Cargando…
Region- and voxel-based quantification in human brain of [(18)F]LSN3316612, a radioligand for O-GlcNAcase
BACKGROUND: Previous studies found that the positron emission tomography (PET) radioligand [(18)F]LSN3316612 accurately quantified O-GlcNAcase in human brain using a two-tissue compartment model (2TCM). This study sought to assess kinetic model(s) as an alternative to 2TCM for quantifying [(18)F]LSN...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017047/ https://www.ncbi.nlm.nih.gov/pubmed/33796956 http://dx.doi.org/10.1186/s13550-021-00780-z |
_version_ | 1783673981597908992 |
---|---|
author | Lee, Jae-Hoon Veronese, Mattia Liow, Jeih-San Morse, Cheryl L. Montero Santamaria, Jose A. Haskali, Mohammad B. Zoghbi, Sami S. Pike, Victor W. Innis, Robert B. Zanotti-Fregonara, Paolo |
author_facet | Lee, Jae-Hoon Veronese, Mattia Liow, Jeih-San Morse, Cheryl L. Montero Santamaria, Jose A. Haskali, Mohammad B. Zoghbi, Sami S. Pike, Victor W. Innis, Robert B. Zanotti-Fregonara, Paolo |
author_sort | Lee, Jae-Hoon |
collection | PubMed |
description | BACKGROUND: Previous studies found that the positron emission tomography (PET) radioligand [(18)F]LSN3316612 accurately quantified O-GlcNAcase in human brain using a two-tissue compartment model (2TCM). This study sought to assess kinetic model(s) as an alternative to 2TCM for quantifying [(18)F]LSN3316612 binding, particularly in order to generate good-quality parametric images. METHODS: The current study reanalyzed data from a previous study of 10 healthy volunteers who underwent both test and retest PET scans with [(18)F]LSN3316612. Kinetic analysis was performed at the region level with 2TCM using 120-min PET data and arterial input function, which was considered as the gold standard. Quantification was then obtained at both the region and voxel levels using Logan plot, Ichise's multilinear analysis-1 (MA1), standard spectral analysis (SA), and impulse response function at 120 min (IRF(120)). To avoid arterial sampling, a noninvasive relative quantification (standardized uptake value ratio (SUVR)) was also tested using the corpus callosum as a pseudo-reference region. Venous samples were also assessed to see whether they could substitute for arterial ones. RESULTS: Logan and MA1 generated parametric images of good visual quality and their total distribution volume (V(T)) values at both the region and voxel levels were strongly correlated with 2TCM-derived V(T) (r = 0.96–0.99) and showed little bias (up to − 8%). SA was more weakly correlated to 2TCM-derived V(T) (r = 0.93–0.98) and was more biased (~ 16%). IRF(120) showed a strong correlation with 2TCM-derived V(T) (r = 0.96) but generated noisier parametric images. All techniques were comparable to 2TCM in terms of test–retest variability and reliability except IRF(120), which gave significantly worse results. Noninvasive SUVR values were not correlated with 2TCM-derived V(T), and arteriovenous equilibrium was never reached. CONCLUSIONS: Compared to SA and IRF, Logan and MA1 are more suitable alternatives to 2TCM for quantifying [(18)F]LSN3316612 and generating good-quality parametric images. |
format | Online Article Text |
id | pubmed-8017047 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-80170472021-04-16 Region- and voxel-based quantification in human brain of [(18)F]LSN3316612, a radioligand for O-GlcNAcase Lee, Jae-Hoon Veronese, Mattia Liow, Jeih-San Morse, Cheryl L. Montero Santamaria, Jose A. Haskali, Mohammad B. Zoghbi, Sami S. Pike, Victor W. Innis, Robert B. Zanotti-Fregonara, Paolo EJNMMI Res Original Research BACKGROUND: Previous studies found that the positron emission tomography (PET) radioligand [(18)F]LSN3316612 accurately quantified O-GlcNAcase in human brain using a two-tissue compartment model (2TCM). This study sought to assess kinetic model(s) as an alternative to 2TCM for quantifying [(18)F]LSN3316612 binding, particularly in order to generate good-quality parametric images. METHODS: The current study reanalyzed data from a previous study of 10 healthy volunteers