Does the beta regularization parameter of bayesian penalized likelihood reconstruction always affect the quantification accuracy and image quality of positron emission tomography computed tomography? Article Swipe
Zhifang Wu
,
Binwei Guo
,
Bin Huang
,
Bin Zhao
,
Zhixing Qin
,
Xinzhong Hao
,
Meng Liang
,
Jun Xie
,
Sijin Li
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1002/acm2.13129
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1002/acm2.13129
Purpose This study aims to provide a detailed investigation on the noise penalization factor in Bayesian penalized likelihood (BPL)‐based algorithm, with the utilization of partial volume effect correction (PVC), so as to offer the suitable beta value and optimum standardized uptake value (SUV) parameters in clinical practice for small pulmonary nodules. Methods A National Electrical Manufacturers Association (NEMA) image‐quality phantom was scanned and images were reconstructed using BPL with beta values ranged from 100 to 1000. The recovery coefficient (RC), contrast recovery (CR), and background variability (BV) were measured to assess the quantification accuracy and image quality. In the clinical assessment, lesions were categorized into sub‐centimeter (<10 mm, n = 7) group and medium size (10–30 mm, n = 16) group. Signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR) were measured to evaluate the image quality and lesion detectability. With PVC was performed, the impact of beta values on SUVs (SUVmax, SUVmean, SUVpeak) of small pulmonary nodules was evaluated. Subjective image analysis was performed by two experienced readers. Results With the increasing of beta values, RC, CR, and BV decreased gradually in the phantom work. In the clinical study, SNR and CNR of both groups increased with the beta values ( P < 0.001), although the sub‐centimeter group showed increases after the beta value reached over 700. In addition, highly significant negative correlations were observed between SUVs and beta values for both lesion‐size groups before the PVC ( P < 0.001 for all). After the PVC, SUVpeak measured from the sub‐centimeter group was no significantly different among different beta values ( P = 0.830). Conclusion Our study suggests using SUVpeak as the quantification parameter with PVC performed to mitigate the effects of beta regularization. Beta values between 300 and 400 were preferred for pulmonary nodules smaller than 30 mm.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/acm2.13129
- OA Status
- gold
- Cited By
- 4
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3133987232
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3133987232Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/acm2.13129Digital Object Identifier
- Title
-
Does the beta regularization parameter of bayesian penalized likelihood reconstruction always affect the quantification accuracy and image quality of positron emission tomography computed tomography?Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-01Full publication date if available
- Authors
-
Zhifang Wu, Binwei Guo, Bin Huang, Bin Zhao, Zhixing Qin, Xinzhong Hao, Meng Liang, Jun Xie, Sijin LiList of authors in order
- Landing page
-
https://doi.org/10.1002/acm2.13129Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1002/acm2.13129Direct OA link when available
- Concepts
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Imaging phantom, Image quality, Positron emission tomography, Nuclear medicine, Mathematics, Standardized uptake value, Bayesian probability, Partial volume, Medicine, Statistics, Image (mathematics), Artificial intelligence, Computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2023: 3, 2022: 1Per-year citation counts (last 5 years)
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40Number 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.nodules. | 51 |
| abstract_inverted_index.observed | 224 |
| abstract_inverted_index.practice | 47 |
| abstract_inverted_index.quality. | 97 |
| abstract_inverted_index.readers. | 167 |
| abstract_inverted_index.recovery | 78, 82 |
| abstract_inverted_index.suggests | 267 |
| abstract_inverted_index.suitable | 35 |
| abstract_inverted_index.addition, | 218 |
| abstract_inverted_index.decreased | 179 |
| abstract_inverted_index.different | 255, 257 |
| abstract_inverted_index.gradually | 180 |
| abstract_inverted_index.increased | 195 |
| abstract_inverted_index.increases | 209 |
| abstract_inverted_index.parameter | 273 |
| abstract_inverted_index.penalized | 17 |
| abstract_inverted_index.performed | 163, 276 |
| abstract_inverted_index.preferred | 291 |
| abstract_inverted_index.pulmonary | 50, 155, 293 |
| abstract_inverted_index.Conclusion | 264 |
| abstract_inverted_index.Electrical | 55 |
| abstract_inverted_index.Subjective | 159 |
| abstract_inverted_index.algorithm, | 20 |
| abstract_inverted_index.background | 85 |
| abstract_inverted_index.correction | 28 |
| abstract_inverted_index.evaluated. | 158 |
| abstract_inverted_index.increasing | 171 |
| abstract_inverted_index.likelihood | 18 |
| abstract_inverted_index.parameters | 44 |
| abstract_inverted_index.performed, | 142 |
| abstract_inverted_index.Association | 57 |
| abstract_inverted_index.assessment, | 101 |
| abstract_inverted_index.categorized | 104 |
| abstract_inverted_index.coefficient | 79 |
| abstract_inverted_index.experienced | 166 |
| abstract_inverted_index.significant | 220 |
| abstract_inverted_index.utilization | 23 |
| abstract_inverted_index.variability | 86 |
| abstract_inverted_index.correlations | 222 |
| abstract_inverted_index.penalization | 13 |
| abstract_inverted_index.standardized | 40 |
| abstract_inverted_index.(BPL)‐based | 19 |
| abstract_inverted_index.Manufacturers | 56 |
| abstract_inverted_index.investigation | 9 |
| abstract_inverted_index.lesion‐size | 232 |
| abstract_inverted_index.reconstructed | 66 |
| abstract_inverted_index.significantly | 254 |
| abstract_inverted_index.detectability. | 138 |
| abstract_inverted_index.quantification | 93, 272 |
| abstract_inverted_index.image‐quality | 59 |
| abstract_inverted_index.regularization. | 283 |
| abstract_inverted_index.sub‐centimeter | 106, 206, 250 |
| abstract_inverted_index.Signal‐to‐noise | 122 |
| abstract_inverted_index.contrast‐to‐noise | 126 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5100708327, https://openalex.org/A5016278248 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I17721919, https://openalex.org/I4210125748 |
| citation_normalized_percentile.value | 0.65577925 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |