To develop a machine learning-based framework for accurately modeling the anode heel effect in Monte Carlo simulations of X-ray imaging systems, enabling realistic beam intensity profiles with minimal experimental calibration. 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 Monte Carlo toolkits to evaluate their integration feasibility and predictive performance. 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 Monte Carlo simulations demonstrated that the GBR-based model closely replicated clinical beam profiles and significantly outperformed conventional symmetric beam models. This study presents a robust and generalizable method for incorporating the anode heel effect into Monte Carlo 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.

翻译：本研究旨在开发一种基于机器学习的框架，用于在X射线成像系统的蒙特卡洛模拟中精确建模阳极足跟效应，从而以最少的实验校准实现真实的束流强度分布。我们训练了多个回归模型，利用在不同管电压下通过束流测量获得的权重来预测沿阳极-阴极轴的空间强度变化，这些权重捕捉了阳极足跟效应引入的不对称性。研究建立了一种系统的微调方案，以在保持模型精度的同时最小化所需测量次数。模型在OpenGATE 10和GGEMS蒙特卡洛工具包中实现，以评估其集成可行性和预测性能。在所有测试模型中，梯度提升回归（GBR）实现了最高的精度，其预测误差在所有能级下均保持在5%以下。优化后的微调策略每个能级仅需六个探测器位置，将测量工作量减少了65%。通过此微调过程引入的最大误差保持在2%以下。蒙特卡洛模拟中的剂量Actor比较表明，基于GBR的模型能紧密复现临床束流分布，并显著优于传统的对称束流模型。本研究提出了一种稳健且可推广的方法，利用机器学习将阳极足跟效应纳入蒙特卡洛模拟。该方法能够利用有限的校准数据实现精确的、能量依赖的束流建模，从而增强了模拟在临床剂量学、图像质量评估和辐射防护等应用中的真实性。