Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localization

Tarpau, Cécilia; Fang, Ming; Zygalakis, Konstantinos C.; Pereyra, Marcelo; Di Fulvio, Angela; Altmann, Yoann

doi:10.1088/1361-6420/ad96de

Title: Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localization

Journal Article · 11 December 2024 · Inverse Problems

DOI: https://doi.org/10.1088/1361-6420/ad96de · OSTI ID:2480960

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Abstract This paper presents a statistical forward model for a Compton imaging system, called Compton imager. This system, under development at the University of Illinois Urbana Champaign, is a variant of Compton cameras with a single type of sensors which can simultaneously act as scatterers and absorbers. This imager is convenient for imaging situations requiring a wide field of view. The proposed statistical forward model is then used to solve the inverse problem of estimating the location and energy of point-like sources from observed data. This inverse problem is formulated and solved in a Bayesian framework by using a Metropolis within Gibbs algorithm for the estimation of the location, and an expectation-maximization algorithm for the estimation of the energy. This approach leads to more accurate estimation when compared with the deterministic standard back-projection approach, with the additional benefit of uncertainty quantification in the low photon imaging setting.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: NA0003996

OSTI ID:: 2480960

Journal Information:: Inverse Problems, Journal Name: Inverse Problems Journal Issue: 12 Vol. 40; ISSN 0266-5611

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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