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Communication Dans Un Congrès Année : 2023

An Iterative Bayesian Algorithm for 3D Image Reconstruction Using Multi-View Compton Data

Résumé

Conventional maximum likelihood-based algorithms for 3D Compton image reconstruction are often stuck with slow convergence and large data volume, which could be unsuitable for some practical applications, such as nuclear engineering. Taking advantage of the Bayesian framework, we propose a fast-converging iterative maximum a posteriori reconstruction algorithm under the assumption of the Poisson data model and Markov random field-based convex prior in this paper. The main originality resides in developing a new iterative maximization scheme with simultaneous updates following the line search strategy to bypass the spatial dependencies among neighboring voxels. Numerical experiments on real datasets conducted with hand-held Temporal Compton cameras developed by Damavan Imaging company and punctual 0.2 MBq 22Na sources with zero-mean Gaussian Markov random field confirm the outperformance of the proposed maximum a posteriori algorithm over various existing expectation–maximization type solutions.

Dates et versions

hal-04444426 , version 1 (07-02-2024)

Identifiants

Citer

Nhan Le, Hichem Snoussi, Alain Iltis. An Iterative Bayesian Algorithm for 3D Image Reconstruction Using Multi-View Compton Data. International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, Jul 2023, Garching, Germany. pp.2, ⟨10.3390/psf2023009002⟩. ⟨hal-04444426⟩
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