Machine learning-based modeling of the anode heel effect in x-ray Beam Monte Carlo simulations

Objective.To develop a machine learning-based framework for accurately modeling the anode heel effect in Monte Carlo(MC) simulations of x-ray imaging systems, enabling realistic beam intensity profiles with minimal experimental calibration.Approach.Multiple regression models were trained to predict spatial intensity variations along the anode-cathode axis using experimentally acquired weights derived from beam measurements across different tube potentials. These weights captured the asymmetry introduced by the anode heel effect. A systematic fine-tuning protocol was established to minimize the number of required measurements while preserving model accuracy. The models were implemented in the OpenGATE 10 and GGEMS MC toolkits to evaluate their integration feasibility and predictive performance.Main results.Among the tested models, gradient boosting regression (GBR) delivered the highest accuracy, with prediction errors remaining below 5% across all energy levels. The optimized fine-tuning strategy required only six detector positions per energy level, reducing measurement effort by 65%. The maximum error introduced through this fine-tuning process remained below 2%. Dose actor comparisons within MC simulations demonstrated that the GBR-based model closely replicated clinical beam profiles and significantly outperformed conventional symmetric beam models.Significance.This study presents a robust and generalizable method for incorporating the anode heel effect into MC simulations using machine learning. By enabling accurate, energy-dependent beam modeling with limited calibration data, the approach enhances simulation realism for applications in clinical dosimetry, image quality assessment, and radiation protection.