Determination of the Small-Field Output Factor for 6 MV Photon Beam Using EGSnrc Monte Carlo

Accuracy of ionization chamber (IC) to measure the scatter output factor (Scp) of a linear accelerator (linac) is crucial, especially in small field (<4 cm × 4 cm). The common IC volume of 0.6 cc is not adequate for small-field measurement and not all radiotherapy centers can afford to purchase additional IC due to the additional cost. This study aimed to determine the efficiency of the EGSnrc Monte Carlo (MC) to calculate the Scp for various field sizes including small field in Elekta Synergy (Agility multileaf collimator) linac. The BEAMnrc and DOSXYZnrc user codes were used to simulate a 6 MV linac model for various field sizes and calculate the radiation dose output in water phantom. The modeled linac treatment head was validated by comparing the percentage depth dose (PDD), beam profile, and beam quality (Tissue Phantom Ratio (TPR)20,10) with the IC measurement. The validated linac model was simulated to calculate the Scp consisting of collimator scatter factor (Sc) and phantom scatter factor (Sp). The PDD and beam profile of the simulated field sizes were within a good agreement of ±2% compared with the measured data. The TPR20,10 value was 0.675 for field size 10 cm × 10 cm. The Scp, Sc, and Sp simulated values were close to the IC measurement within ±2% difference. The simulation for Sc and Sp in 3 cm × 3 cm field size was calculated to be 0.955 and 0.884, respectively. In conclusion, this study validated the efficiency of the MC simulation as a promising tool for the Scp calculation including small-field size for linac.

Dosimetric characterization of a small-scale (Zn,Cd)S:Ag inorganic scintillating detector to be used in radiotherapy

PURPOSE

In modern radiotherapy techniques, to ensure an accurate beam modeling process, dosimeters with high accuracy and spatial resolution are required. Therefore, this work aims to propose a simple, robust, and a small-scale fiber-integrated X-ray inorganic detector and investigate the dosimetric characteristics used in radiotherapy.

METHODS

The detector is based on red-emitting silver-activated zinc-cadmium sulfide (Zn,Cd)S:Ag nanoclusters and the proposed system has been tested under 6 MV photons with standard dose rate used in the patient treatment protocol. The article presents the performances of the detector in terms of dose linearity, repeatability, reproducibility, percentage depth dose distribution, and field output factor. A comparative study is shown using a microdiamond dosimeter and considering data from recent literature.

RESULTS

We accurately measured a small field beam profile of 0.5 × 0.5 cm² at a spatial resolution of 100 µm using a LINAC system. The dose linearity at 400 MU/min has shown less than 0.53% and 1.10% deviations from perfect linearity for the regular and smallest field. Percentage depth dose measurement agrees with microdiamond measurements within 1.30% and 2.94%, respectively for regular to small field beams. Besides, the stem effect analysis shows a negligible contribution in the measurements for fields smaller than 3x3 cm². This study highlights the drastic decrease of the convolution effect using a point-like detector, especially in small dimension beam characterization. Field output factor has shown a good agreement while comparing it with the microdiamond dosimeter.

CONCLUSION

All the results presented here anticipated that the developed detector can accurately measure delivered dose to the region of interest, claim accurate depth dose distribution hence it can be a suitable candidate for beam characterization and quality assurance of LINAC system.

Beam data modeling of linear accelerators (linacs) through machine learning and its potential applications in fast and robust linac commissioning and quality assurance

BACKGROUND AND PURPOSE

To propose a novel machine learning-based method for reliable and accurate modeling of linac beam data applicable to the processes of linac commissioning and QA.

MATERIALS AND METHODS

We hypothesize that the beam data is a function of inherent linac features and percentage depth doses (PDDs) and profiles of different field sizes are correlated with each other. The correlation is formulated as a multivariable regression problem using a machine learning framework. Varian TrueBeam beam data sets (n = 43) acquired from multiple institutions were used to evaluate the framework. The data sets included PDDs and profiles across different energies and field sizes. A multivariate regression model was trained for prediction of beam specific PDDs and profiles of different field sizes using a 10 × 10 cm² field as input.

RESULTS

Predictions of PDDs were achieved with a mean absolute percent relative error (%RE) of 0.19-0.35% across the different beam energies investigated. The maximum mean absolute %RE was 0.93%. For profile prediction, the mean absolute %RE was 0.66-0.93% with a maximum absolute %RE of 3.76%. The largest uncertainties in the PDD and profile predictions were found at the build-up region and at the field penumbra, respectively. The prediction accuracy increased with the number of training sets up to around 20 training sets.

CONCLUSIONS

Through this novel machine learning-based method we have shown accurate and reproducible generation of beam data for linac commissioning for routine radiation therapy. This method has the potential to simplify the linac commissioning procedure, save time and manpower while increasing the accuracy of the commissioning process.

Monte carlo model and output factors of elekta infinity™ 6 and 10 MV photon beam

Aim

This study aimed to commission the Elekta Infinity™ working in 6 and 10 MV photon beam installed in Concord International Hospital, Singapore, and compare the OFs between MC simulation and measurement using PTW semiflex and microDiamond detector for small field sizes.

Material and Methods

There are two main steps in this study: modelling of Linac 6 and 10 MV photon beam and analysis of the output factors for field size 2 × 2-10 × 10 cm². The EGSnrc/BEAMnrc-DOSXYZnrc code was used to model and characterize the Linac and to calculate the dose distributions in a water phantom. The dose distribution and OFs were compared to the measurement data in the same condition.

Results

The commissioning process was only conducted for a 10 × 10 cm² field size. The PDD obtained from MC simulation showed a good agreement with the measurement. The local dose difference of PDDs was less than 2% for 6 and 10 MV. The initial electron energy was 5.2 and 9.4 MeV for 6 and 10 MV photon beam, respectively. This Linac model can be used for dose calculation in other situations and different field sizes because this Linac has been commissioned and validated using Monte Carlo simulation. The 10 MV Linac produces higher electron contamination than that of 6 MV.

Conclusions

The Linac model in this study was acceptable. The most important result in this work comes from OFs resulted from MC calculation. This value was more significant than the OFs from measurement using semiflex and microDiamond for all beam energy and field sizes because of the CPE phenomenon.