Literature DB >> 23556888

Geometrical splitting technique to improve the computational efficiency in Monte Carlo calculations for proton therapy.

José Ramos-Méndez1, Joseph Perl, Bruce Faddegon, Jan Schümann, Harald Paganetti.   

Abstract

PURPOSE: To present the implementation and validation of a geometrical based variance reduction technique for the calculation of phase space data for proton therapy dose calculation.
METHODS: The treatment heads at the Francis H Burr Proton Therapy Center were modeled with a new Monte Carlo tool (TOPAS based on Geant4). For variance reduction purposes, two particle-splitting planes were implemented. First, the particles were split upstream of the second scatterer or at the second ionization chamber. Then, particles reaching another plane immediately upstream of the field specific aperture were split again. In each case, particles were split by a factor of 8. At the second ionization chamber and at the latter plane, the cylindrical symmetry of the proton beam was exploited to position the split particles at randomly spaced locations rotated around the beam axis. Phase space data in IAEA format were recorded at the treatment head exit and the computational efficiency was calculated. Depth-dose curves and beam profiles were analyzed. Dose distributions were compared for a voxelized water phantom for different treatment fields for both the reference and optimized simulations. In addition, dose in two patients was simulated with and without particle splitting to compare the efficiency and accuracy of the technique.
RESULTS: A normalized computational efficiency gain of a factor of 10-20.3 was reached for phase space calculations for the different treatment head options simulated. Depth-dose curves and beam profiles were in reasonable agreement with the simulation done without splitting: within 1% for depth-dose with an average difference of (0.2 ± 0.4)%, 1 standard deviation, and a 0.3% statistical uncertainty of the simulations in the high dose region; 1.6% for planar fluence with an average difference of (0.4 ± 0.5)% and a statistical uncertainty of 0.3% in the high fluence region. The percentage differences between dose distributions in water for simulations done with and without particle splitting were within the accepted clinical tolerance of 2%, with a 0.4% statistical uncertainty. For the two patient geometries considered, head and prostate, the efficiency gain was 20.9 and 14.7, respectively, with the percentages of voxels with gamma indices lower than unity 98.9% and 99.7%, respectively, using 2% and 2 mm criteria.
CONCLUSIONS: The authors have implemented an efficient variance reduction technique with significant speed improvements for proton Monte Carlo simulations. The method can be transferred to other codes and other treatment heads.

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Year:  2013        PMID: 23556888      PMCID: PMC3618101          DOI: 10.1118/1.4795343

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  31 in total

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2.  A particle track-repeating algorithm for proton beam dose calculation.

Authors:  J S Li; B Shahine; E Fourkal; C-M Ma
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3.  Shielding design for a laser-accelerated proton therapy system.

Authors:  J Fan; W Luo; E Fourkal; T Lin; J Li; I Veltchev; C-M Ma
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Review 4.  Report of the AAPM Task Group No. 105: Issues associated with clinical implementation of Monte Carlo-based photon and electron external beam treatment planning.

Authors:  Indrin J Chetty; Bruce Curran; Joanna E Cygler; John J DeMarco; Gary Ezzell; Bruce A Faddegon; Iwan Kawrakow; Paul J Keall; Helen Liu; C M Charlie Ma; D W O Rogers; Jan Seuntjens; Daryoush Sheikh-Bagheri; Jeffrey V Siebers
Journal:  Med Phys       Date:  2007-12       Impact factor: 4.071

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

Authors:  J Perl; J Shin; J Schumann; B Faddegon; H Paganetti
Journal:  Med Phys       Date:  2012-11       Impact factor: 4.071

6.  A TRACK-REPEATING ALGORITHM FOR FAST MONTE CARLO DOSE CALCULATIONS OF PROTON RADIOTHERAPY.

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7.  Monte Carlo fast dose calculator for proton radiotherapy: application to a voxelized geometry representing a patient with prostate cancer.

