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Influence of 7T GRE-MRI Signal Compartment Model Choice on Tissue Parameters

Quantitative assessment of tissue microstructure is important in studying human brain diseases and disorders. Ultra-high field magnetic resonance imaging (MRI) data obtained using a multi-echo gradient echo sequence have been shown to contain information on myelin, axonal, and extracellular compartm...

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Autores principales: Thapaliya, Kiran, Vegh, Viktor, Bollmann, Steffen, Barth, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206227/
https://www.ncbi.nlm.nih.gov/pubmed/32457565
http://dx.doi.org/10.3389/fnins.2020.00271
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author Thapaliya, Kiran
Vegh, Viktor
Bollmann, Steffen
Barth, Markus
author_facet Thapaliya, Kiran
Vegh, Viktor
Bollmann, Steffen
Barth, Markus
author_sort Thapaliya, Kiran
collection PubMed
description Quantitative assessment of tissue microstructure is important in studying human brain diseases and disorders. Ultra-high field magnetic resonance imaging (MRI) data obtained using a multi-echo gradient echo sequence have been shown to contain information on myelin, axonal, and extracellular compartments in tissue. Quantitative assessment of water fraction, relaxation time (T(2)*), and frequency shift using multi-compartment models has been shown to be useful in studying white matter properties via specific tissue parameters. It remains unclear how tissue parameters vary with model selection based on 7T multiple echo time gradient-recalled echo (GRE) MRI data. We applied existing signal compartment models to the corpus callosum and investigated whether a three-compartment model can be reduced to two compartments and still resolve white matter parameters [i.e., myelin water fraction (MWF) and g-ratio]. We show that MWF should be computed using a three-compartment model in the corpus callosum, and the g-ratios obtained using three compartment models are consistent with previous reports. We provide results for other parameters, such as signal compartment frequency shifts.
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spelling pubmed-72062272020-05-25 Influence of 7T GRE-MRI Signal Compartment Model Choice on Tissue Parameters Thapaliya, Kiran Vegh, Viktor Bollmann, Steffen Barth, Markus Front Neurosci Neuroscience Quantitative assessment of tissue microstructure is important in studying human brain diseases and disorders. Ultra-high field magnetic resonance imaging (MRI) data obtained using a multi-echo gradient echo sequence have been shown to contain information on myelin, axonal, and extracellular compartments in tissue. Quantitative assessment of water fraction, relaxation time (T(2)*), and frequency shift using multi-compartment models has been shown to be useful in studying white matter properties via specific tissue parameters. It remains unclear how tissue parameters vary with model selection based on 7T multiple echo time gradient-recalled echo (GRE) MRI data. We applied existing signal compartment models to the corpus callosum and investigated whether a three-compartment model can be reduced to two compartments and still resolve white matter parameters [i.e., myelin water fraction (MWF) and g-ratio]. We show that MWF should be computed using a three-compartment model in the corpus callosum, and the g-ratios obtained using three compartment models are consistent with previous reports. We provide results for other parameters, such as signal compartment frequency shifts. Frontiers Media S.A. 2020-04-28 /pmc/articles/PMC7206227/ /pubmed/32457565 http://dx.doi.org/10.3389/fnins.2020.00271 Text en Copyright © 2020 Thapaliya, Vegh, Bollmann and Barth. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Thapaliya, Kiran
Vegh, Viktor
Bollmann, Steffen
Barth, Markus
Influence of 7T GRE-MRI Signal Compartment Model Choice on Tissue Parameters
title Influence of 7T GRE-MRI Signal Compartment Model Choice on Tissue Parameters
title_full Influence of 7T GRE-MRI Signal Compartment Model Choice on Tissue Parameters
title_fullStr Influence of 7T GRE-MRI Signal Compartment Model Choice on Tissue Parameters
title_full_unstemmed Influence of 7T GRE-MRI Signal Compartment Model Choice on Tissue Parameters
title_short Influence of 7T GRE-MRI Signal Compartment Model Choice on Tissue Parameters
title_sort influence of 7t gre-mri signal compartment model choice on tissue parameters
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206227/
https://www.ncbi.nlm.nih.gov/pubmed/32457565
http://dx.doi.org/10.3389/fnins.2020.00271
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