Uncertainty-aware gamma interaction localization and reconstruction in PET
Abstract
Background: Precise 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.
Methods: We employ machine learning to perform gamma interaction positioning in a semi-monolithic scintillation detector block with overall dimensions of 48.4×48.3×10mm3$48.4 \times 48.3 \times 10 \,\mathrm{mm}^3$ , corresponding to the planar-segmented, planar-monolithic, and depth-of-interaction (DOI) directions. We train multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) using silicon photomultiplier (SiPM) light spread measurements as network inputs. Regression models were trained using a Gaussian negative log-likelihood to jointly estimate gamma interaction coordinates and event-wise positional variance. Classifiers were evaluated, which inferred event-level uncertainty from the variance of the predicted spatial probability distribution. These uncertainty estimates primarily reflect variability in detector response and photon statistics, corresponding to aleatoric…
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