Simultaneous Coordinate Maximization Algorithm for Maximum A Posteriori Compton Camera Imaging With Markov Random Field Prior
It is widely acknowledged that maximum a posteriori (MAP) estimation, when combined with a Markov random field (MRF) prior, is an effective tool for Compton camera imaging from Poisson data. While MAP estimation involves solving an optimization problem, the primary challenge arises from the correlat...
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| Vydáno v: | IEEE transactions on instrumentation and measurement Ročník 74; s. 1 - 17 |
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2025
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| Abstract | It is widely acknowledged that maximum a posteriori (MAP) estimation, when combined with a Markov random field (MRF) prior, is an effective tool for Compton camera imaging from Poisson data. While MAP estimation involves solving an optimization problem, the primary challenge arises from the correlation inherent in the MRF prior. Unlike most existing expectation maximization (EM)-like algorithms that address this challenge indirectly, we propose a simultaneous coordinate maximization (SCM) algorithm to directly handle convex MRF priors. Basically, the proposed algorithm breaks the correlation within MRF in the same way as sequential coordinate ascent (CA) algorithms; however, it allows updating all coordinates simultaneously at each iteration, rather than one coordinate or one block of coordinates sequentially. It is thus applicable to large-scale optimization problems, and hence especially suitable for high-dimensional Compton image reconstruction in real time. We prove the convergence of the SCM algorithm and analyze its convergence rate and complexity using both analytical and numerical methods. In light of the SCM algorithm, we develop a closed-form algorithm called MAP-SCM-EM for Compton camera imaging under the assumption of the EM surrogate of the Poisson log-likelihood function and the zero-mean Gaussian MRF prior. Numerous comparative studies with more classical reconstruction algorithms using real-world data, conducted with hand-held CeBr 3 Temporal Compton cameras developed by Damavan company, have confirmed that our algorithm offers a good compromise between speed and accuracy of reconstruction. |
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| AbstractList | It is widely acknowledged that maximum a posteriori (MAP) estimation, when combined with a Markov random field (MRF) prior, is an effective tool for Compton camera imaging from Poisson data. While MAP estimation involves solving an optimization problem, the primary challenge arises from the correlation inherent in the MRF prior. Unlike most existing expectation maximization (EM)-like algorithms that address this challenge indirectly, we propose a simultaneous coordinate maximization (SCM) algorithm to directly handle convex MRF priors. Basically, the proposed algorithm breaks the correlation within MRF in the same way as sequential coordinate ascent (CA) algorithms; however, it allows updating all coordinates simultaneously at each iteration, rather than one coordinate or one block of coordinates sequentially. It is thus applicable to large-scale optimization problems, and hence especially suitable for high-dimensional Compton image reconstruction in real time. We prove the convergence of the SCM algorithm and analyze its convergence rate and complexity using both analytical and numerical methods. In light of the SCM algorithm, we develop a closed-form algorithm called MAP-SCM-EM for Compton camera imaging under the assumption of the EM surrogate of the Poisson log-likelihood function and the zero-mean Gaussian MRF prior. Numerous comparative studies with more classical reconstruction algorithms using real-world data, conducted with hand-held CeBr3 Temporal Compton cameras developed by Damavan company, have confirmed that our algorithm offers a good compromise between speed and accuracy of reconstruction. It is widely acknowledged that maximum a posteriori (MAP) estimation, when combined with a Markov random field (MRF) prior, is an effective tool for Compton camera imaging from Poisson data. While MAP estimation involves solving an optimization problem, the primary challenge arises from the correlation inherent in the MRF prior. Unlike most existing expectation maximization (EM)-like algorithms that address this challenge indirectly, we propose a simultaneous coordinate maximization (SCM) algorithm to directly handle convex MRF priors. Basically, the proposed algorithm breaks the correlation within MRF in the same way as sequential coordinate ascent (CA) algorithms; however, it allows updating all coordinates simultaneously at each iteration, rather than one coordinate or one block of coordinates sequentially. It is thus applicable to large-scale optimization problems, and hence especially suitable for high-dimensional Compton image reconstruction in real time. We prove the convergence of the SCM algorithm and analyze its convergence rate and complexity using both analytical and numerical methods. In light of the SCM algorithm, we develop a closed-form algorithm called MAP-SCM-EM for Compton camera imaging under the assumption of the EM surrogate of the Poisson log-likelihood function and the zero-mean Gaussian MRF prior. Numerous comparative studies with more classical reconstruction algorithms using real-world data, conducted with hand-held CeBr 3 Temporal Compton cameras developed by Damavan company, have confirmed that our algorithm offers a good compromise between speed and accuracy of reconstruction. |
| Author | Snoussi, Hichem Iltis, Alain Le, Nhan |
| Author_xml | – sequence: 1 givenname: Nhan orcidid: 0000-0003-3873-2795 surname: Le fullname: Le, Nhan email: thi-ai-nhan.le@utt.fr organization: Computer Science and Digital Society Laboratory, Troyes University of Technology, Troyes, France – sequence: 2 givenname: Hichem orcidid: 0000-0002-6563-2135 surname: Snoussi fullname: Snoussi, Hichem email: hichem.snoussi@utt.fr organization: Computer Science and Digital Society Laboratory, Troyes University of Technology, Troyes, France – sequence: 3 givenname: Alain orcidid: 0000-0002-3443-5390 surname: Iltis fullname: Iltis, Alain email: alain.iltis@damavan-imaging.com organization: Damavan Imaging, Troyes, France |
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| Snippet | It is widely acknowledged that maximum a posteriori (MAP) estimation, when combined with a Markov random field (MRF) prior, is an effective tool for Compton... |
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| SubjectTerms | Accuracy Algorithms Cameras Comparative studies Complexity theory Compton camera imaging Convergence Correlation Estimation Fields (mathematics) Image reconstruction Imaging Markov random field (MRF) Markov random fields Maximization maximum a posteriori (MAP) estimation Numerical methods Optimization real-world data reconstruction algorithm Reconstruction algorithms simultaneous coordinate maximization (SCM) |
| Title | Simultaneous Coordinate Maximization Algorithm for Maximum A Posteriori Compton Camera Imaging With Markov Random Field Prior |
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