用神经网络加速质子放疗剂量计算，还给出每一体素的不确定性
Surrogate Modelling of Proton Dose with Monte Carlo Dropout Uncertainty Quantification
- 用蒙特卡洛丢弃法构建可微分的剂量代理模型
- 速度比蒙特卡洛快数十倍，且能分离模型与输入不确定性
- 适合用于鲁棒计划、自适应治疗等需要不确定性的场景
使用蒙特卡洛（MC）进行精确质子剂量计算在鲁棒优化、自适应再计划和概率推断等流程中计算成本高昂，因需重复评估。为此，我们开发了一种集成蒙特卡洛丢弃法的神经代理模型，实现快速、可微分的剂量预测，并提供体素级预测不确定性。通过一系列实验验证：一维解析基准测试验证了准确性、收敛性和方差分解；二维骨-水幻影（使用TOPAS Geant4生成）展示了模型在域异质性和束流不确定性下的表现；三维水幻影证实了其在体积剂量预测中的可扩展性。在不同设置下，分离了认知不确定性（模型）与参数不确定性（输入），结果显示认知方差在分布偏移时增加，而参数方差在材料边界占主导。该方法在显著提升速度的同时保留了不确定性信息，适用于鲁棒规划、自适应工作流及不确定性感知优化。
原文摘要 · Abstract (English)
Accurate proton dose calculation using Monte Carlo (MC) is computationally demanding in workflows like robust optimisation, adaptive replanning, and probabilistic inference, which require repeated evaluations. To address this, we develop a neural surrogate that integrates Monte Carlo dropout to provide fast, differentiable dose predictions along with voxelwise predictive uncertainty. The method is validated through a series of experiments, starting with a one-dimensional analytic benchmark that establishes accuracy, convergence, and variance decomposition. Two-dimensional bone-water phantoms, generated using TOPAS Geant4, demonstrate the method's behavior under domain heterogeneity and beam uncertainty, while a three-dimensional water phantom confirms scalability for volumetric dose prediction. Across these settings, we separate epistemic (model) from parametric (input) contributions, showing that epistemic variance increases under distribution shift, while parametric variance dominates at material boundaries. The approach achieves significant speedups over MC while retaining uncertainty information, making it suitable for integration into robust planning, adaptive workflows, and uncertainty-aware optimisation in proton therapy.
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