Enhancing Precision in Radiotherapy Delivery: Validating Monte Carlo Simulation Models for 6 MV Elekta Synergy Agility LINAC Photon Beam Using Two Models of the GAMOS Code

The most crucial requirement in radiation therapy treatment planning is a fast and accurate treatment planning system that minimizes damage to healthy tissues surrounding cancer cells. The use of Monte Carlo toolkits has become indispensable for research aimed at precisely determining the dose in radiotherapy. Among the numerous algorithms developed in recent years, the GAMOS code, which utilizes the Geant4 toolkit for Monte Carlo simula-tions, incorporates various electromagnetic physics models and multiple scattering models for simulating particle interactions with matter. This makes it a valuable tool for dose calculations in medical applications and throughout the patient’s volume. The aim of this present work aims to vali-date the GAMOS code for the simulation of a 6 MV photon-beam output from the Elekta Synergy Agility linear accelerator. The simulation involves mod-eling the major components of the accelerator head and the interactions of the radiation beam with a homogeneous water phantom and particle information was collected following the modeling of the phase space. This space was po-sitioned under the X and Y jaws, utilizing three electromagnetic physics mod-els of the GAMOS code: Standard, Penelope, and Low-Energy, along with three multiple scattering models: Goudsmit-Saunderson, Urban, and Wentzel-VI. The obtained phase space file was used as a particle source to simulate dose distributions (depth-dose and dose profile) for field sizes of 5 × 5 cm2 and 10 × 10 cm2 at depths of 10 cm and 20 cm in a water phantom, with a source-surface distance (SSD) of 90 cm from the target. We compared the three electromagnetic physics models and the three multiple scattering mod-els of the GAMOS code to experimental results. Validation of our results was performed using the gamma index, with an acceptability criterion of 3% for the dose difference (DD) and 3 mm for the distance-to-agreement (DTA). We achieved agreements of 94% and 96%, respectively, between simulation and experimentation for the three electromagnetic physics models and three mul-tiple scattering models, for field sizes of 5 × 5 cm2 and 10 × 10 cm2 for depth-dose curves. For dose profile curves, a good agreement of 100% was found between simulation and experimentation for the three electromagnetic physics models, as well as for the three multiple scattering models for a field size of 5 × 5 cm2 at 10 cm and 20 cm depths. For a field size of 10 × 10 cm2, the Penelope model dominated with 98% for 10 cm, along with the three multiple scattering models. The Penelope model and the Standard model, along with the three multiple scattering models, dominated with 100% for 20 cm. Our study, which compared these different GAMOS code models, can be crucial for enhancing the accuracy and quality of radiotherapy, contributing to more effective patient treatment. Our research compares various electro-magnetic physics models and multiple scattering models with experimental measurements, enabling us to choose the models that produce the most reli-able results, thereby directly impacting the quality of simulations. This en-hances confidence in using these models for treatment planning. Our re-search consistently contributes to the progress of Monte Carlo simulation techniques in radiation therapy, enriching the scientific literature.

基于GAMOS的蒙特卡罗方法模拟放疗电子线照射的剂量学研究

目的:探讨基于GAMOS的蒙特卡罗(MC)方法模拟电子线放疗的剂量精确性。方法:运用GAMOSMC程序,建立Varian Rapidarc加速器3档能量(6、9和12 MeV)及3种限光筒[(6×6)、(10×10)和(15×15)cm2]的束流模型,模拟束流在水模体中的剂量分布,并与测量得到的百分深度剂量和等平面剂量分布比较,评估GAMOS软件模拟电子线照射的精确性和运算效率。结果:模拟的粒子数越多,模拟与测量结果的误差越小;当模拟粒子的数量达到5×108时,各个能量的电子线射程(Rp)和50%剂量深度(R50)的模拟结果与测量结果一致;除建成区外,百分深度剂量模拟和测量的结果误差在2%以内;等平面剂量分布模拟和测量的结果误差也在2%以内,模拟的照射野大小与测量结果一致。运算效率中,能量越大,限光筒尺寸越大,并行同步模拟所用的时间越多,模拟时间的变化越大。结论:基于GAMOS的MC方法可准确地模拟放疗电子线照射剂量的分布,粒子数的增加可提高模拟的精确性,并行同步计算可提高模拟的效率。

Impacts of livelihood assets on livelihood security in drought-prone Gamo lowlands of southwest Ethiopia

Livelihood assets are a matter of high concern for secured survival.Drought-prone Gamo lowland households have differential access to livelihood resources which indicates the varying capacity of resisting to shocks.The main objective of this study is to explore the impacts of livelihood assets on livelihood security in the drought-prone Gamo lowlands.Multistage sampling procedures were employed to select the study sites and sample respondents.Primary data of households’capital assets and livelihood security status were produced from 285 survey households,agricultural experts,key informants,focus group discussants,and field observation through transect walks.Descriptive and inferential statistics were used to analyze quantitative data,whereas discussions and annotations were employed for analyzing qualitative data.The Sustainable Livelihoods Framework is used with modifications to schematize the study conceptually.The findings indicated that the study households possessed combinations of livelihood resources differentially.Financial and natural capitals were found to be the most deficient and better-accessed capitals,respectively.The study also showed that lowland residents’access to assets has significant indications of livelihood security.Households’poor access to assets such as financial,information,and social capital demands raised attention of the concerned stakeholders and policy debates in the drought-prone rural setup.Hence,it has been concluded that the more assets are accessed,the stronger the capacity of the households to resist shocks,and better the livelihood security.Accordingly,enhancing people’s access to multiple livelihood assets is suggested to sustainably secure livelihoods.

Assessment of dead layers thickness of an HPGe detector after an extended operating period using response surface methodology and Box–Behnken design

Purpose This work aims to study the increase in dead layer thickness of an HPGe N-type detector during its operational period from 2012 to 2018.Methods The dead layer was examined along three Ge-crystal surfaces,such as outer frontal,outer lateral,and inner lateral.These parameters were optimized using response surface methodology(RSM)with a Box–Behnken design(BBD).The Monte Carlo calculations using the GAMOS(Geant4-based Architecture for Medicine-Oriented Simulations)code were performed to evaluate the detector’s efficiency at different values of the inactive germanium layer.Results and conclusion The optimal combination of dead layer thickness has been identified using the desirability function approach,which is a useful tool to optimize multi-response problems.To find the variation in dead layer thickness over the operational period,the optimization procedure was reiterated for both experimental efficiencies measured in 2012 and 2018.The obtained results show that dead layers thickness has increased from 0.6141 mm to 0.7447 mm,0.0803 mm to 2.2721 mm,and 1.5012 mm to 1.6091 mm for the outer frontal,outer lateral,and inner lateral surfaces,respectively.