Machine learning for proton path tracking in proton computed tomography.
Machine learning for proton path tracking in proton computed tomography.
复制标题
质子计算机断层扫描中质子路径跟踪的机器学习。
DOI：
10.1088/1361-6560/abf1fd
复制
发表时间：
2021
影响因子：
3.5
通讯作者：
Lazos D
中科院分区：
文献类型：
--
作者：
Lazos D
提出了一种基于机器学习的质子CT中质子路径计算方法。该方法的发展，以减轻损失的空间分辨率和定量完整性的重建图像所造成的多次库仑散射的质子穿越的问题。使用了两种机器学习模型：前向神经网络（NN）和XGBoost方法。一个启发式的方法，跟踪平均的基础上，也实现了为了评估的精度限制跟踪计算，所施加的统计性质的散射。由Monte Carlo（MC）Geant 4代码生成的拟人化体素化幻影的合成数据用于训练模型并评估其准确性，与广泛使用的基于似然最大化和Fermi-Eyges散射模型的分析方法进行比较。发现NN和XGBoost模型的性能非常接近或达到准确度极限，进一步提高了分析方法的准确度（在20 cm水体上200 MeV质子的典型情况下，提高了12%），特别是对于以大角度散射的质子。包含辐射长度方面的路径沿着材料信息并未显示研究中模拟体模的准确度提高。还构建了一个NN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质量产生负面影响的质子事件。通过参数化大量的合成数据，机器学习模型被证明能够以间接和时间有效的方式将MC方法的准确性带入质子跟踪问题。
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.