3D Activity reconstruction from Angular Gamma Scanning via variational Bayes: A proof of concept

2026-02-01

PRE

Casas-Molina, Victor J. *

Laloy, Eric

Rogiers, Bart

Tom Dhaene

Couckuyt, Ivo

DOI：10.1016/j.apradiso.2026.112479

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Abstract

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This study serves as a proof of concept for a Bayesian variational framework enabling high-resolution 3D activity reconstruction in 220 liter waste drums using Angular Segmented Gamma Scanning (ASGS) data and transmission-derived attenuation maps. Our proposed inference and uncertainty quantification approach is demonstrated using virtual experiments that simulate typical waste characterization scenarios. Computations are made tractable by using stochastic variational inference (SVI) together with a multi-resolution spatial prior to infer the spatial activity distribution. Results show that the approach can recover the spatial activity distribution within the considered drum, while also providing more accurate total activity estimates than conventional methods, thereby enhancing the accuracy of radiological waste characterization.

Keywords:

Segmented Gamma Scanning

Bayesian Inference

Variational Inference

Uncertainty

Legacy waste

Non-destructive testing

3D Activity reconstruction from Angular Gamma Scanning via variational Bayes: A proof of concept

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