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Optimal HMPAO α value for Lassen’s correction algorithm obscured by statistical noise

OBJECTIVE: [(99m)Tc] d,l-hexamethyl-propyeneamine oxime ((99m)Tc-HMPAO), a brain perfusion tracer, suffers significant underestimation of regional cerebral blood flow (rCBF). Lassen et al. developed their linearization algorithm to correct the influence of back-diffusion of the tracer, and proposed...

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Autores principales: Kameyama, Masashi, Murakami, Koji, Jinzaki, Masahiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Japan 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4925692/
https://www.ncbi.nlm.nih.gov/pubmed/27017602
http://dx.doi.org/10.1007/s12149-016-1073-z
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author Kameyama, Masashi
Murakami, Koji
Jinzaki, Masahiro
author_facet Kameyama, Masashi
Murakami, Koji
Jinzaki, Masahiro
author_sort Kameyama, Masashi
collection PubMed
description OBJECTIVE: [(99m)Tc] d,l-hexamethyl-propyeneamine oxime ((99m)Tc-HMPAO), a brain perfusion tracer, suffers significant underestimation of regional cerebral blood flow (rCBF). Lassen et al. developed their linearization algorithm to correct the influence of back-diffusion of the tracer, and proposed their parameter α as 1.5. Based on mathematical modeling and literature review, recently, a new α value of 0.5 has been proposed for Lassen’s correction algorithm for (99m)Tc-HMPAO, although correction using the old α value of 1.5 was confirmed to be sufficient. Inugami et al. reported that linearization correction gives a stable correlation coefficient over a wide range of α. Our hypotheses are that statistical noise is the source of the stable correlation coefficient presented by them and that the robustness of the correlation coefficient is the reason why many studies confirmed the value of α as 1.5. METHODS: Statistical noise was added in silico to the count, whose relationship with flow was α = 0.5. Then, the count was corrected by Lassen’s linearization algorithm with a variety of α. RESULTS: This study confirmed the hypothesis that smaller α values (strong correction) increase the noise at high flow values, leading to nominal increases in correlation coefficient as α decreases. CONCLUSION: Despite this, adoption of the new, smaller α value of 0.5 would be more useful clinically in regaining the contrast between low-flow and high-flow areas of the brain. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12149-016-1073-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-49256922016-07-12 Optimal HMPAO α value for Lassen’s correction algorithm obscured by statistical noise Kameyama, Masashi Murakami, Koji Jinzaki, Masahiro Ann Nucl Med Short Communication OBJECTIVE: [(99m)Tc] d,l-hexamethyl-propyeneamine oxime ((99m)Tc-HMPAO), a brain perfusion tracer, suffers significant underestimation of regional cerebral blood flow (rCBF). Lassen et al. developed their linearization algorithm to correct the influence of back-diffusion of the tracer, and proposed their parameter α as 1.5. Based on mathematical modeling and literature review, recently, a new α value of 0.5 has been proposed for Lassen’s correction algorithm for (99m)Tc-HMPAO, although correction using the old α value of 1.5 was confirmed to be sufficient. Inugami et al. reported that linearization correction gives a stable correlation coefficient over a wide range of α. Our hypotheses are that statistical noise is the source of the stable correlation coefficient presented by them and that the robustness of the correlation coefficient is the reason why many studies confirmed the value of α as 1.5. METHODS: Statistical noise was added in silico to the count, whose relationship with flow was α = 0.5. Then, the count was corrected by Lassen’s linearization algorithm with a variety of α. RESULTS: This study confirmed the hypothesis that smaller α values (strong correction) increase the noise at high flow values, leading to nominal increases in correlation coefficient as α decreases. CONCLUSION: Despite this, adoption of the new, smaller α value of 0.5 would be more useful clinically in regaining the contrast between low-flow and high-flow areas of the brain. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12149-016-1073-z) contains supplementary material, which is available to authorized users. Springer Japan 2016-03-26 2016 /pmc/articles/PMC4925692/ /pubmed/27017602 http://dx.doi.org/10.1007/s12149-016-1073-z Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Short Communication
Kameyama, Masashi
Murakami, Koji
Jinzaki, Masahiro
Optimal HMPAO α value for Lassen’s correction algorithm obscured by statistical noise
title Optimal HMPAO α value for Lassen’s correction algorithm obscured by statistical noise
title_full Optimal HMPAO α value for Lassen’s correction algorithm obscured by statistical noise
title_fullStr Optimal HMPAO α value for Lassen’s correction algorithm obscured by statistical noise
title_full_unstemmed Optimal HMPAO α value for Lassen’s correction algorithm obscured by statistical noise
title_short Optimal HMPAO α value for Lassen’s correction algorithm obscured by statistical noise
title_sort optimal hmpao α value for lassen’s correction algorithm obscured by statistical noise
topic Short Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4925692/
https://www.ncbi.nlm.nih.gov/pubmed/27017602
http://dx.doi.org/10.1007/s12149-016-1073-z
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