Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localisation
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.
Cite
@article{arxiv.2402.07676,
title = {Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localisation},
author = {Cecilia Tarpau and Ming Fang and Konstantinos C. Zygalakis and Marcelo Pereyra and Angela Di Fulvio and Yoann Altmann},
journal= {arXiv preprint arXiv:2402.07676},
year = {2024}
}