]  A. Ferrari,et al.  FLUKA: A Multi-Particle Transport Code , 2005 .

[41]  A. Kimura,et al.  gMocren: High-quality volume visualization tool for Geant4 simulation , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[42]  Katia Parodi,et al.  Clinical implementation of full Monte Carlo dose calculation in proton beam therapy , 2008, Physics in medicine and biology.

[43]  Harald Paganetti,et al.  Sensitivity of different dose scoring methods on organ-specific neutron dose calculations in proton therapy , 2008, Physics in medicine and biology.

[44]  Lynn J. Verhey,et al.  New UCSF proton ocular beam facility at the Crocker Nuclear Laboratory Cyclotron (UC Davis) , 1996 .

[45]  Zhengrong Liang,et al.  Reconstruction for proton computed tomography by tracing proton trajectories: a Monte Carlo study. , 2006, Medical physics.

[46]  G. Barrand,et al.  The Geant4 Visualisation System , 2008, Comput. Phys. Commun..