An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction Article Swipe
Robert Twyman
,
Simon Arridge
,
Željko Kereta
,
Bangti Jin
,
Ludovica Brusaferri
,
Sangtae Ahn
,
C.W. Stearns
,
Brian F. Hutton
,
Irene A. Burger
,
Fotis A. Kotasidis
,
Kris Thielemans
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2022.3203237
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/tmi.2022.3203237
Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data.
Related Topics
Concepts
Algorithm
Variance reduction
Voxel
Convergence (economics)
Reduction (mathematics)
Iterative reconstruction
Monte Carlo method
Computer science
Sequence (biology)
Mathematics
Heuristic
Rate of convergence
Image quality
Variance (accounting)
Artificial intelligence
Image (mathematics)
Statistics
Accounting
Business
Economics
Biology
Channel (broadcasting)
Computer network
Genetics
Geometry
Economic growth
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tmi.2022.3203237
- https://ieeexplore.ieee.org/ielx7/42/10003062/09872020.pdf
- OA Status
- hybrid
- Cited By
- 12
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4293811975
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4293811975Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/tmi.2022.3203237Digital Object Identifier
- Title
-
An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image ReconstructionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-31Full publication date if available
- Authors
-
Robert Twyman, Simon Arridge, Željko Kereta, Bangti Jin, Ludovica Brusaferri, Sangtae Ahn, C.W. Stearns, Brian F. Hutton, Irene A. Burger, Fotis A. Kotasidis, Kris ThielemansList of authors in order
- Landing page
-
https://doi.org/10.1109/tmi.2022.3203237Publisher landing page
- PDF URL
-
https://ieeexplore.ieee.org/ielx7/42/10003062/09872020.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/42/10003062/09872020.pdfDirect OA link when available
- Concepts
-
Algorithm, Variance reduction, Voxel, Convergence (economics), Reduction (mathematics), Iterative reconstruction, Monte Carlo method, Computer science, Sequence (biology), Mathematics, Heuristic, Rate of convergence, Image quality, Variance (accounting), Artificial intelligence, Image (mathematics), Statistics, Accounting, Business, Economics, Biology, Channel (broadcasting), Computer network, Genetics, Geometry, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 3, 2023: 3Per-year citation counts (last 5 years)
- References (count)
-
58Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.which | 125 |
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| abstract_inverted_index.BSREM, | 225 |
| abstract_inverted_index.BSREM. | 104 |
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| abstract_inverted_index.images | 34, 176 |
| abstract_inverted_index.impact | 60, 153 |
| abstract_inverted_index.memory | 119 |
| abstract_inverted_index.number | 156 |
| abstract_inverted_index.rates. | 70 |
| abstract_inverted_index.subset | 123 |
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| abstract_inverted_index.within | 173 |
| abstract_inverted_index.between | 30, 195 |
| abstract_inverted_index.compare | 100 |
| abstract_inverted_index.exhibit | 20 |
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| abstract_inverted_index.regions | 169 |
| abstract_inverted_index.relaxed | 49 |
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| abstract_inverted_index.several | 133 |
| abstract_inverted_index.studies | 135 |
| abstract_inverted_index.updates | 197 |
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| abstract_inverted_index.achieved | 37 |
| abstract_inverted_index.adaption | 77 |
| abstract_inverted_index.computed | 122 |
| abstract_inverted_index.constant | 184 |
| abstract_inverted_index.converge | 215 |
| abstract_inverted_index.gradient | 87 |
| abstract_inverted_index.interest | 171 |
| abstract_inverted_index.recently | 121 |
| abstract_inverted_index.relative | 95 |
| abstract_inverted_index.sequence | 66 |
| abstract_inverted_index.solution | 223 |
| abstract_inverted_index.subclass | 40 |
| abstract_inverted_index.subsets, | 158 |
| abstract_inverted_index.subsets. | 15, 31 |
| abstract_inverted_index.updates. | 130 |
| abstract_inverted_index.utilised | 127 |
| abstract_inverted_index.variance | 85 |
| abstract_inverted_index.Desirable | 32 |
| abstract_inverted_index.Penalised | 0 |
| abstract_inverted_index.algorithm | 68 |
| abstract_inverted_index.behaviour | 23 |
| abstract_inverted_index.converged | 33 |
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| abstract_inverted_index.heuristic | 55 |
| abstract_inverted_index.numerical | 134 |
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| abstract_inverted_index.retention | 117 |
| abstract_inverted_index.selection | 56, 206 |
| abstract_inverted_index.sequence, | 52 |
| abstract_inverted_index.simulated | 140 |
| abstract_inverted_index.algorithms | 4, 43, 88, 108, 114 |
| abstract_inverted_index.difference | 96 |
| abstract_inverted_index.gradients, | 124 |
| abstract_inverted_index.iterations | 10, 26 |
| abstract_inverted_index.likelihood | 222 |
| abstract_inverted_index.parameters | 58 |
| abstract_inverted_index.stochastic | 84 |
| abstract_inverted_index.subsequent | 129, 196 |
| abstract_inverted_index.variations | 29, 191 |
| abstract_inverted_index.accelerated | 7 |
| abstract_inverted_index.application | 79 |
| abstract_inverted_index.convergence | 69, 101, 167 |
| abstract_inverted_index.demonstrate | 75, 189 |
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| abstract_inverted_index.performance | 102 |
| abstract_inverted_index.Furthermore, | 210 |
| abstract_inverted_index.investigated | 107 |
| abstract_inverted_index.particularly | 226 |
| abstract_inverted_index.acquisitions. | 151 |
| abstract_inverted_index.reconstructed | 175 |
| abstract_inverted_index.significantly | 216 |
| abstract_inverted_index.implementation | 46 |
| abstract_inverted_index.reconstruction | 3, 92 |
| abstract_inverted_index.hyper-parameter | 205 |
| abstract_inverted_index.preconditioners | 160 |
| abstract_inverted_index.preconditioning, | 185 |
| abstract_inverted_index.reconstructions. | 209 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 11 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.84652027 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |