ESTRO 2024 - Abstract Book

S3414

Physics - Dose calculation algorithms

ESTRO 2024

The CCC algorithm demonstrates remarkable consistency by consistently producing accurate dose calculations, as reflected in a stable distribution of dose differences across various scenarios. On average, the difference is 1.068%. This reliability reaffirms its suitability for dose calculations.

The AAA algorithm excels when we apply density assignments to the dental implant or both the implant and surrounding tissue. However, it consistently leans toward overestimating doses, with an average difference of 3.3%.

The results for the Acuros algorithm introduce complexity. Regardless of the specific treatment plans or artifacts used, the distribution of dose differences appears unpredictable. While the average dose difference is 3.7%, there is a notable variance, with measured doses ranging from -1.5% to 8.23% compared to the calculated doses. This indicates that the Acuros algorithm may not be the optimal choice for this study. Further investigation is necessary to comprehend its behavior and enhance its performance.

Conclusion:

In summary, the findings indicate that CCC is a reliable algorithm, while AAA tends to overestimate doses slightly. The behavior of the Acuros algorithm in this context appears less predictable, necessitating further research to improve its alignment with study goals. These results contribute to the quest for more precise and dependable radiation therapy planning. These findings underscore the importance of algorithm selection and density assignments in the realm of treatment planning, offering valuable insights into the nuances and intricacies of dose accuracy across various scenarios and materials. Such observations contribute to the ongoing refinement and enhancement of radiation therapy protocols for improved patient outcomes.

Keywords: implants, head and neck, artifacts

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Digital Poster

Monte Carlo Simulations in the Modelling and Optimisation of Linac Bunker Shielding

Gavin Pikes 1 , Pejman Rowshan Farzad 1,2 , David Pfefferle 1

1 University of Western Australia, Physics, Perth, Australia. 2 Centre for Advanced Technologies in Cancer Research, Physics, Perth, Australia

Purpose/Objective:

Monte Carlo modelling allows for an efficient method of numerical integration through the use of random number sampling. It becomes ideal for the determination of linear accelerator bunker shielding capabilities where non standard geometry and beams are in use, meaning the traditional manual calculations provided by the National Council on Radiation Protection and Measurements become less useful. An accurate knowledge of the bunker shielding is essential for ensuring that the dose rate outside the bunker remains at an acceptable level for the safety of both radiation workers and general staff/patients/public. There is some precedent, however, to suggest that