Evaluation of variance reduction techniques in BEAMnrc Monte Carlo simulation to improve the computing efficiency

Effect of the Gaussian distribution parameters of the electron beam generated at the target on the simulated x-ray dose

The purpose of this work was to investigate by Monte Carlo method the adjustment of photon beams delivered by the medical LINear ACcelerator (LINAC) Elekta Synergy MLCi2. This study presents an optimization of the Gaussian distribution parameters of the accelerated electrons before the target simulated by two Monte Carlo codes and for three beams. The photon (x-ray) beam is produced by the interaction of accelerated electrons with the LINAC target. The electrons are accelerated by a potential difference created between the anode and the cathode of the gun and directed towards the target. In the Monte Carlo simulation, it is necessary to setup the spectrum parameters of the generated electrons to simulate the x-ray dose distribution. In this study, we modeled the LINAC geometry for photon beams 18MV and 6MV in cases Flattened (FF) and Flattening-Filter-Free (FFF). The Monte Carlo simulations are based on G4Linac_MT and GATE codes. The results of the optimized configurations determined after more than 20 tests for each beam energy show a very good agreement with the experimental measurements for different irradiation fields for the depth (PDD) and lateral (Profile) dose distribution. In all Monte Carlo calculations performed in this study, the statistical uncertainty is less than 2%. The results were also in very good agreement in terms ofγ-index analysis, for the 3%/3 mm and 2%/2 mm criteria.

Assessing the Effect of Directional Bremsstrahlung Splitting on the Output Spectra and Parameters Using BEAMnrc Monte Carlo Simulation Package

Introduction:

EGSnrc software package is one of the computational packages for Monte Carlo simulation in radiation therapy and has several subset codes. Directional bremsstrahlung splitting (DBS) is a technique that applies braking radiations in interactions in this software. This study aimed to evaluate the effect of this technique on the simulation time, uncertainty, particle number of phase-space data, and photon beam spectrum resulting from a medical linear accelerator (LINAC).

Materials and methods:

The gantry of the accelerator, including the materials and geometries of different parts, was simulated using the BEAMnrc code (a subset code in the EGSnrc package). The phase-space data were recorded in different parts of the LINAC. The DBS values (1, 10, 100, and 1000) were changed, and their effects were evaluated on the simulation parameters and output spectra.

Results:

Increasing the DBS value from 1 to 1000 resulted in an increase in the simulation time from 1.778 to 11.310 hours, and increasing the number of particles in the phase-space plane (5 590 732-180 328 382). When the DBS had been picked up from 1 to 100, the simulation uncertainty decreased by about 1.29%. In addition, the DBS increment value from 100 to 1000 leads to an increase in uncertainty and simulation time of about 0.71% and 315%, respectively.

Conclusion:

Although using the DBS technique reduces the simulation time or uncertainty, increasing the DBS from a specific value, equal to 100 in our study, increases simulation uncertainties and times. Therefore, we propose considering a specific DBS value as we obtained for the Monte Carlo simulation of photon beams produced by linear accelerators.

Towards efficient Monte Carlo N-Particle simulation of a positron emission tomography (PET) via source volume definition

Monte Carlo N-Particle (MCNP) simulation has been extensively proven in nuclear medicine imaging systems, most notably in designing and optimizing new medical imaging tools. It enables more complicated geometries and the simulation of particles passing through and interacting with materials. However, a relatively long simulation time is a drawback of Monte Carlo simulation, mainly when complex geometry exists. The current study presents an alternative variance reduction technique for a modeled positron emission tomography (PET) camera by reducing the height of the source volume definition while maintaining the geometry of the simulated model. The National Electrical Manufacturers Association (NEMA) of the International Electrotechnical Commission (IEC) PET's phantom was used with a 1 cm diameter and 7 cm height of line source placed in the middle. The first geometry was fully filled the line source with 0.50 mCi radioactivity. In contrast, the second geometry decreased the source definition to 2.4 cm in height, covering 1 cm above and below the sub-block detector level. The source volume definition approach led to a 71% reduction in the total photons to be simulated. Results showed that the proposed variance reduction strategy could produce spatial resolution as precise as fully filled geometry and sped up the simulation time by approximately 65%. Hence, this strategy can be utilized for further PET optimizing simulation studies.

A multivariate approach to determine electron beam parameters for a Monte Carlo 6 MV Linac model: Statistical and machine learning methods

PURPOSE

This study aimed to determine the optimal initial electron beam parameters of a Linac for radiotherapy with a multivariate approach using statistical and machine-learning tools.

METHODS

For MC beam commissioning, a 6 MV Varian Clinac was simulated using the Geant4 toolkit. The authors investigated the relations between simulated dose distribution and initial electron beam parameters, namely, mean energy (E), energy spread (ES), and radial beam size (RS). The goodness of simulation was evaluated by the slope of differences between the simulated and the golden beam data. The best-fit combination of the electron beam parameters that minimized the slope of dose difference was searched through multivariate methods using conventional statistical methods and machine-learning tools of the scikit-learn library.

