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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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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer Japan
2016
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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. |
format | Online Article Text |
id | pubmed-4925692 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Japan |
record_format | MEDLINE/PubMed |
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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