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arXiv · 2402.07676

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

Cecilia Tarpau·Ming Fang·Konstantinos C. Zygalakis·Marcelo Pereyra·Angela Di Fulvio·Yoann Altmann
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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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Cecilia Tarpau, Ming Fang, Konstantinos C. Zygalakis, Marcelo Pereyra, Angela Di Fulvio, Yoann Altmann. 2024-02-16. Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localisation. https://arxiv.org/abs/2402.07676

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