Oliver Lodge, Department of Physics, University of Liverpool, Oxford Street, L69 7ZE Liverpool, United Kingdom.
Fondazione Bruno Kessler (FBK), Via Sommarive, 18, Povo, I-38123, Trento, Italy.
Phys Med Biol. 2021 Apr 6;66(7). doi: 10.1088/1361-6560/abf00f.
The most likely path formalism (MLP) is widely established as the most statistically precise method for proton path reconstruction in proton computed tomography. 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.
最可能路径形式（MLP）被广泛认为是质子计算机断层扫描中质子路径重建最精确的统计方法。然而，尽管该方法考虑了小角度多次库仑散射（MCS）和能量损失，但非弹性核相互作用在大量质子路径中起着重要作用。通过基于能量和方向的切割，受核相互作用影响的轨迹在很大程度上被排除在 MLP 分析之外。在这项工作中，我们提出了一种基于深度神经网络（DNN）的新方法来估计质子路径。通过这种方法，在仅发生 MCS 的情况下，我们实现了与 MLP 预测相当的质子路径估计，并且在存在核相互作用时提高了准确性。此外，我们的测试表明，DNN 算法可以比 MLP 算法快得多。
