Literature DB >> 25715661

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

Drosoula Giantsoudi1, Jan Schuemann, Xun Jia, Stephen Dowdell, Steve Jiang, Harald Paganetti.   

Abstract

Monte Carlo (MC) methods are recognized as the gold-standard for dose calculation, however they have not replaced analytical methods up to now due to their lengthy calculation times. GPU-based applications allow MC dose calculations to be performed on time scales comparable to conventional analytical algorithms. This study focuses on validating our GPU-based MC code for proton dose calculation (gPMC) using an experimentally validated multi-purpose MC code (TOPAS) and compare their performance for clinical patient cases. Clinical cases from five treatment sites were selected covering the full range from very homogeneous patient geometries (liver) to patients with high geometrical complexity (air cavities and density heterogeneities in head-and-neck and lung patients) and from short beam range (breast) to large beam range (prostate). Both gPMC and TOPAS were used to calculate 3D dose distributions for all patients. Comparisons were performed based on target coverage indices (mean dose, V95, D98, D50, D02) and gamma index distributions. Dosimetric indices differed less than 2% between TOPAS and gPMC dose distributions for most cases. Gamma index analysis with 1%/1 mm criterion resulted in a passing rate of more than 94% of all patient voxels receiving more than 10% of the mean target dose, for all patients except for prostate cases. Although clinically insignificant, gPMC resulted in systematic underestimation of target dose for prostate cases by 1-2% compared to TOPAS. Correspondingly the gamma index analysis with 1%/1 mm criterion failed for most beams for this site, while for 2%/1 mm criterion passing rates of more than 94.6% of all patient voxels were observed. For the same initial number of simulated particles, calculation time for a single beam for a typical head and neck patient plan decreased from 4 CPU hours per million particles (2.8-2.9 GHz Intel X5600) for TOPAS to 2.4 s per million particles (NVIDIA TESLA C2075) for gPMC. Excellent agreement was demonstrated between our fast GPU-based MC code (gPMC) and a previously extensively validated multi-purpose MC code (TOPAS) for a comprehensive set of clinical patient cases. This shows that MC dose calculations in proton therapy can be performed on time scales comparable to analytical algorithms with accuracy comparable to state-of-the-art CPU-based MC codes.

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Year:  2015        PMID: 25715661      PMCID: PMC7788741          DOI: 10.1088/0031-9155/60/6/2257

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


  18 in total

1.  Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version.

Authors:  I Kawrakow
Journal:  Med Phys       Date:  2000-03       Impact factor: 4.071

2.  Dose to water versus dose to medium in proton beam therapy.

Authors:  Harald Paganetti
Journal:  Phys Med Biol       Date:  2009-06-23       Impact factor: 3.609

3.  A technique for the quantitative evaluation of dose distributions.

Authors:  D A Low; W B Harms; S Mutic; J A Purdy
Journal:  Med Phys       Date:  1998-05       Impact factor: 4.071

4.  Comprehensive analysis of proton range uncertainties related to patient stopping-power-ratio estimation using the stoichiometric calibration.

Authors:  Ming Yang; X Ronald Zhu; Peter C Park; Uwe Titt; Radhe Mohan; Gary Virshup; James E Clayton; Lei Dong
Journal:  Phys Med Biol       Date:  2012-06-07       Impact factor: 3.609

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.  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

7.  Numerical solutions of the γ-index in two and three dimensions.

Authors:  Benjamin M Clasie; Gregory C Sharp; Joao Seco; Jacob B Flanz; Hanne M Kooy
Journal:  Phys Med Biol       Date:  2012-10-09       Impact factor: 3.609

8.  A Monte Carlo dose calculation algorithm for proton therapy.

Authors:  Matthias Fippel; Martin Soukup
Journal:  Med Phys       Date:  2004-08       Impact factor: 4.071

9.  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

10.  Efficient voxel navigation for proton therapy dose calculation in TOPAS and Geant4.

Authors:  J Schümann; H Paganetti; J Shin; B Faddegon; J Perl
Journal:  Phys Med Biol       Date:  2012-05-09       Impact factor: 3.609

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  19 in total

1.  Robust optimization for intensity-modulated proton therapy with soft spot sensitivity regularization.

Authors:  Wenbo Gu; Dan Ruan; Daniel O'Connor; Wei Zou; Lei Dong; Min-Yu Tsai; Xun Jia; Ke Sheng
Journal:  Med Phys       Date:  2019-01-21       Impact factor: 4.071

Review 2.  Monte Carlo Simulations of Particle Interactions with Tissue in Carbon Ion Therapy.

Authors:  George Dedes; Katia Parodi
Journal:  Int J Part Ther       Date:  2016-02-09

Review 3.  Empowering Intensity Modulated Proton Therapy Through Physics and Technology: An Overview.

Authors:  Radhe Mohan; Indra J Das; Clifton C Ling
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-10-01       Impact factor: 7.038

4.  DICOM-RT Ion interface to utilize MC simulations in routine clinical workflow for proton pencil beam radiotherapy.

Authors:  Jungwook Shin; Hanne M Kooy; Harald Paganetti; Benjamin Clasie
Journal:  Phys Med       Date:  2020-05-07       Impact factor: 2.685

5.  Reoptimization of Intensity Modulated Proton Therapy Plans Based on Linear Energy Transfer.

Authors:  Jan Unkelbach; Pablo Botas; Drosoula Giantsoudi; Bram L Gorissen; Harald Paganetti
Journal:  Int J Radiat Oncol Biol Phys       Date:  2016-09-01       Impact factor: 7.038

6.  Recent developments and comprehensive evaluations of a GPU-based Monte Carlo package for proton therapy.

Authors:  Nan Qin; Pablo Botas; Drosoula Giantsoudi; Jan Schuemann; Zhen Tian; Steve B Jiang; Harald Paganetti; Xun Jia
Journal:  Phys Med Biol       Date:  2016-10-03       Impact factor: 3.609

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.  Linear energy transfer weighted beam orientation optimization for intensity-modulated proton therapy.

Authors:  Wenbo Gu; Dan Ruan; Wei Zou; Lei Dong; Ke Sheng
Journal:  Med Phys       Date:  2020-07-13       Impact factor: 4.071

9.  Comparison of weekly and daily online adaptation for head and neck intensity-modulated proton therapy.

Authors:  Mislav Bobić; Arthur Lalonde; Gregory C Sharp; Clemens Grassberger; Joost M Verburg; Brian A Winey; Antony J Lomax; Harald Paganetti
Journal:  Phys Med Biol       Date:  2021-02-25       Impact factor: 3.609

10.  Development and validation of the Dynamic Collimation Monte Carlo simulation package for pencil beam scanning proton therapy.

Authors:  Nicholas P Nelson; Wesley S Culberson; Daniel E Hyer; Theodore J Geoghegan; Kaustubh A Patwardhan; Blake R Smith; Ryan T Flynn; Jen Yu; Suresh Rana; Alonso N Gutiérrez; Patrick M Hill
Journal:  Med Phys       Date:  2021-04-09       Impact factor: 4.506

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