Fast, accurate photon beam accelerator modeling using <scp>BEAMnrc</scp>: A systematic investigation of efficiency enhancing methods and cross‐section data

Fragoso, M; Kawrakow, I.; Faddegon, Bruce A.; Solberg, Timothy D.; Chetty, Indrin J.

doi:10.1118/1.3253300

Cited by 21 publications

(12 citation statements)

References 52 publications

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“…However, the accuracy in bremsstrahlung tail calculation was improved by using EGSnrc instead of EGS4. 46,47 In summary, electron beams, especially for large fields, are more difficult to simulate to high accuracy than photon beams, mainly because electron beams are very sensitive to the materials and geometries of linac head components and possible problems with measurement techniques. Therefore, for accurate simulation of electron beams, detailed and accurate geometric descriptions of all components in the beam path and accurate dose measurements are necessary.…”

Section: B Electron Beam Simulationmentioning

confidence: 99%

Beam modeling and beam model commissioning for Monte Carlo dose calculation‐based radiation therapy treatment planning: Report of AAPM Task Group 157

et al. 2019

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Dose calculation plays an important role in the accuracy of radiotherapy treatment planning and beam delivery. The Monte Carlo (MC) method is capable of achieving the highest accuracy in radiotherapy dose calculation and has been implemented in many commercial systems for radiotherapy treatment planning. The objective of this task group was to assist clinical physicists with the potentially complex task of acceptance testing and commissioning MC-based treatment planning systems (TPS) for photon and electron beam dose calculations. This report provides an overview on the general approach of clinical implementation and testing of MC-based TPS with a specific focus on models of clinical photon and electron beams. Different types of beam models are described including those that utilize MC simulation of the treatment head and those that rely on analytical methods and measurements. The trade-off between accuracy and efficiency in the various source-modeling approaches is discussed together with guidelines for acceptance testing of MC-based TPS from the clinical standpoint. Specific recommendations are given on methods and practical procedures to commission clinical beam models for MC-based TPS.

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Section: B Electron Beam Simulationmentioning

confidence: 99%

Beam modeling and beam model commissioning for Monte Carlo dose calculation‐based radiation therapy treatment planning: Report of AAPM Task Group 157

et al. 2019

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“…A research version of the TPS RayStation version 6 was used for all MC calculations in this study. The TPS incorporates VMC++, a MC code designed for electron/photon dose calculations in a voxelized geometry with photon interaction cross sections from the XCOM database . VMC++ has been validated for photon energies down to 20 keV by comparison with calculations using the EGSnrc MC code .…”

Section: Methodsmentioning

confidence: 99%

Toward automated and personalized organ dose determination in CT examinations — A comparison of two tissue characterization models for Monte Carlo organ dose calculation with a Therapy Planning System

2019

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Purpose Computed tomography (CT) is a versatile tool in diagnostic radiology with rapidly increasing number of examinations per year globally. Routine adaption of the exposure level for patient anatomy and examination protocol cause the patients' exposures to become diversified and harder to predict by simple methods. To facilitate individualized organ dose estimates, we explore the possibility to automate organ dose calculations using a radiotherapy treatment planning system (TPS). In particular, the mapping of CT number to elemental composition for Monte Carlo (MC) dose calculations is investigated. Methods Organ dose calculations were done for a female thorax examination test case with a TPS (Raystation™, Raysearch Laboratories AB, Stockholm, Sweden) utilizing a MC dose engine with a CT source model presented in a previous study. The TPS's inherent tissue characterization model for mapping of CT number to elemental composition of the tissues was calibrated using a phantom with known elemental compositions and validated through comparison of MC calculated dose with dose measured with Thermo Luminescence Dosimeters (TLD) in an anthropomorphic phantom. Given the segmentation tools of the TPS, organ segmentation strategies suitable for automation were analyzed for high contrast organs, utilizing CT number thresholding and model‐based segmentation, and for low contrast organs utilizing water replacements in larger tissue volumes. Organ doses calculated with a selection of organ segmentation methods in combination with mapping of CT numbers to elemental composition (RT model), normally used in radiotherapy, were compared to a tissue characterization model with organ segmentation and elemental compositions defined by replacement materials [International Commission on Radiological Protection (ICRP) model], frequently favored in imaging dosimetry. Results The results of the validation with the anthropomorphic phantom yielded mean deviations from the dose to water calculated with the RT and ICRP model as measured with TLD of 1.1% and 1.5% with maximum deviations of 6.1% and 8.7% respectively over all locations in the phantom. A strategy for automated organ segmentation was evaluated for two different risk organ groups, that is, low contrast soft organs and high contrast organs. The relative deviation between organ doses calculated with the RT model and with the ICRP model varied between 0% and 20% for the thorax/upper abdomen risk organs. Conclusions After calibration, the RT model in the TPS provides accurate MC dose results as compared to measurements with TLD and the ICRP model. Dosimetric feasible segmentation of the risk organs for a female thorax demonstrates a possibility for automation using the segmentation tool available in a TPS for high contrast organs. Low contrast soft organs can be represented by water volumes, but organ dose to the esophagus and thyroid must be determined using standardized organ shapes. The uncertainties of the organ doses are small compared to the overall uncertainty, at least an order...

