A Fast Convergent Ordered-Subsets Algorithm With Subiteration-Dependent Preconditioners for PET Image Reconstruction Article Swipe
Jianfeng Guo
,
C. Ross Schmidtlein
,
Andrzej Król
,
Si Li
,
Yizun Lin
,
Sangtae Ahn
,
C.W. Stearns
,
Yuesheng Xu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2022.3181813
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2022.3181813
We investigated the imaging performance of a fast convergent ordered-subsets algorithm with subiteration-dependent preconditioners (SDPs) for positron emission tomography (PET) image reconstruction. In particular, we considered the use of SDP with the block sequential regularized expectation maximization (BSREM) approach with the relative difference prior (RDP) regularizer due to its prior clinical adaptation by vendors. Because the RDP regularization promotes smoothness in the reconstructed image, the directions of the gradients in smooth areas more accurately point toward the objective function's minimizer than those in variable areas. Motivated by this observation, two SDPs have been designed to increase iteration step-sizes in the smooth areas and reduce iteration step-sizes in the variable areas relative to a conventional expectation maximization preconditioner. The momentum technique used for convergence acceleration can be viewed as a special case of SDP. We have proved the global convergence of SDP-BSREM algorithms by assuming certain characteristics of the preconditioner. By means of numerical experiments using both simulated and clinical PET data, we have shown that the SDP-BSREM algorithms substantially improve the convergence rate, as compared to conventional BSREM and a vendor's implementation as Q.Clear. Specifically, SDP-BSREM algorithms converge 35%-50% faster in reaching the same objective function value than conventional BSREM and commercial Q.Clear algorithms. Moreover, we showed in phantoms with hot, cold and background regions that the SDP-BSREM algorithms approached the values of a highly converged reference image faster than conventional BSREM and commercial Q.Clear algorithms.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tmi.2022.3181813
- OA Status
- green
- Cited By
- 6
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3214032575
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- OpenAlex ID
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https://openalex.org/W3214032575Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tmi.2022.3181813Digital Object Identifier
- Title
-
A Fast Convergent Ordered-Subsets Algorithm With Subiteration-Dependent Preconditioners for PET Image ReconstructionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-06-09Full publication date if available
- Authors
-
Jianfeng Guo, C. Ross Schmidtlein, Andrzej Król, Si Li, Yizun Lin, Sangtae Ahn, C.W. Stearns, Yuesheng XuList of authors in order
- Landing page
-
https://doi.org/10.1109/tmi.2022.3181813Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/9810102Direct OA link when available
- Concepts
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Preconditioner, Algorithm, Regularization (linguistics), Mathematics, Iterative reconstruction, Mathematical optimization, Computer science, Expectation–maximization algorithm, Iterative method, Artificial intelligence, Statistics, Maximum likelihoodTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 5, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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