Uncertainty-aware gamma interaction localization and reconstruction in PET.

backgroundPrecise localization of gamma-ray interactions inside scintillation detectors is essential for high-resolution positron emission tomography (PET) imaging. Although machine learning methods have demonstrated strong performance in gamma interaction positioning, most existing approaches do not quantify event-level uncertainty, leaving valuable information unused. PURPOSE: This study quantifies event-wise positional uncertainties in gamma interaction localization and demonstrates its utility for improving PET image quality through uncertainty-aware filtering and weighting.

methodsWe employ machine learning to perform gamma interaction positioning in a semi-monolithic scintillation detector block with overall dimensions of

resultsAcross architectures, uncertainty-aware models achieved high positioning accuracy. CNN classifiers provided the best planar performance, while CNN regressors performed best for depth-of-interaction (DOI) estimation. Variance- and energy-based filtering substantially improved positioning accuracy, reducing the MAE to

conclusionsEvent-level uncertainty estimation enables meaningful filtering and weighting strategies that improve interaction positioning and reconstructed PET image quality. Despite the trade-off between enhanced spatial resolution and increased noise, the uncertainty-aware framework introduces a new reconstruction parameter that can be exploited to improve image quality and potentially support more reliable quantitative PET imaging.