A Machine Learning approach to the problem of calculating the proton paths inside a scanned object in proton Computed Tomography is presented. The method is developed in order to mitigate the loss in both spatial resolution and quantitative integrity of the reconstructed images caused by multiple Coulomb scattering of protons traversing the matter. Two Machine Learning models were used: a forward neural network (NN) and the XGBoost method. A heuristic approach, based on track averaging was also implemented in order to evaluate the accuracy limits on track calculation, imposed by the statistical nature of the scattering. Synthetic data from anthropomorphic voxelized phantoms, generated by the Monte Carlo (MC) Geant4 code, were utilized to train the models and evaluate their accuracy, in comparison to a widely used analytical method that is based on likelihood maximization and Fermi−Eyges scattering model. Both NN and XGBoost model were found to perform very close or at the accuracy limit, further improving the accuracy of the analytical method (by 12% in the typical case of 200 MeV protons on 20 cm of water object), especially for protons scattered at large angles. Inclusion of the material information along the path in terms of radiation length did not show improvement in accuracy for the phantoms simulated in the study. A NN was also constructed to predict the error in path calculation, thus enabling a criterion to filter out proton events that may have a negative effect on the quality of the reconstructed image. By parametrizing a large set of synthetic data, the Machine Learning models were proved capable to bring—in an indirect and time efficient way—the accuracy of the MC method into the problem of proton tracking.

提出了一种用于在质子计算机断层扫描中计算扫描对象内部质子路径问题的机器学习方法。开发该方法是为了减轻由穿过物质的质子的多次库仑散射引起的重建图像的空间分辨率和定量完整性的损失。使用了两种机器学习模型：前向神经网络 (NN) 和 XGBoost 方法。还实施了基于轨迹平均的启发式方法，以评估由散射的统计性质强加的轨迹计算的精度限制。由蒙特卡罗 (MC) Geant4 代码生成的拟人体素化体模的合成数据用于训练模型并评估其准确性，与基于似然最大化和 Fermi-Eyges 散射模型的广泛使用的分析方法相比。发现 NN 和 XGBoost 模型的性能都非常接近或处于准确度极限，进一步提高了分析方法的准确度（在 20 厘米水体上 200 MeV 质子的典型情况下提高了 12%），特别是对于散射在大角度。在辐射长度方面包含沿路径的材料信息并没有显示研究中模拟的体模的准确性有所提高。还构建了一个神经网络来预测路径计算中的误差，从而使标准能够过滤掉可能对重建图像的质量产生负面影响的质子事件。通过参数化大量合成数据，