Experimental test of Monte Carlo proton transport at grazing incidence in GEANT4, FLUKA and MCNPX

Kimstrand, Peter; Tilly, Nina; Ahnesjö, Anders; Traneus, E.

doi:10.1088/0031-9155/53/4/020

Cited by 14 publications

(15 citation statements)

References 27 publications

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“…MCNPX results had a wider FWHM than those for GEANT4, leading to a lower depth dose on the central axis. Similar findings were reported by Kimstrand et al [13] However, Kimstrand et al also compared experimental data with GEANT4, MCNPX, and FLUKA and concluded that all of these codes underestimated the probability of outscatter. MCS can be accurately modelled only if careful tuning of transport parameters was performed.…”

Section: Dose Contribution Of Different Kinds Of Particlessupporting

confidence: 79%

“…The simulation of proton transport under these conditions has to be carefully studied in order to obtain accurate results. [13] Comparisons of MC simulations and the pencil beam algorithm may help to clarify dose distribution under proton disequilibrium, which is likely to occur in lung dosimetry. The subcentimetre lateral dose distribution was also studied to understand the dose deviation between different MC simulations and the pencil beam algorithm.…”

Section: What This Study Adds To the Fieldmentioning

confidence: 99%

“…The reason for this deviation is mainly caused by the modelling of MCS. [13] Hong's algorithm only used one simple Gaussian to model the scattering angle distribution, while both MCNPX and GEANT4 used more complicated assumptions, as shown in Figure 6. MCNPX results had a wider FWHM than those for GEANT4, leading to a lower depth dose on the central axis.…”

Section: Dose Contribution Of Different Kinds Of Particlesmentioning

confidence: 99%

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MCNPX simulation of proton dose distributions in a water phantom

Lee

Chen

et al. 2015

Biomed J

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Background: This study presents the Monte Carlo N-Particles Transport Code, Extension (MCNPX) simulation of proton dose distributions in a water phantom. Methods:In this study, fluence and dose distributions from an incident proton pencil beam were calculated as a function of depth in a water phantom. Moreover, lateral dose distributions were also studied to understand the deviation among different MC simulations and the pencil beam algorithm. MCNPX codes were used to model the transport and interactions of particles in the water phantom using its built-in "repeated structures" feature. Mesh Tally was used in which the track lengths were distributed in a defined cell and then converted into doses and fluences. Two different scenarios were studied including a proton equilibrium case and a proton disequilibrium case. Results:For the proton equilibrium case, proton fluence and dose in depths beyond the Bragg peak were slightly perturbed by the choice of the simulated particle types. The dose from secondary particles was about three orders smaller, but its simulation consumed significant computing time. This suggests that the simulation of secondary particles may only be necessary for radiation safety issues for proton therapy. For the proton disequilibrium case, the impacts of different multiple Coulomb scattering (MCS) models were studied. Depth dose distributions of a 70 MeV proton pencil beam in a water phantom obtained from MCNPX, Geometry and Track, version 4, and the pencil beam algorithm showed significant deviations between each other, because of different MCS models used. Conclusions: Careful modelling of MCS is necessary when proton disequilibrium exists.( Biomed J 2015;38:414-420)

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Section: Dose Contribution Of Different Kinds Of Particlessupporting

confidence: 79%

Section: What This Study Adds To the Fieldmentioning

confidence: 99%

Section: Dose Contribution Of Different Kinds Of Particlesmentioning

confidence: 99%

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MCNPX simulation of proton dose distributions in a water phantom

Lee

Chen

et al. 2015

Biomed J

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“…Multiple groups have developed Monte Carlo treatment planning engines (Tourovsky et al , 2005; Newhauser et al , 2008b; Perl et al , 2012; Mairani et al , 2013) based on various Monte Carlo codes, independently proving the technical feasibility of this approach. The physics of general purpose Monte Carlo codes for this purpose has been exhaustively validated against benchmark measurements in therapeutic proton beam data (Polf JC, 2007; Kimstrand et al , 2008; Titt et al , 2008a; Randeniya et al , 2009). Just 10 years ago, the slow execution times, memory constraints, unknown accuracy, and unknown technical feasibility of a full-blown Monte Carlo engine were serious research challenges.…”

Section: Proton Transport Calculationsmentioning

confidence: 99%

The physics of proton therapy

2015

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The physics of proton therapy has advanced considerably since it was proposed in 1946. Today analytical equations and numerical simulation methods are available to predict and characterize many aspects of proton therapy. This article reviews the basic aspects of the physics of proton therapy, including proton interaction mechanisms, proton transport calculations, the determination of dose from therapeutic and stray radiations, and shielding design. The article discusses underlying processes as well as selected practical experimental and theoretical methods. We conclude by briefly speculating on possible future areas of research of relevance to the physics of proton therapy.

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“…All these general purpose codes with careful choice of approximations produce accurate results for proton transport compared to experiments. [22232425262728]…”

Section: Introductionmentioning

confidence: 99%

A fast Monte Carlo code for proton transport in radiation therapy based on MCNPX

Jabbari

Seuntjens

2014

J Med Phys

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An important requirement for proton therapy is a software for dose calculation. Monte Carlo is the most accurate method for dose calculation, but it is very slow. In this work, a method is developed to improve the speed of dose calculation. The method is based on pre-generated tracks for particle transport. The MCNPX code has been used for generation of tracks. A set of data including the track of the particle was produced in each particular material (water, air, lung tissue, bone, and soft tissue). This code can transport protons in wide range of energies (up to 200 MeV for proton). The validity of the fast Monte Carlo (MC) code is evaluated with data MCNPX as a reference code. While analytical pencil beam algorithm transport shows great errors (up to 10%) near small high density heterogeneities, there was less than 2% deviation of MCNPX results in our dose calculation and isodose distribution. In terms of speed, the code runs 200 times faster than MCNPX. In the Fast MC code which is developed in this work, it takes the system less than 2 minutes to calculate dose for 106 particles in an Intel Core 2 Duo 2.66 GHZ desktop computer.

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Experimental test of Monte Carlo proton transport at grazing incidence in GEANT4, FLUKA and MCNPX

Cited by 14 publications

References 27 publications

MCNPX simulation of proton dose distributions in a water phantom

MCNPX simulation of proton dose distributions in a water phantom

The physics of proton therapy

A fast Monte Carlo code for proton transport in radiation therapy based on MCNPX

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