Literature DB >> 15798269

Monte Carlo dose calculations for spot scanned proton therapy.

A Tourovsky1, A J Lomax, U Schneider, E Pedroni.   

Abstract

Density heterogeneities can have a profound effect on dose distributions for proton therapy. Although analytical calculations in homogeneous media are relatively straightforward, the modelling of the propagation of the beam through density heterogeneities can be more problematical. At the Paul Scherrer Institute, an in-house dedicated Monte Carlo (MC) code has been used for over a decade to assess the possible deficiencies of the analytical calculations in patient geometries. The MC code has been optimized for speed, and as such traces primary protons only through the treatment nozzle and patient's CT. Contributions from nuclear interactions are modelled analytically with no tracing of secondary particles. The MC code has been verified against measured data in water and experimental proton radiographs through a heterogeneous anthropomorphic phantom. In comparison to the analytical calculation, the MC code has been applied to both spot scanned and intensity modulated proton therapy plans, and to a number of cases containing titanium metal implants. In summary, MC-based dose calculations could provide an invaluable tool for independently verifying the calculated dose distribution within a patient geometry as part of a comprehensive quality assurance protocol for proton treatment plans.

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Year:  2005        PMID: 15798269     DOI: 10.1088/0031-9155/50/5/019

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


  19 in total

1.  A GPU implementation of a track-repeating algorithm for proton radiotherapy dose calculations.

Authors:  Pablo P Yepes; Dragan Mirkovic; Phillip J Taddei
Journal:  Phys Med Biol       Date:  2010-11-12       Impact factor: 3.609

2.  A generalized 2D pencil beam scaling algorithm for proton dose calculation in heterogeneous slab geometries.

Authors:  David C Westerly; Xiaohu Mo; Wolfgang A Tomé; Thomas R Mackie; Paul M DeLuca
Journal:  Med Phys       Date:  2013-06       Impact factor: 4.071

3.  Benchmark measurements and simulations of dose perturbations due to metallic spheres in proton beams.

Authors:  Wayne D Newhauser; Laura Rechner; Dragan Mirkovic; Pablo Yepes; Nicholas C Koch; Uwe Titt; Jonas D Fontenot; Rui Zhang
Journal:  Radiat Meas       Date:  2013-11-01       Impact factor: 1.898

Review 4.  Advanced Proton Beam Dosimetry Part I: review and performance evaluation of dose calculation algorithms.

Authors:  Jatinder Saini; Erik Traneus; Dominic Maes; Rajesh Regmi; Stephen R Bowen; Charles Bloch; Tony Wong
Journal:  Transl Lung Cancer Res       Date:  2018-04

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

Authors:  Pablo Yepes; Sharmalee Randeniya; Phillip J Taddei; Wayne D Newhauser
Journal:  Nucl Technol       Date:  2009-12-01

6.  Comparison of MCNPX and Geant4 proton energy deposition predictions for clinical use.

Authors:  U Titt; B Bednarz; H Paganetti
Journal:  Phys Med Biol       Date:  2012-09-21       Impact factor: 3.609

7.  A new approach to integrate GPU-based Monte Carlo simulation into inverse treatment plan optimization for proton therapy.

Authors:  Yongbao Li; Zhen Tian; Ting Song; Zhaoxia Wu; Yaqiang Liu; Steve Jiang; Xun Jia
Journal:  Phys Med Biol       Date:  2016-12-17       Impact factor: 3.609

Review 8.  The physics of proton therapy.

Authors:  Wayne D Newhauser; Rui Zhang
Journal:  Phys Med Biol       Date:  2015-03-24       Impact factor: 3.609

9.  GRID-ENABLED TREATMENT PLANNING FOR PROTON THERAPY USING MONTE CARLO SIMULATIONS.

Authors:  Ravi Vadapalli; Pablo Yepes; Wayne Newhauser; Roger Lichti
Journal:  Nucl Technol       Date:  2011-07

Review 10.  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

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