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Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions
OBJECTIVE: We sought to explore the feasibility of shorter acquisition times using two short dynamic scans for a multiparametric PET study and the influence of quantitative performance in shortened dynamic PET. METHODS: Twenty-one patients underwent whole-body dynamic (18)F-FDG PET/CT examinations o...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9097952/ https://www.ncbi.nlm.nih.gov/pubmed/35574350 http://dx.doi.org/10.3389/fonc.2022.822708 |
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author | Wang, Hui Miao, Ying Yu, Wenjing Zhu, Gan Wu, Tao Zhao, Xuefeng Yuan, Guangjie Li, Biao Xu, Huiqin |
author_facet | Wang, Hui Miao, Ying Yu, Wenjing Zhu, Gan Wu, Tao Zhao, Xuefeng Yuan, Guangjie Li, Biao Xu, Huiqin |
author_sort | Wang, Hui |
collection | PubMed |
description | OBJECTIVE: We sought to explore the feasibility of shorter acquisition times using two short dynamic scans for a multiparametric PET study and the influence of quantitative performance in shortened dynamic PET. METHODS: Twenty-one patients underwent whole-body dynamic (18)F-FDG PET/CT examinations on a PET/CT (Siemens Biograph Vision) with a total scan time of 75 min using continuous bed motion for Patlak multiparametric imaging. Two sets of Patlak multiparametric images were produced: the standard MR(FDG) and DV(FDG) images (MR(FDG)-(std) and DV(FDG)-(std)) and two short dynamic MR(FDG) and DV(FDG) images (MR(FDG)-(tsd) and DV(FDG)-(tsd)), which were generated by a 0–75 min post injection (p.i.) dynamic PET series and a 0–6 min + 60–75 min p.i. dynamic PET series, respectively. The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were obtained and compared using Passing–Bablok regression and Bland–Altman analysis. RESULTS: High correlations were obtained between MR(FDG)-(tsd) and MR(FDG)-(std), and between DV(FDG)-(tsd) and DV(FDG)-(std) for both normal organs and all lesions (0.962 ≦ Spearman’s rho ≦ 0.982, p < 0.0001). The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were also in agreement. For normal organs, the Bland–Altman plot showed that the mean bias of MR(FDG-)max, MR(FDG-)mean, and MR(FDG-)peak was -0.002 (95% CI: -0.032–0.027), -0.002 (95% CI: -0.026–0.023), and -0.002 (95% CI: -0.026–0.022), respectively. The mean bias of DV(FDG-)max, DV(FDG-)mean, and DV(FDG-)peak was -3.3 (95% CI: -24.8–18.2), -1.4 (95% CI: -12.1–9.2), and -2.3 (95% CI: -15–10.4), respectively. For lesions, the Bland–Altman plot showed that the mean bias of MR(FDG-)max, MR(FDG-)mean, and MR(FDG-)peak was -0.009 (95% CI: -0.056–0.038), -0.004 (95% CI: -0.039–0.031), and -0.004 (95% CI: -0.036–0.028), respectively. The mean bias of DV(FDG-)max, DV(FDG-)mean, and DV(FDG-)peak was -8.4 (95% CI: -42.6–25.9), -4.8 (95% CI: -20.2–10.6), and -4.0 (95% CI: -23.7–15.6), respectively. CONCLUSIONS: This study demonstrates the feasibility of using two short dynamic scans that include the first 0–6 min and 60–75 min scans p.i. for Patlak multiparametric images, which can increase patient throughout for parametric analysis. |
format | Online Article Text |
id | pubmed-9097952 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90979522022-05-13 Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions Wang, Hui Miao, Ying Yu, Wenjing Zhu, Gan Wu, Tao Zhao, Xuefeng Yuan, Guangjie Li, Biao Xu, Huiqin Front Oncol Oncology OBJECTIVE: We sought to explore the feasibility of shorter acquisition times using two short dynamic scans for a multiparametric PET study and the influence of quantitative performance in shortened dynamic PET. METHODS: Twenty-one patients underwent whole-body dynamic (18)F-FDG PET/CT examinations on a PET/CT (Siemens Biograph Vision) with a total scan time of 75 min using continuous bed motion for Patlak multiparametric imaging. Two sets of Patlak multiparametric images were produced: the standard MR(FDG) and DV(FDG) images (MR(FDG)-(std) and DV(FDG)-(std)) and two short dynamic MR(FDG) and DV(FDG) images (MR(FDG)-(tsd) and DV(FDG)-(tsd)), which were generated by a 0–75 min post injection (p.i.) dynamic PET series and a 0–6 min + 60–75 min p.i. dynamic PET series, respectively. The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were obtained and compared using Passing–Bablok regression and Bland–Altman analysis. RESULTS: High correlations were obtained between MR(FDG)-(tsd) and MR(FDG)-(std), and between DV(FDG)-(tsd) and DV(FDG)-(std) for both normal organs and all lesions (0.962 ≦ Spearman’s rho ≦ 0.982, p < 0.0001). The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were also in agreement. For normal organs, the Bland–Altman plot showed that the mean bias of MR(FDG-)max, MR(FDG-)mean, and MR(FDG-)peak was -0.002 (95% CI: -0.032–0.027), -0.002 (95% CI: -0.026–0.023), and -0.002 (95% CI: -0.026–0.022), respectively. The mean bias of DV(FDG-)max, DV(FDG-)mean, and DV(FDG-)peak was -3.3 (95% CI: -24.8–18.2), -1.4 (95% CI: -12.1–9.2), and -2.3 (95% CI: -15–10.4), respectively. For lesions, the Bland–Altman plot showed that the mean bias of MR(FDG-)max, MR(FDG-)mean, and MR(FDG-)peak was -0.009 (95% CI: -0.056–0.038), -0.004 (95% CI: -0.039–0.031), and -0.004 (95% CI: -0.036–0.028), respectively. The mean bias of DV(FDG-)max, DV(FDG-)mean, and DV(FDG-)peak was -8.4 (95% CI: -42.6–25.9), -4.8 (95% CI: -20.2–10.6), and -4.0 (95% CI: -23.7–15.6), respectively. CONCLUSIONS: This study demonstrates the feasibility of using two short dynamic scans that include the first 0–6 min and 60–75 min scans p.i. for Patlak multiparametric images, which can increase patient throughout for parametric analysis. Frontiers Media S.A. 2022-04-28 /pmc/articles/PMC9097952/ /pubmed/35574350 http://dx.doi.org/10.3389/fonc.2022.822708 Text en Copyright © 2022 Wang, Miao, Yu, Zhu, Wu, Zhao, Yuan, Li and Xu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Wang, Hui Miao, Ying Yu, Wenjing Zhu, Gan Wu, Tao Zhao, Xuefeng Yuan, Guangjie Li, Biao Xu, Huiqin Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions |
title | Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions |
title_full | Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions |
title_fullStr | Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions |
title_full_unstemmed | Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions |
title_short | Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions |
title_sort | improved clinical workflow for whole-body patlak parametric imaging using two short dynamic acquisitions |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9097952/ https://www.ncbi.nlm.nih.gov/pubmed/35574350 http://dx.doi.org/10.3389/fonc.2022.822708 |
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