Literature DB >> 18196810

Report of the AAPM Task Group No. 105: Issues associated with clinical implementation of Monte Carlo-based photon and electron external beam treatment planning.

Indrin J Chetty1, 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.   

Abstract

The Monte Carlo (MC) method has been shown through many research studies to calculate accurate dose distributions for clinical radiotherapy, particularly in heterogeneous patient tissues where the effects of electron transport cannot be accurately handled with conventional, deterministic dose algorithms. Despite its proven accuracy and the potential for improved dose distributions to influence treatment outcomes, the long calculation times previously associated with MC simulation rendered this method impractical for routine clinical treatment planning. However, the development of faster codes optimized for radiotherapy calculations and improvements in computer processor technology have substantially reduced calculation times to, in some instances, within minutes on a single processor. These advances have motivated several major treatment planning system vendors to embark upon the path of MC techniques. Several commercial vendors have already released or are currently in the process of releasing MC algorithms for photon and/or electron beam treatment planning. Consequently, the accessibility and use of MC treatment planning algorithms may well become widespread in the radiotherapy community. With MC simulation, dose is computed stochastically using first principles; this method is therefore quite different from conventional dose algorithms. Issues such as statistical uncertainties, the use of variance reduction techniques, the ability to account for geometric details in the accelerator treatment head simulation, and other features, are all unique components of a MC treatment planning algorithm. Successful implementation by the clinical physicist of such a system will require an understanding of the basic principles of MC techniques. The purpose of this report, while providing education and review on the use of MC simulation in radiotherapy planning, is to set out, for both users and developers, the salient issues associated with clinical implementation and experimental verification of MC dose algorithms. As the MC method is an emerging technology, this report is not meant to be prescriptive. Rather, it is intended as a preliminary report to review the tenets of the MC method and to provide the framework upon which to build a comprehensive program for commissioning and routine quality assurance of MC-based treatment planning systems.

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Year:  2007        PMID: 18196810     DOI: 10.1118/1.2795842

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


  143 in total

1.  Dose discrepancies in the buildup region and their impact on dose calculations for IMRT fields.

Authors:  Shu-Hui Hsu; Jean M Moran; Yu Chen; Ravi Kulasekere; Peter L Roberson
Journal:  Med Phys       Date:  2010-05       Impact factor: 4.071

2.  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

3.  Monte Carlo modeling of ultrasound probes for image guided radiotherapy.

Authors:  Magdalena Bazalova-Carter; Jeffrey Schlosser; Josephine Chen; Dimitre Hristov
Journal:  Med Phys       Date:  2015-10       Impact factor: 4.071

Review 4.  Accurate accumulation of dose for improved understanding of radiation effects in normal tissue.

Authors:  David A Jaffray; Patricia E Lindsay; Kristy K Brock; Joseph O Deasy; W A Tomé
Journal:  Int J Radiat Oncol Biol Phys       Date:  2010-03-01       Impact factor: 7.038

5.  Monte Carlo simulation of large electron fields.

Authors:  Bruce A Faddegon; Joseph Perl; Makoto Asai
Journal:  Phys Med Biol       Date:  2008-02-21       Impact factor: 3.609

6.  Determination of electron energy, spectral width, and beam divergence at the exit window for clinical megavoltage x-ray beams.

Authors:  D L Sawkey; B A Faddegon
Journal:  Med Phys       Date:  2009-03       Impact factor: 4.071

7.  Generation of a novel phase-space-based cylindrical dose kernel for IMRT optimization.

Authors:  Hualiang Zhong; Indrin J Chetty
Journal:  Med Phys       Date:  2012-05       Impact factor: 4.071

8.  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

9.  Monte Carlo modeling of a 6 and 18 MV Varian Clinac medical accelerator for in-field and out-of-field dose calculations: development and validation.

Authors:  Bryan Bednarz; X George Xu
Journal:  Phys Med Biol       Date:  2009-01-14       Impact factor: 3.609

10.  The accuracy of EGSnrc, Geant4 and PENELOPE Monte Carlo systems for the simulation of electron scatter in external beam radiotherapy.

Authors:  Bruce A Faddegon; Iwan Kawrakow; Yuri Kubyshin; Joseph Perl; Josep Sempau; Laszlo Urban
Journal:  Phys Med Biol       Date:  2009-09-24       Impact factor: 3.609

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