Deep Learning reconstruction with uncertainty estimation for $\gamma$ photon interaction in fast scintillator detectors

TL;DR

This paper introduces a physics-informed deep learning method with uncertainty estimation for accurately determining gamma photon interaction positions in scintillator detectors, enhancing PET imaging quality and robustness.

Contribution

It presents a novel density neural network approach with a custom loss function that estimates both positions and uncertainties, incorporating physical detector constraints.

Findings

Effective position estimation demonstrated in lead tungstate scintillators

Uncertainty estimation improves robustness and reliability

Potential to enhance PET imaging quality

Abstract

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.