论文标题
PCT使用神经网络重建的质子路径重建
Proton path reconstruction for pCT using Neural Networks
论文作者
论文摘要
最可能的路径形式主义（MLP）被广泛确定为质子计算机断层扫描（PCT）中质子路径重建最精确的方法。但是，尽管这种方法解释了小角度的多重库仑散射（MC）和能量损失，但非弹性核相互作用在大量质子路径中起着影响力。通过基于能量和方向的削减，受核相互作用影响的轨道主要从MLP分析中丢弃。在这项工作中，我们提出了一种基于深神经网络（DNN）的质子路径的新方法。通过这种方法，在只有MCS发生的情况下，在存在核相互作用时，对等效于MLP预测的质子路径的估计得到了实现。此外，我们的测试表明，DNN算法的速度可以比MLP算法快得多。
The Most Likely Path formalism (MLP) is widely established as the most statistically precise method for proton path reconstruction in proton computed tomography (pCT). However, while this method accounts for small-angle Multiple Coulomb Scattering (MCS) and energy loss, inelastic nuclear interactions play an influential role in a significant number of proton paths. By applying cuts based on energy and direction, tracks influenced by nuclear interactions are largely discarded from the MLP analysis. In this work we propose a new method to estimate the proton paths based on a Deep Neural Network (DNN). Through this approach, estimates of proton paths equivalent to MLP predictions have been achieved in the case where only MCS occurs, together with an increased accuracy when nuclear interactions are present. Moreover, our tests indicate that the DNN algorithm can be considerably faster than the MLP algorithm.
