Literature DB >> 22572201

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

J Shin1, J Perl, J Schümann, H Paganetti, B A Faddegon.   

Abstract

A general method for handling time-dependent quantities in Monte Carlo simulations was developed to make such simulations more accessible to the medical community for a wide range of applications in radiotherapy, including fluence and dose calculation. To describe time-dependent changes in the most general way, we developed a grammar of functions that we call 'Time Features'. When a simulation quantity, such as the position of a geometrical object, an angle, a magnetic field, a current, etc, takes its value from a Time Feature, that quantity varies over time. The operation of time-dependent simulation was separated into distinct parts: the Sequence samples time values either sequentially at equal increments or randomly from a uniform distribution (allowing quantities to vary continuously in time), and then each time-dependent quantity is calculated according to its Time Feature. Due to this modular structure, time-dependent simulations, even in the presence of multiple time-dependent quantities, can be efficiently performed in a single simulation with any given time resolution. This approach has been implemented in TOPAS (TOol for PArticle Simulation), designed to make Monte Carlo simulations with Geant4 more accessible to both clinical and research physicists. To demonstrate the method, three clinical situations were simulated: a variable water column used to verify constancy of the Bragg peak of the Crocker Lab eye treatment facility of the University of California, the double-scattering treatment mode of the passive beam scattering system at Massachusetts General Hospital (MGH), where a spinning range modulator wheel accompanied by beam current modulation produces a spread-out Bragg peak, and the scanning mode at MGH, where time-dependent pulse shape, energy distribution and magnetic fields control Bragg peak positions. Results confirm the clinical applicability of the method.

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Year:  2012        PMID: 22572201      PMCID: PMC4477630          DOI: 10.1088/0031-9155/57/11/3295

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  12 in total

1.  Monte Carlo dose calculations for dynamic IMRT treatments.

Authors:  P J Keall; J V Siebers; M Arnfield; J O Kim; R Mohan
Journal:  Phys Med Biol       Date:  2001-04       Impact factor: 3.609

2.  Monte Carlo modelling of a virtual wedge.

Authors:  F Verhaegen; I J Das
Journal:  Phys Med Biol       Date:  1999-12       Impact factor: 3.609

3.  Incorporating dynamic collimator motion in Monte Carlo simulations: an application in modelling a dynamic wedge.

Authors:  F Verhaegen; H H Liu
Journal:  Phys Med Biol       Date:  2001-02       Impact factor: 3.609

4.  Test of GEANT3 and GEANT4 nuclear models for 160 MeV protons stopping in CH2.

Authors:  H Paganetti; B Gottschalk
Journal:  Med Phys       Date:  2003-07       Impact factor: 4.071

5.  Accurate Monte Carlo simulations for nozzle design, commissioning and quality assurance for a proton radiation therapy facility.

Authors:  H Paganetti; H Jiang; S Y Lee; H M Kooy
Journal:  Med Phys       Date:  2004-07       Impact factor: 4.071

6.  4D Monte Carlo simulation of proton beam scanning: modelling of variations in time and space to study the interplay between scanning pattern and time-dependent patient geometry.

Authors:  H Paganetti; H Jiang; A Trofimov
Journal:  Phys Med Biol       Date:  2005-02-17       Impact factor: 3.609

7.  Monte Carlo calculations for absolute dosimetry to determine machine outputs for proton therapy fields.

Authors:  Harald Paganetti
Journal:  Phys Med Biol       Date:  2006-05-17       Impact factor: 3.609

8.  Sensitivities in the production of spread-out Bragg peak dose distributions by passive scattering with beam current modulation.

Authors:  Hsiao-Ming Lu; Robert Brett; Martijn Engelsman; Roelf Slopsema; Hanne Kooy; Jay Flanz
Journal:  Med Phys       Date:  2007-10       Impact factor: 4.071

9.  Position-probability-sampled Monte Carlo calculation of VMAT, 3DCRT, step-shoot IMRT, and helical tomotherapy dose distributions using BEAMnrc/DOSXYZnrc.

Authors:  Jason Belec; Nicolas Ploquin; Daniel J La Russa; Brenda G Clark
Journal:  Med Phys       Date:  2011-02       Impact factor: 4.071

10.  A method of simulating dynamic multileaf collimators using Monte Carlo techniques for intensity-modulated radiation therapy.

Authors:  H H Liu; F Verhaegen; L Dong
Journal:  Phys Med Biol       Date:  2001-09       Impact factor: 3.609

View more
  10 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.  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

5.  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

6.  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

7.  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
Journal:  Radiat Res       Date:  2019-01-04       Impact factor: 2.841

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

Authors:  José Ramos-Méndez; Joseph Perl; Bruce Faddegon; Jan Schümann; Harald Paganetti
Journal:  Med Phys       Date:  2013-04       Impact factor: 4.071

9.  Monte Carlo simulation of chemistry following radiolysis with TOPAS-nBio.

Authors:  J Ramos-Méndez; J Perl; J Schuemann; A McNamara; H Paganetti; B Faddegon
Journal:  Phys Med Biol       Date:  2018-05-17       Impact factor: 3.609

Review 10.  Monte Carlo methods for device simulations in radiation therapy.

Authors:  Hyojun Park; Harald Paganetti; Jan Schuemann; Xun Jia; Chul Hee Min
Journal:  Phys Med Biol       Date:  2021-09-14       Impact factor: 4.174

  10 in total

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