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Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study

BACKGROUND: To explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki i...

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Autores principales: Tanaka, Takato, Nakajo, Masatoyo, Kawakami, Hirofumi, Motomura, Eriko, Fujisaka, Tomofumi, Ojima, Satoko, Saigo, Yasumasa, Yoshiura, Takashi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9458796/
https://www.ncbi.nlm.nih.gov/pubmed/36075998
http://dx.doi.org/10.1186/s13550-022-00933-8
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author Tanaka, Takato
Nakajo, Masatoyo
Kawakami, Hirofumi
Motomura, Eriko
Fujisaka, Tomofumi
Ojima, Satoko
Saigo, Yasumasa
Yoshiura, Takashi
author_facet Tanaka, Takato
Nakajo, Masatoyo
Kawakami, Hirofumi
Motomura, Eriko
Fujisaka, Tomofumi
Ojima, Satoko
Saigo, Yasumasa
Yoshiura, Takashi
author_sort Tanaka, Takato
collection PubMed
description BACKGROUND: To explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki images were generated from 73 dynamic (18)F-FDG-PET/CT scans of 30 patients with cardiac sarcoidosis. For each dynamic scan, the Ki images were obtained using the IF from each individual patient and a long time window (10–60 min). In addition, Ki images were obtained using the normalized averaged population-based IF and BPL algorithms with different beta values (350, 700, and 1000) with a short time window (40–60 min). The visual quality of each image was visually rated using a 4-point scale (0, not visible; 1, poor; 2, moderate; and 3, good), and the Ki parameters (Ki-max, Ki-mean, Ki-volume) of positive myocardial lesions were measured independently by two readers. Wilcoxon’s rank sum test, McNemar’s test, or linear regression analysis were performed to assess the differences or relationships between two quantitative variables. RESULTS: Both readers similarly rated 51 scans as positive (scores = 1–3) and 22 scans as negative (score = 0) for all four Ki images. Among the three types of population-based IF Ki images, the proportion of images with scores of 3 was highest with a beta of 1000 (78.4 and 72.5%, respectively) and lowest with a beta of 350 (33.3 and 23.5%) for both readers (all p < 0.001). The coefficients of determination between the Ki parameters obtained with the individual patient-based IF and those obtained with the population-based IF were highest with a beta of 1000 for both readers (Ki-max, 0.91 and 0.92, respectively; Ki-mean, 0.91 and 0.92, respectively; Ki-volume, 0.75 and 0.60, respectively; and all p < 0.001). CONCLUSIONS: Short-time-window Ki images with a population-based IF reconstructed using the BPL algorithm and a high beta value were closely correlated with long-time-window Ki images generated with an individual patient-based IF. Short-time-window Ki images using a population-based IF and BPL reconstruction might represent practical alternatives to long-time-window Ki images generated using an individual patient-based IF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-022-00933-8.
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spelling pubmed-94587962022-09-10 Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study Tanaka, Takato Nakajo, Masatoyo Kawakami, Hirofumi Motomura, Eriko Fujisaka, Tomofumi Ojima, Satoko Saigo, Yasumasa Yoshiura, Takashi EJNMMI Res Original Research BACKGROUND: To explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki images were generated from 73 dynamic (18)F-FDG-PET/CT scans of 30 patients with cardiac sarcoidosis. For each dynamic scan, the Ki images were obtained using the IF from each individual patient and a long time window (10–60 min). In addition, Ki images were obtained using the normalized averaged population-based IF and BPL algorithms with different beta values (350, 700, and 1000) with a short time window (40–60 min). The visual quality of each image was visually rated using a 4-point scale (0, not visible; 1, poor; 2, moderate; and 3, good), and the Ki parameters (Ki-max, Ki-mean, Ki-volume) of positive myocardial lesions were measured independently by two readers. Wilcoxon’s rank sum test, McNemar’s test, or linear regression analysis were performed to assess the differences or relationships between two quantitative variables. RESULTS: Both readers similarly rated 51 scans as positive (scores = 1–3) and 22 scans as negative (score = 0) for all four Ki images. Among the three types of population-based IF Ki images, the proportion of images with scores of 3 was highest with a beta of 1000 (78.4 and 72.5%, respectively) and lowest with a beta of 350 (33.3 and 23.5%) for both readers (all p < 0.001). The coefficients of determination between the Ki parameters obtained with the individual patient-based IF and those obtained with the population-based IF were highest with a beta of 1000 for both readers (Ki-max, 0.91 and 0.92, respectively; Ki-mean, 0.91 and 0.92, respectively; Ki-volume, 0.75 and 0.60, respectively; and all p < 0.001). CONCLUSIONS: Short-time-window Ki images with a population-based IF reconstructed using the BPL algorithm and a high beta value were closely correlated with long-time-window Ki images generated with an individual patient-based IF. Short-time-window Ki images using a population-based IF and BPL reconstruction might represent practical alternatives to long-time-window Ki images generated using an individual patient-based IF. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13550-022-00933-8. Springer Berlin Heidelberg 2022-09-08 /pmc/articles/PMC9458796/ /pubmed/36075998 http://dx.doi.org/10.1186/s13550-022-00933-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Research
Tanaka, Takato
Nakajo, Masatoyo
Kawakami, Hirofumi
Motomura, Eriko
Fujisaka, Tomofumi
Ojima, Satoko
Saigo, Yasumasa
Yoshiura, Takashi
Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study
title Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study
title_full Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study
title_fullStr Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study
title_full_unstemmed Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study
title_short Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study
title_sort short-time-window patlak imaging using a population-based arterial input function and optimized bayesian penalized likelihood reconstruction: a feasibility study
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9458796/
https://www.ncbi.nlm.nih.gov/pubmed/36075998
http://dx.doi.org/10.1186/s13550-022-00933-8
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