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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.07676 |
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| _version_ | 1866916127757565952 |
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| author | Tarpau, Cecilia Fang, Ming Zygalakis, Konstantinos C. Pereyra, Marcelo Di Fulvio, Angela Altmann, Yoann |
| author_facet | Tarpau, Cecilia Fang, Ming Zygalakis, Konstantinos C. Pereyra, Marcelo Di Fulvio, Angela Altmann, Yoann |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_07676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localisation Tarpau, Cecilia Fang, Ming Zygalakis, Konstantinos C. Pereyra, Marcelo Di Fulvio, Angela Altmann, Yoann Applications Image and Video Processing Mathematical Physics 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. |
| title | Statistical modelling and Bayesian inversion for a Compton imaging system: application to radioactive source localisation |
| topic | Applications Image and Video Processing Mathematical Physics |
| url | https://arxiv.org/abs/2402.07676 |