Related Experiment Video
Updated: Aug 19, 2026

Surrogate Model Development for Digital Experiments in Welding
Published on: March 28, 2025
Treatment simulation approaches for the estimation of the distributions of treatment quality parameters generated by
1Sektion für Biomedizinische Physik, Universitätsklinik für Radioonkologie Tübingen, D-72076 Tübingen, Germany.
Abstract:
Geometric uncertainties arise during treatment planning and treatment and mean that dose-dependent parameters such as EUD are random variables with a patient specific probability distribution. Treatment planning with highly conformal treatment techniques such as intensity modulated radiation therapy requires new evaluation tools which allow us to estimate this influence of geometrical uncertainties on the probable treatment dose for a planned dose distribution. Monte Carlo simulations of treatment courses with recalculation of the dose according to the daily geometric errors are a gold standard for such an evaluation. Distribution histograms which show the relative frequency of a treatment quality parameter in the treatment simulations can be used to evaluate the potential risks and chances of a planned dose distribution. As treatment simulations with dose recalculation are very time consuming for sufficient statistical accuracy, it is proposed to do treatment simulations in the dose parameter space where the result is mainly determined by the systematic and random component of the geometrical uncertainties. Comparison of the parameter space simulation method with the gold standard for prostate cases and a head and neck case shows good agreement as long as the number of fractions is high enough and the influence of tissue inhomogeneities and surface curvature on the dose is small.
Related Concept Videos
Distributions to Estimate Population Parameter
Propagation of Uncertainty from Random Error
Random Error
Contaminants and Errors
Another key consideration is determining the appropriate number of samples required to...
Choosing Between z and t Distribution
Uncertainty in Measurement: Accuracy and Precision