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“…Variance reduction techniques (VRT) have been used in conventional radiotherapy calculations to reduce the simulation time to a more clinically practical level. [3][4][5][6][7][8] The aim of a VRT in conventional radiotherapy is to increase the number of rare events of interest (e.g., the frequency of bremsstrahlung) or the secondary particles produced in these events, without adding systematic errors. 9 As a result, the simulation time to produce these events is reduced while maintaining unbiased results.…”

Section: Introductionmentioning

confidence: 99%

Geometrical splitting technique to improve the computational efficiency in Monte Carlo calculations for proton therapy

et al. 2013

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Purpose: To present the implementation and validation of a geometrical based variance reduction technique for the calculation of phase space data for proton therapy dose calculation. Methods:The treatment heads at the Francis H Burr Proton Therapy Center were modeled with a new Monte Carlo tool (TOPAS based on Geant4). For variance reduction purposes, two particlesplitting planes were implemented. First, the particles were split upstream of the second scatterer or at the second ionization chamber. Then, particles reaching another plane immediately upstream of the field specific aperture were split again. In each case, particles were split by a factor of 8. At the second ionization chamber and at the latter plane, the cylindrical symmetry of the proton beam was exploited to position the split particles at randomly spaced locations rotated around the beam axis. Phase space data in IAEA format were recorded at the treatment head exit and the computational efficiency was calculated. Depth-dose curves and beam profiles were analyzed. Dose distributions were compared for a voxelized water phantom for different treatment fields for both the reference and optimized simulations. In addition, dose in two patients was simulated with and without particle splitting to compare the efficiency and accuracy of the technique. Results: A normalized computational efficiency gain of a factor of 10-20.3 was reached for phase space calculations for the different treatment head options simulated. Depth-dose curves and beam profiles were in reasonable agreement with the simulation done without splitting: within 1% for depth-dose with an average difference of (0.2 ± 0.4)%, 1 standard deviation, and a 0.3% statistical uncertainty of the simulations in the high dose region; 1.6% for planar fluence with an average difference of (0.4 ± 0.5)% and a statistical uncertainty of 0.3% in the high fluence region. The percentage differences between dose distributions in water for simulations done with and without particle splitting were within the accepted clinical tolerance of 2%, with a 0.4% statistical uncertainty. For the two patient geometries considered, head and prostate, the efficiency gain was 20.9 and 14.7, respectively, with the percentages of voxels with gamma indices lower than unity 98.9% and 99.7%, respectively, using 2% and 2 mm criteria. Conclusions: The authors have implemented an efficient variance reduction technique with significant speed improvements for proton Monte Carlo simulations. The method can be transferred to other codes and other treatment heads.

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Fast, accurate photon beam accelerator modeling using BEAMnrc: A systematic investigation of efficiency enhancing methods and cross‐section data

Cited by 21 publications

References 52 publications

Beam modeling and beam model commissioning for Monte Carlo dose calculation‐based radiation therapy treatment planning: Report of AAPM Task Group 157

Beam modeling and beam model commissioning for Monte Carlo dose calculation‐based radiation therapy treatment planning: Report of AAPM Task Group 157

Toward automated and personalized organ dose determination in CT examinations — A comparison of two tissue characterization models for Monte Carlo organ dose calculation with a Therapy Planning System

Geometrical splitting technique to improve the computational efficiency in Monte Carlo calculations for proton therapy

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