RESULTS

Simulation results with 87 combinations of the electron beam parameters were analyzed. Regardless of being univariate or multivariate, traditional statistical models did not recommend a single parameter set simultaneously minimizing slope of dose differences for percent depth dose (PDD) and lateral dose profile (LDP). Two machine learning classification modules, RandomForestClassifier and BaggingClassifier, agreed in recommending (E = 6.3 MeV, ES = ±5.0%, RS = 1.0 mm) for predicting simultaneous acceptance of PDD and LDP.

CONCLUSIONS

The machine learning with random-forest and bagging classifier modules recommended a consistent result. It was possible to draw an optimal electron beam parameter set using multivariate methods for MC simulation of a radiotherapy 6 MV Linac.

Assessment of skin doses in small field radiotherapy for 6 MV photons and beam spectral analysis at skin surface: an EGSnrc based Monte Carlo study

This study aims at the estimation of skin doses during small field radiotherapy with 6 MV photons and analysis of beam spectra at skin surface. The EGSnrc Monte Carlo code was used for spectral analysis and dose scoring in a water phantom. Percent skin dose (PSD) was calculated at a depth of 70 µm (relative to 10 cm depth), and the effects of field size, collimation, source-to-surface distance, and tissue inhomogeneity (bone/air) below the skin were evaluated. Low-energy photons and contaminant electrons from the machine head or back-scattered from underlying tissue were found to be the major contributors to skin dose. As the field size was reduced, the beam hardened, while the photon and electron fluences at the skin decreased compared to those at the reference depth of 10 cm. This resulted in a PSD reduction for fields smaller than the reference field size. Multi leaf collimators increased the PSD (up to 4%) while variation in source-to-skin dose showed a negligible effect. A substantial increase in PSD has been observed (up to 6%) when high Z material like bone was placed below the skin. In contrast, air as underlying material decreased the skin dose. The skin dose varied considerably with various clinical and geometric parameters. It is concluded that, although the skin doses were low for small fields compared to those for the reference field, skin doses may become substantial when escalated target doses are delivered with multi leaf collimators. Moreover, the presence of high Z materials such as bones or metallic implants below the skin can result in significant enhancement of the skin dose.

Monte Carlo commissioning of radiotherapy LINAC-Introducing an improved methodology

Purpose

Monte Carlo (MC) commissioning of medical linear accelerator (LINAC) is a time-consuming process involving a comparison between measured and simulated cross beam/lateral profiles and percentage depth doses (PDDs) for various field sizes. An agreement between these two data sets is sought by trial and error method while varying the incident electron beam parameters, such as electron beam energy or width, etc. This study aims to improve the efficiency of MC commissioning of a LINAC by assessing the feasibility of using a limited number of simulated PDDs.

Materials and methods

Using EGSnrc codes, a Varian Clinac 2100 unit has been commissioned for 6 MV photon beam, and a methodology has been proposed to identify the incident electron beam parameters in a speedier fashion. Impact of voxel size in 3-dimensions and cost functions used for comparison of the measured and simulated data have been investigated along with the role of interpolation.

Results

A voxel size of 1 × 1×0.5 cm³ has been identified as suitable for accurate and fast commissioning of the LIANC. The optimum number of simulated PDDs (required for further interpolation) has been found to be five.

Conclusion

The present study suggests that PDDs alone at times can be insufficient for an unambiguous commissioning process and should be supported by including the lateral beam profiles in the process.

Validation of irtGPUMCD, a GPU-based Monte Carlo internal dosimetry framework for radionuclide therapy

PURPOSE

Monte Carlo (MC) simulations are highly desirable for dose treatment planning and evaluation in radiation oncology. This is true also in emerging nuclear medicine applications such as internal radiotherapy with radionuclides. The purpose of this study is the validation of irtGPUMCD, a GPU-based MC code for dose calculations in internal radiotherapy.

METHODS

The female and male phantoms of the International Commission on Radiological Protection (ICRP 110) were used as benchmarking geometries for this study focused on ¹⁷⁷Lu and including ^99mTc and ¹³¹I. Dose calculations were also conducted for a real patient. For phantoms, twelve anatomical structures were considered as target/source organs. The S-values were evaluated with irtGPUMCD simulations (10⁸ photons), with gamma branching ratios of ICRP 107 publication. The ¹⁷⁷Lu electrons S-values were calculated for source organs only, based on local deposition of dose in irtGPUMCD. The S-value relative difference between irtGPUMCD and IDAC-DOSE were evaluated for all targets/sources considered. A DVHs comparison with GATE was conducted. An exponential track length estimator was introduced in irtGPUMCD to increase computational efficiency.

RESULTS

The relative S-value differences between irtGPUMCD and IDAC-DOSE were <5% while this comparison with GATE was <1%. The DVHs dosimetric indices comparison between GATE and irtGPUMCD for the patient led to an excellent agreement (<2%). The time required for the simulation of 10⁸ photons was 1.5 min for the female phantom, and one minute for the real patient (<1% uncertainty). These results are promising and let envision the use of irtGPUMCD for internal dosimetry in clinical applications.