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Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom
BACKGROUND: The Bayesian penalized likelihood (BPL) algorithm Q.Clear (GE Healthcare) allows fully convergent iterative reconstruction that results in better image quality and quantitative accuracy, while limiting image noise. The present study aimed to optimize BPL reconstruction parameters for (18...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486353/ https://www.ncbi.nlm.nih.gov/pubmed/32915344 http://dx.doi.org/10.1186/s40658-020-00325-8 |
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author | Yoshii, Tokiya Miwa, Kenta Yamaguchi, Masashi Shimada, Kai Wagatsuma, Kei Yamao, Tensho Kamitaka, Yuto Hiratsuka, Seiya Kobayashi, Rinya Ichikawa, Hajime Miyaji, Noriaki Miyazaki, Tsuyoshi Ishii, Kenji |
author_facet | Yoshii, Tokiya Miwa, Kenta Yamaguchi, Masashi Shimada, Kai Wagatsuma, Kei Yamao, Tensho Kamitaka, Yuto Hiratsuka, Seiya Kobayashi, Rinya Ichikawa, Hajime Miyaji, Noriaki Miyazaki, Tsuyoshi Ishii, Kenji |
author_sort | Yoshii, Tokiya |
collection | PubMed |
description | BACKGROUND: The Bayesian penalized likelihood (BPL) algorithm Q.Clear (GE Healthcare) allows fully convergent iterative reconstruction that results in better image quality and quantitative accuracy, while limiting image noise. The present study aimed to optimize BPL reconstruction parameters for (18)F-NaF PET/CT images and to determine the feasibility of (18)F-NaF PET/CT image acquisition over shorter durations in clinical practice. METHODS: A custom-designed thoracic spine phantom consisting of several inserts, soft tissue, normal spine, and metastatic bone tumor, was scanned using a Discovery MI PET/CT scanner (GE Healthcare). The phantom allows optional adjustment of activity distribution, tumor size, and attenuation. We reconstructed PET images using OSEM + PSF + TOF (2 iterations, 17 subsets, and a 4-mm Gaussian filter), BPL + TOF (β = 200 to 700), and scan durations of 30–120 s. Signal-to-noise ratios (SNR), contrast, and coefficients of variance (CV) as image quality indicators were calculated, whereas the quantitative measures were recovery coefficients (RC) and RC linearity over a range of activity. We retrospectively analyzed images from five persons without bone metastases (male, n = 1; female, n = 4), then standardized uptake values (SUV), CV, and SNR at the 4th, 5th, and 6th thoracic vertebra were calculated in BPL + TOF (β = 400) images. RESULTS: The optimal reconstruction parameter of the BPL was β = 400 when images were acquired at 120 s/bed. At 90 s/bed, the BPL with a β value of 400 yielded 24% and 18% higher SNR and contrast, respectively, than OSEM (2 iterations; 120 s acquisitions). The BPL was superior to OSEM in terms of RC and the RC linearity over a range of activity, regardless of scan duration. The SUV(max) were lower in BPL, than in OSEM. The CV and vertebral SNR in BPL were superior to those in OSEM. CONCLUSIONS: The optimal reconstruction parameters of (18)F-NaF PET/CT images acquired over different durations were determined. The BPL can reduce PET acquisition to 90 s/bed in (18)F-NaF PET/CT imaging. Our results suggest that BPL (β = 400) on SiPM-based TOF PET/CT scanner maintained high image quality and quantitative accuracy even for shorter acquisition durations. |
format | Online Article Text |
id | pubmed-7486353 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-74863532020-09-21 Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom Yoshii, Tokiya Miwa, Kenta Yamaguchi, Masashi Shimada, Kai Wagatsuma, Kei Yamao, Tensho Kamitaka, Yuto Hiratsuka, Seiya Kobayashi, Rinya Ichikawa, Hajime Miyaji, Noriaki Miyazaki, Tsuyoshi Ishii, Kenji EJNMMI Phys Original Research BACKGROUND: The Bayesian penalized likelihood (BPL) algorithm Q.Clear (GE Healthcare) allows fully convergent iterative reconstruction that results in better image quality and quantitative accuracy, while limiting image noise. The present study aimed to optimize BPL reconstruction parameters for (18)F-NaF PET/CT images and to determine the feasibility of (18)F-NaF PET/CT image acquisition over shorter durations in clinical practice. METHODS: A custom-designed thoracic spine phantom consisting of several inserts, soft tissue, normal spine, and metastatic bone tumor, was scanned using a Discovery MI PET/CT scanner (GE Healthcare). The phantom allows optional adjustment of activity distribution, tumor size, and attenuation. We reconstructed PET images using OSEM + PSF + TOF (2 iterations, 17 subsets, and a 4-mm Gaussian filter), BPL + TOF (β = 200 to 700), and scan durations of 30–120 s. Signal-to-noise ratios (SNR), contrast, and coefficients of variance (CV) as image quality indicators were calculated, whereas the quantitative measures were recovery coefficients (RC) and RC linearity over a range of activity. We retrospectively analyzed images from five persons without bone metastases (male, n = 1; female, n = 4), then standardized uptake values (SUV), CV, and SNR at the 4th, 5th, and 6th thoracic vertebra were calculated in BPL + TOF (β = 400) images. RESULTS: The optimal reconstruction parameter of the BPL was β = 400 when images were acquired at 120 s/bed. At 90 s/bed, the BPL with a β value of 400 yielded 24% and 18% higher SNR and contrast, respectively, than OSEM (2 iterations; 120 s acquisitions). The BPL was superior to OSEM in terms of RC and the RC linearity over a range of activity, regardless of scan duration. The SUV(max) were lower in BPL, than in OSEM. The CV and vertebral SNR in BPL were superior to those in OSEM. CONCLUSIONS: The optimal reconstruction parameters of (18)F-NaF PET/CT images acquired over different durations were determined. The BPL can reduce PET acquisition to 90 s/bed in (18)F-NaF PET/CT imaging. Our results suggest that BPL (β = 400) on SiPM-based TOF PET/CT scanner maintained high image quality and quantitative accuracy even for shorter acquisition durations. Springer International Publishing 2020-09-11 /pmc/articles/PMC7486353/ /pubmed/32915344 http://dx.doi.org/10.1186/s40658-020-00325-8 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Original Research Yoshii, Tokiya Miwa, Kenta Yamaguchi, Masashi Shimada, Kai Wagatsuma, Kei Yamao, Tensho Kamitaka, Yuto Hiratsuka, Seiya Kobayashi, Rinya Ichikawa, Hajime Miyaji, Noriaki Miyazaki, Tsuyoshi Ishii, Kenji Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom |
title | Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom |
title_full | Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom |
title_fullStr | Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom |
title_full_unstemmed | Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom |
title_short | Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for (18)F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom |
title_sort | optimization of a bayesian penalized likelihood algorithm (q.clear) for (18)f-naf bone pet/ct images acquired over shorter durations using a custom-designed phantom |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7486353/ https://www.ncbi.nlm.nih.gov/pubmed/32915344 http://dx.doi.org/10.1186/s40658-020-00325-8 |
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