This article presents a physics-informed deep learning method for the quantitative estimation of the spatial coordinates of gamma interactions within a monolithic scintillator, with a focus on Positron Emission Tomography (PET) imaging. A Density Neural Network approach is designed to estimate the 2-dimensional gamma photon interaction coordinates in a fast lead tungstate (PbWO4) monolithic scintillator detector. We introduce a custom loss function to estimate the inherent uncertainties associated with the reconstruction process and to incorporate the physical constraints of the detector. This unique combination allows for more robust and reliable position estimations and the obtained results demonstrate the effectiveness of the proposed approach and highlights the significant benefits of the uncertainties estimation. We discuss its potential impact on improving PET imaging quality and show how the results can be used to improve the exploitation of the model, to bring benefits to the application and how to evaluate the validity of the given prediction and the associated uncertainties. Importantly, our proposed methodology extends beyond this specific use case, as it can be generalized to other applications beyond PET imaging.

本文提出了一种基于物理信息的深度学习方法，用于定量估计单体闪烁体中伽马相互作用的空间坐标，重点是正电子发射断层扫描成像。使用密度神经网络方法在快速铅钨酸盐闪烁体探测器中估计二维伽马光子相互作用坐标。我们引入了定制损失函数来估计重构过程的固有不确定性，并将探测器的物理限制纳入其中。此独特组合可实现更健壮可靠的位置估计，并且所获得的结果证明了该方法的有效性，并凸显了不确定性估计的显著益处。我们讨论了其对改善正电子发射断层扫描成像质量的潜在影响，并展示了如何利用结果来改进模型的应用并评估给定预测及相关不确定性的有效性。重要的是，我们提出的方法学不仅限于正电子发射断层扫描成像，还可以推广到其他应用领域。

快速闪烁体探测器中γ射线相互作用的深度学习重建与不确定性估计