Authors:  Pablo Yepes; Sharmalee Randeniya; Phillip J Taddei; Wayne D Newhauser
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8.  A Monte Carlo dose calculation algorithm for proton therapy.

Authors:  Matthias Fippel; Martin Soukup
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Review 9.  Range uncertainties in proton therapy and the role of Monte Carlo simulations.

Authors:  Harald Paganetti
Journal:  Phys Med Biol       Date:  2012-05-09       Impact factor: 3.609

10.  A modular method to handle multiple time-dependent quantities in Monte Carlo simulations.

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  12 in total

1.  Improved efficiency in Monte Carlo simulation for passive-scattering proton therapy.

Authors:  J Ramos Méndez; J Perl; J Schümann; J Shin; H Paganetti; B Faddegon
Journal:  Phys Med Biol       Date:  2015-06-10       Impact factor: 3.609

2.  Extension of TOPAS for the simulation of proton radiation effects considering molecular and cellular endpoints.

Authors:  Lisa Polster; Jan Schuemann; Ilaria Rinaldi; Lucas Burigo; Aimee L McNamara; Robert D Stewart; Andrea Attili; David J Carlson; Tatsuhiko Sato; José Ramos Méndez; Bruce Faddegon; Joseph Perl; Harald Paganetti
Journal:  Phys Med Biol       Date:  2015-06-10       Impact factor: 3.609

3.  A framework for implementation of organ effect models in TOPAS with benchmarks extended to proton therapy.

Authors:  J Ramos-Méndez; J Perl; J Schümann; J Shin; H Paganetti; B Faddegon
Journal:  Phys Med Biol       Date:  2015-06-10       Impact factor: 3.609

4.  Assessing the Clinical Impact of Approximations in Analytical Dose Calculations for Proton Therapy.

Authors:  Jan Schuemann; Drosoula Giantsoudi; Clemens Grassberger; Maryam Moteabbed; Chul Hee Min; Harald Paganetti
Journal:  Int J Radiat Oncol Biol Phys       Date:  2015-04-08       Impact factor: 7.038

5.  Validation of a GPU-based Monte Carlo code (gPMC) for proton radiation therapy: clinical cases study.

Authors:  Drosoula Giantsoudi; Jan Schuemann; Xun Jia; Stephen Dowdell; Steve Jiang; Harald Paganetti
Journal:  Phys Med Biol       Date:  2015-02-26       Impact factor: 3.609

6.  Experimental validation of the TOPAS Monte Carlo system for passive scattering proton therapy.

Authors:  M Testa; J Schümann; H-M Lu; J Shin; B Faddegon; J Perl; H Paganetti
Journal:  Med Phys       Date:  2013-12       Impact factor: 4.071

7.  The TOPAS tool for particle simulation, a Monte Carlo simulation tool for physics, biology and clinical research.

Authors:  Bruce Faddegon; José Ramos-Méndez; Jan Schuemann; Aimee McNamara; Jungwook Shin; Joseph Perl; Harald Paganetti
Journal:  Phys Med       Date:  2020-04-03       Impact factor: 2.685

8.  TOPAS-nBio: An Extension to the TOPAS Simulation Toolkit for Cellular and Sub-cellular Radiobiology.

Authors:  J Schuemann; A L McNamara; J Ramos-Méndez; J Perl; K D Held; H Paganetti; S Incerti; B Faddegon
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9.  Site-specific range uncertainties caused by dose calculation algorithms for proton therapy.

Authors:  J Schuemann; S Dowdell; C Grassberger; C H Min; H Paganetti
Journal:  Phys Med Biol       Date:  2014-07-03       Impact factor: 3.609

10.  A phenomenological relative biological effectiveness (RBE) model for proton therapy based on all published in vitro cell survival data.

Authors:  Aimee L McNamara; Jan Schuemann; Harald Paganetti
Journal:  Phys Med Biol       Date:  2015-10-13       Impact factor: 3.609

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