Pencil beam (PB) dose calculation is fast but inaccurate due to the approximations when dealing with inhomogeneities. Monte Carlo (MC) dose calculation is the most accurate method but it is time consuming. The aim of this study was to develop a deep learning model that can boost the accuracy of PB dose calculation to the level of MC dose by converting PB dose to MC dose for different tumor sites. The proposed model uses the PB dose and computed tomography image as inputs to generate the MC dose. We used 290 patients (90 head and neck, 93 liver, 75 prostate and 32 lung) to train, validate, and test the model. For each tumor site, we performed four numerical experiments to explore various combinations of training datasets. Training the model on data from all tumor sites together and using the dose distribution of each individual beam as input yielded the best performance for all four tumor sites. The average gamma passing rate (1 mm/1%) between the converted and the MC dose was 92.8%, 92.7%, 89.7% and 99.6% for head and neck, liver, lung, and prostate test patients, respectively. The average dose conversion time for a single field was less than 4 s. The trained model can be adapted to new datasets through transfer learning. Our deep learning-based approach can quickly boost the accuracy of PB dose to that of MC dose. The developed model can be added to the clinical workflow of proton treatment planning to improve dose calculation accuracy.

笔形束 (PB) 剂量计算速度快，但由于处理不均匀性时的近似值而不够准确。蒙特卡罗（MC）剂量计算是最准确的方法，但非常耗时。本研究的目的是开发一种深度学习模型，通过将不同肿瘤部位的 PB 剂量转换为 MC 剂量，将 PB 剂量计算的准确性提高到 MC 剂量的水平。所提出的模型使用 PB 剂量和计算机断层扫描图像作为输入来生成 MC 剂量。我们使用 290 名患者（90 名头颈患者、93 名肝脏患者、75 名前列腺患者和 32 名肺患者）来训练、验证和测试该模型。对于每个肿瘤部位，我们进行了四次数值实验来探索训练数据集的各种组合。使用来自所有肿瘤部位的数据一起训练模型，并使用每个单独光束的剂量分布作为输入，为所有四个肿瘤部位产生了最佳性能。对于头颈、肝脏、肺和前列腺测试患者，转换剂量和 MC 剂量之间的平均伽马通过率 (1 mm/1%) 分别为 92.8%、92.7%、89.7% 和 99.6%。单视野平均剂量转换时间小于4秒。经过训练的模型可以通过迁移学习适应新的数据集。我们基于深度学习的方法可以快速将 PB 剂量的准确性提高到 MC 剂量的准确性。开发的模型可以添加到质子治疗计划的临床工作流程中，以提高剂量计算的准确性。