Deep Learning reconstruction with uncertainty estimation for γ photon interaction in fast scintillator detectors
Résumé
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. The authors propose a density neural
network approach to estimate the 2-dimensional gamma photon interaction coordinates for
a fast lead tungstate (PbWO$_4$) monolithic scintillator detector. This approach considers
uncertainties on the estimated coordinates and accounts for physical constraints on the
detector. The results demonstrate the effectiveness of the proposed approach for accurate
position estimation and the benefits of the uncertainties estimation. The authors discuss its
potential impact on improving PET imaging quality and highlight the generalization of the
proposed methodology for other applications.
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AAIMME_Paper.pdf (4.67 Mo)
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