who underwent both test and retest PET scans with [(18)F]LSN3316612. Kinetic analysis was performed at the region level with 2TCM using 120-min PET data and arterial input function, which was considered as the gold standard. Quantification was then obtained at both the region and voxel levels using Logan plot, Ichise's multilinear analysis-1 (MA1), standard spectral analysis (SA), and impulse response function at 120 min (IRF(120)). To avoid arterial sampling, a noninvasive relative quantification (standardized uptake value ratio (SUVR)) was also tested using the corpus callosum as a pseudo-reference region. Venous samples were also assessed to see whether they could substitute for arterial ones. RESULTS: Logan and MA1 generated parametric images of good visual quality and their total distribution volume (V(T)) values at both the region and voxel levels were strongly correlated with 2TCM-derived V(T) (r = 0.96–0.99) and showed little bias (up to − 8%). SA was more weakly correlated to 2TCM-derived V(T) (r = 0.93–0.98) and was more biased (~ 16%). IRF(120) showed a strong correlation with 2TCM-derived V(T) (r = 0.96) but generated noisier parametric images. All techniques were comparable to 2TCM in terms of test–retest variability and reliability except IRF(120), which gave significantly worse results. Noninvasive SUVR values were not correlated with 2TCM-derived V(T), and arteriovenous equilibrium was never reached. CONCLUSIONS: Compared to SA and IRF, Logan and MA1 are more suitable alternatives to 2TCM for quantifying [(18)F]LSN3316612 and generating good-quality parametric images. Springer Berlin Heidelberg 2021-04-01 /pmc/articles/PMC8017047/ /pubmed/33796956 http://dx.doi.org/10.1186/s13550-021-00780-z Text en © The Author(s) 2021 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 | Original Research Lee, Jae-Hoon Veronese, Mattia Liow, Jeih-San Morse, Cheryl L. Montero Santamaria, Jose A. Haskali, Mohammad B. Zoghbi, Sami S. Pike, Victor W. Innis, Robert B. Zanotti-Fregonara, Paolo Region- and voxel-based quantification in human brain of [(18)F]LSN3316612, a radioligand for O-GlcNAcase |
title | Region- and voxel-based quantification in human brain of [(18)F]LSN3316612, a radioligand for O-GlcNAcase |
title_full | Region- and voxel-based quantification in human brain of [(18)F]LSN3316612, a radioligand for O-GlcNAcase |
title_fullStr | Region- and voxel-based quantification in human brain of [(18)F]LSN3316612, a radioligand for O-GlcNAcase |
title_full_unstemmed | Region- and voxel-based quantification in human brain of [(18)F]LSN3316612, a radioligand for O-GlcNAcase |
title_short | Region- and voxel-based quantification in human brain of [(18)F]LSN3316612, a radioligand for O-GlcNAcase |
title_sort | region- and voxel-based quantification in human brain of [(18)f]lsn3316612, a radioligand for o-glcnacase |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8017047/ https://www.ncbi.nlm.nih.gov/pubmed/33796956 http://dx.doi.org/10.1186/s13550-021-00780-z |
work_keys_str_mv | AT leejaehoon regionandvoxelbasedquantificationinhumanbrainof18flsn3316612aradioligandforoglcnacase AT veronesemattia regionandvoxelbasedquantificationinhumanbrainof18flsn3316612aradioligandforoglcnacase AT liowjeihsan regionandvoxelbasedquantificationinhumanbrainof18flsn3316612aradioligandforoglcnacase AT morsecheryll regionandvoxelbasedquantificationinhumanbrainof18flsn3316612aradioligandforoglcnacase AT monterosantamariajosea regionandvoxelbasedquantificationinhumanbrainof18flsn3316612aradioligandforoglcnacase AT haskalimohammadb regionandvoxelbasedquantificationinhumanbrainof18flsn3316612aradioligandforoglcnacase AT zoghbisamis regionandvoxelbasedquantificationinhumanbrainof18flsn3316612aradioligandforoglcnacase AT pikevictorw regionandvoxelbasedquantificationinhumanbrainof18flsn3316612aradioligandforoglcnacase AT innisrobertb regionandvoxelbasedquantificationinhumanbrainof18flsn3316612aradioligandforoglcnacase AT zanottifregonarapaolo regionandvoxelbasedquantificationinhumanbrainof18flsn3316612aradioligandforoglcnacase |