Monte Carlo simulations for configuring and testing an analytical proton dose-calculation algorithm

Contemporary treatment planning systems for proton radiotherapy typically use analytical pencil-beam algorithms - which require a comprehensive set of configuration data - to predict the absorbed dose distributions in the patient. In order to reduce the time required to prepare a new proton treatment planning system for clinical use, it was desirable to configure the planning system before measured beam data were available. However, it was not known if the Monte Carlo simulation method was a practical alternative to measuring beam profiles. The purpose of this study was to develop a model of a passively scattered proton therapy unit, to simulate the properties of the proton fields using the Monte Carlo technique and to configure an analytical treatment planning system using the simulated beam data. Additional simulations and treatment plans were calculated in order to validate the pencil-beam predictions against the Monte Carlo simulations using realistic treatment beams. Comparison of dose distributions in a water phantom revealed small dose difference and distances to agreement under the validation conditions. The total simulation time for generating the 768 beam configuration profiles was approximately 6 weeks using 30 nodes in a parallel processing cluster. The results of this study show that it is possible to configure and test a proton treatment planning system prior to the availability of measured proton beam data. The model presented here provided a means to reduce by several months the time required to prepare an analytical treatment planning system for patient treatments.

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

[2]  J. Siebers,et al.  Shielding measurements for 230-MeV protons , 1993 .

[3]  Computer Staff,et al.  Medical systems , 1993 .

[4]  Brian Wang,et al.  Simulation of organ-specific patient effective dose due to secondary neutrons in proton radiation treatment , 2005, Physics in medicine and biology.

[5]  E. Pedroni,et al.  Dose calculation models for proton treatment planning using a dynamic beam delivery system: an attempt to include density heterogeneity effects in the analytical dose calculation. , 1999, Physics in medicine and biology.

[6]  Alfred R. Smith,et al.  Benchmarking analytical calculations of proton doses in heterogeneous matter. , 2005, Medical physics.

[7]  S. Agosteo,et al.  Monte Carlo simulations for the design of the treatment rooms and synchrotron access mazes in the CNAO Hadrontherapy facility. , 2005, Radiation protection dosimetry.

[8]  F. Verhaegen,et al.  A systematic Monte Carlo study of secondary electron fluence perturbation in clinical proton beams (70-250 MeV) for cylindrical and spherical ion chambers. , 2001, Medical physics.

[9]  U. Titt,et al.  Patient neutron dose equivalent exposures outside of the proton therapy treatment field. , 2005, Radiation protection dosimetry.

[10]  M. Wagner.  Automated range compensation for proton therapy. , 1982, Medical physics.

[11]  W. Newhauser,et al.  Calculations of neutron dose equivalent exposures from range-modulated proton therapy beams , 2005, Physics in medicine and biology.

[12]  Radhe Mohan,et al.  Significant reduction of normal tissue dose by proton radiotherapy compared with three-dimensional conformal or intensity-modulated radiation therapy in Stage I or Stage III non-small-cell lung cancer. , 2006, International journal of radiation oncology, biology, physics.

[13]  R. Mohan,et al.  Monte Carlo simulations of stray neutron radiation exposures in proton therapy , 2007 .

[14]  M Goitein,et al.  Proton beam penumbra: effects of separation between patient and beam modifying devices. , 1986, Medical physics.

[15]  P L Petti,et al.  Evaluation of a pencil-beam dose calculation technique for charged particle radiotherapy. , 1996, International journal of radiation oncology, biology, physics.

[16]  A. Brahme,et al.  Influence of multiple scattering and energy loss straggling on the absorbed dose distributions of therapeutic light ion beams: I. Analytical pencil beam model. , 2004, Physics in medicine and biology.

[17]  P Andreo,et al.  Monte Carlo and analytical calculation of proton pencil beams for computerized treatment plan optimization , 1997, Physics in medicine and biology.

[18]  P Andreo,et al.  Monte Carlo calculated stopping-power ratios, water/air, for clinical proton dosimetry (50-250 MeV). , 1997, Physics in medicine and biology.

[19]  Dan T. L. Jones,et al.  Fluence correction factors in plastic phantoms for clinical proton beams. , 2002, Physics in medicine and biology.

[20]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[21]  Alfred R. Smith,et al.  Treatment planning with protons for pediatric retinoblastoma, medulloblastoma, and pelvic sarcoma: how do protons compare with other conformal techniques? , 2005, International journal of radiation oncology, biology, physics.

[22]  H Paganetti,et al.  Monte Carlo method to study the proton fluence for treatment planning. , 1998, Medical physics.

[23]  E. Pedroni,et al.  The calibration of CT Hounsfield units for radiotherapy treatment planning. , 1996, 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]  F. Verhaegen,et al.  Dosimetry using plane-parallel ionization chambers in a 75 MeV clinical proton beam. , 2002, Physics in medicine and biology.

[26]  Leonard Steinborn.  International Organization for Standardization ISO 9000-3, Guidelines for the Application of ISO 9001:1994 to the Development, Supply, Installation and Maintenance of Computer Software , 2004 .

[27]  W. Newhauser,et al.  Virtual commissioning of a treatment planning system for proton therapy of ocular cancers. , 2005, Radiation protection dosimetry.

[28]  Design tools for proton therapy nozzles based on the double-scattering foil technique. , 2005, Radiation protection dosimetry.

[29]  E Pedroni,et al.  Monte Carlo dose calculations for spot scanned proton therapy , 2005, Physics in medicine and biology.

[30]  Uwe Titt,et al.  Monte Carlo simulations of a nozzle for the treatment of ocular tumours with high-energy proton beams , 2005, Physics in medicine and biology.

[31]  S Agosteo,et al.  Secondary neutron and photon dose in proton therapy. , 1998, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[32]  George Starkschall,et al.  Determination of output factors for small proton therapy fields. , 2007, Medical physics.

[33]  A. Kacperek.  Clinical Proton Dosimetry Part I: Beam Production, Beam Delivery and Measurement of Absorbed Dose (ICRU Report 59) , 2000 .

[34]  R. Mohan,et al.  Dosimetric impact of tantalum markers used in the treatment of uveal melanoma with proton beam therapy , 2007, Physics in medicine and biology.

[35]  J. Siebers,et al.  Measurement of Neutron Dose Equivalent and Penetration in Concrete for 230 MeV Proton Bombardment of Al, Fe, and Pb Targets , 1992 .

[36]  R. Mohan,et al.  Monte Carlo simulations of the dosimetric impact of radiopaque fiducial markers for proton radiotherapy of the prostate , 2007, Physics in medicine and biology.

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

[38]  U. Titt,et al.  Neutron shielding calculations in a proton therapy facility based on Monte Carlo simulations and analytical models: criterion for selecting the method of choice. , 2005, Radiation protection dosimetry.