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1.
Med Phys ; 47(9): 4616-4625, 2020 Sep.
Article in English | MEDLINE | ID: mdl-32583441

ABSTRACT

PURPOSE: An accurate assessment of out-of-field dose is necessary to estimate the risk of second cancer after radiotherapy and the damage to the organs at risk surrounding the planning target volume. Although treatment planning systems (TPSs) calculate dose distributions outside the treatment field, little is known about the accuracy of these calculations. The aim of this work is to thoroughly compare the out-of-field dose distributions given by two algorithms implemented in the Monaco TPS, with measurements and full Monte Carlo simulations. METHODS: Out-of-field dose distributions predicted by the collapsed cone convolution (CCC) and Monte Carlo (MCMonaco ) algorithms, built into the commercially available Monaco version 5.11 TPS, are compared with measurements carried out on an Elekta Axesse linear accelerator. For the measurements, ion chambers, thermoluminescent dosimeters, and EBT3 film are used. The BEAMnrc code, built on the EGSnrc system, is used to create a model of the Elekta Axesse with the Agility collimation system, and the space phase file generated is scored by DOSXYZnrc to generate the dose distributions (MCEGSnrc ). Three different irradiation scenarios are considered: (a) a 10 × 10 cm2 field, (b) an IMRT prostate plan, and (c) a three-field lung plan. Monaco's calculations, experimental measurements, and Monte Carlo simulations are carried out in water and/or in an ICRP110 phantom. RESULTS: For the 10 × 10 cm2 field case, CCC underestimated the dose, compared to ion chamber measurements, by 13% (differences relative to the algorithm) on average between the 5% and the ≈2% isodoses. MCMonaco underestimated the dose only from approximately the 2% isodose for this case. Qualitatively similar results were observed for the studied IMRT case when compared to film dosimetry. For the three-field lung plan, dose underestimations of up to ≈90% for MCMonaco and ≈60% for CCC, relative to MCEGSnrc simulations, were observed in mean dose to organs located beyond the 2% isodose. CONCLUSIONS: This work shows that Monaco underestimates out-of-field doses in almost all the cases considered. Thus, it does not describe dose distribution beyond the border of the field accurately. This is in agreement with previously published works reporting similar results for other TPSs. Analytical models for out-of-field dose assessment, MC simulations or experimental measurements may be an adequate alternative for this purpose.


Subject(s)
Particle Accelerators , Radiotherapy Planning, Computer-Assisted , Algorithms , Monte Carlo Method , Phantoms, Imaging , Radiometry , Radiotherapy Dosage
2.
Phys Med Biol ; 57(10): 3093-105, 2012 May 21.
Article in English | MEDLINE | ID: mdl-22538509

ABSTRACT

A new component module (CM) named mini multileaf collimator (mMLC) was developed for the Monte Carlo code BEAMnrc. It models the geometry of the add-on miniature multileaf collimator ModuLeaf (MRC Systems GmbH, Heidelberg, Germany, now part of Siemens, Erlangen, Germany). The new CM is partly based on the existing CM called DYNVMLC. The development was performed using a modified EGSnrc platform which enables us to work in the Microsoft Visual Studio environment. In order to validate the new CM, the PRIMUS linac with 6 MV x-rays (Siemens OCS, Concord, CA, USA) equipped with the ModuLeaf mMLC was modelled. Validation was performed by two methods: (a) a ray-tracing method to check the correct geometry of the multileaf collimator (MLC) and (b) a comparison of calculated and measured results of the following dosimetrical parameters: output factors, dose profiles, field edge position penumbra, MLC interleaf leakage and transmission values. Excellent agreement was found for all parameters. It was, in particular, found that the relationship between leaf position and field edge depending on the shape of the leaf ends can be investigated with a higher accuracy by this new CM than by measurements demonstrating the usefulness of the new CM.


Subject(s)
Miniaturization/methods , Monte Carlo Method , Acceleration , Reproducibility of Results
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