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Development and evaluation of an automated quantification tool for amyloid PET images

BACKGROUND: Quantitative evaluation of amyloid positron emission tomography (PET) with standardized uptake value ratio (SUVR) plays a key role in clinical studies of Alzheimer’s disease (AD). We have proposed a PET-only (MR-free) amyloid quantification method, although some commercial software packa...

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Autores principales: Tsubaki, Yuma, Akamatsu, Go, Shimokawa, Natsumi, Katsube, Suguru, Takashima, Aya, Sasaki, Masayuki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7524908/
https://www.ncbi.nlm.nih.gov/pubmed/32990884
http://dx.doi.org/10.1186/s40658-020-00329-4
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author Tsubaki, Yuma
Akamatsu, Go
Shimokawa, Natsumi
Katsube, Suguru
Takashima, Aya
Sasaki, Masayuki
author_facet Tsubaki, Yuma
Akamatsu, Go
Shimokawa, Natsumi
Katsube, Suguru
Takashima, Aya
Sasaki, Masayuki
author_sort Tsubaki, Yuma
collection PubMed
description BACKGROUND: Quantitative evaluation of amyloid positron emission tomography (PET) with standardized uptake value ratio (SUVR) plays a key role in clinical studies of Alzheimer’s disease (AD). We have proposed a PET-only (MR-free) amyloid quantification method, although some commercial software packages are required. The aim of this study was to develop an automated quantification tool for amyloid PET without using commercial software. METHODS: The quantification tool was created by combining four components: (1) anatomical standardization to positive and negative templates using NEUROSTAT stereo.exe; (2) similarity calculation between standardized images and respective templates based on normalized cross-correlation (selection of the image for SUVR measurement); (3) voxel value normalization by the mean value of reference regions (making an SUVR-scaled image); and (4) SUVR calculation based on pre-defined regions of interest (ROIs). We examined 166 subjects who underwent a [(11)C] Pittsburgh compound-B PET scan through the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) study. SUVRs in five ROIs (frontal lobe, temporal lobe, parietal lobe, occipital lobe, and posterior cingulate cortex and precuneus) were calculated with the cerebellar cortex as the reference region. The SUVRs obtained by our tool were compared with manual step-by-step processing and the conventional PMOD-based method (PMOD Technologies, Switzerland). RESULTS: Compared with manual step-by-step processing, our developed automated quantification tool reduced processing time by 85%. The SUVRs obtained by the developed quantification tool were consistent with those obtained by manual processing. Compared with the conventional PMOD-based method, the developed quantification tool provided 1.5% lower SUVR values, on average. We determined that this bias is likely due to the difference in anatomical standardization methods. CONCLUSIONS: We developed an automated quantification tool for amyloid PET images. Using this tool, SUVR values can be quickly measured without individual MRI and without commercial software. This quantification tool may be useful for clinical studies of AD.
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spelling pubmed-75249082020-10-14 Development and evaluation of an automated quantification tool for amyloid PET images Tsubaki, Yuma Akamatsu, Go Shimokawa, Natsumi Katsube, Suguru Takashima, Aya Sasaki, Masayuki EJNMMI Phys Young Investigators BACKGROUND: Quantitative evaluation of amyloid positron emission tomography (PET) with standardized uptake value ratio (SUVR) plays a key role in clinical studies of Alzheimer’s disease (AD). We have proposed a PET-only (MR-free) amyloid quantification method, although some commercial software packages are required. The aim of this study was to develop an automated quantification tool for amyloid PET without using commercial software. METHODS: The quantification tool was created by combining four components: (1) anatomical standardization to positive and negative templates using NEUROSTAT stereo.exe; (2) similarity calculation between standardized images and respective templates based on normalized cross-correlation (selection of the image for SUVR measurement); (3) voxel value normalization by the mean value of reference regions (making an SUVR-scaled image); and (4) SUVR calculation based on pre-defined regions of interest (ROIs). We examined 166 subjects who underwent a [(11)C] Pittsburgh compound-B PET scan through the Japanese Alzheimer’s Disease Neuroimaging Initiative (J-ADNI) study. SUVRs in five ROIs (frontal lobe, temporal lobe, parietal lobe, occipital lobe, and posterior cingulate cortex and precuneus) were calculated with the cerebellar cortex as the reference region. The SUVRs obtained by our tool were compared with manual step-by-step processing and the conventional PMOD-based method (PMOD Technologies, Switzerland). RESULTS: Compared with manual step-by-step processing, our developed automated quantification tool reduced processing time by 85%. The SUVRs obtained by the developed quantification tool were consistent with those obtained by manual processing. Compared with the conventional PMOD-based method, the developed quantification tool provided 1.5% lower SUVR values, on average. We determined that this bias is likely due to the difference in anatomical standardization methods. CONCLUSIONS: We developed an automated quantification tool for amyloid PET images. Using this tool, SUVR values can be quickly measured without individual MRI and without commercial software. This quantification tool may be useful for clinical studies of AD. Springer International Publishing 2020-09-29 /pmc/articles/PMC7524908/ /pubmed/32990884 http://dx.doi.org/10.1186/s40658-020-00329-4 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 Young Investigators
Tsubaki, Yuma
Akamatsu, Go
Shimokawa, Natsumi
Katsube, Suguru
Takashima, Aya
Sasaki, Masayuki
Development and evaluation of an automated quantification tool for amyloid PET images
title Development and evaluation of an automated quantification tool for amyloid PET images
title_full Development and evaluation of an automated quantification tool for amyloid PET images
title_fullStr Development and evaluation of an automated quantification tool for amyloid PET images
title_full_unstemmed Development and evaluation of an automated quantification tool for amyloid PET images
title_short Development and evaluation of an automated quantification tool for amyloid PET images
title_sort development and evaluation of an automated quantification tool for amyloid pet images
topic Young Investigators
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7524908/
https://www.ncbi.nlm.nih.gov/pubmed/32990884
http://dx.doi.org/10.1186/s40658-020-00329-4
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