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Reduction of bias in the evaluation of fractional anisotropy and mean diffusivity in magnetic resonance diffusion tensor imaging using region-of-interest methodology

Accurate quantification of fractional anisotropy (FA) and mean diffusivity (MD) in MR diffusion tensor imaging (DTI) requires adequate signal-to-noise ratio (SNR) especially in low FA areas of the brain, which necessitates clinically impractical long image acquisition times. We explored a SNR enhanc...

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Autores principales: Seo, Youngseob, Rollins, Nancy K., Wang, Zhiyue J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6739503/
https://www.ncbi.nlm.nih.gov/pubmed/31511553
http://dx.doi.org/10.1038/s41598-019-49311-w
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author Seo, Youngseob
Rollins, Nancy K.
Wang, Zhiyue J.
author_facet Seo, Youngseob
Rollins, Nancy K.
Wang, Zhiyue J.
author_sort Seo, Youngseob
collection PubMed
description Accurate quantification of fractional anisotropy (FA) and mean diffusivity (MD) in MR diffusion tensor imaging (DTI) requires adequate signal-to-noise ratio (SNR) especially in low FA areas of the brain, which necessitates clinically impractical long image acquisition times. We explored a SNR enhancement strategy using region-of-interest (ROI)-based diffusion tensor for quantification. DTI scans from a healthy male were acquired 15 times and combined into sets with different number of signal averages (NSA = 1–4, 15) at one 1.5-T Philips and three 3-T (Philips, Siemens and GE) scanners. Equivalence test was performed to determine NSA thresholds for bias-free FA and MD quantifications by comparison with reference values derived from images with NSA = 15. We examined brain areas with low FA values including caudate nucleus, globus pallidus, putamen, superior temporal gyrus, and substructures within thalamus (lateral dorsal, ventral anterior and posterior nuclei), where bias-free FA is difficult to obtain using a conventional approach. Our results showed that bias-free FA can be obtained with NSA = 2 or 3 in some cases using ROI-based analysis. ROI-based analysis allows reliable FA and MD quantifications in various brain structures previously difficult to study with clinically feasible data acquisition schemes.
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spelling pubmed-67395032019-09-26 Reduction of bias in the evaluation of fractional anisotropy and mean diffusivity in magnetic resonance diffusion tensor imaging using region-of-interest methodology Seo, Youngseob Rollins, Nancy K. Wang, Zhiyue J. Sci Rep Article Accurate quantification of fractional anisotropy (FA) and mean diffusivity (MD) in MR diffusion tensor imaging (DTI) requires adequate signal-to-noise ratio (SNR) especially in low FA areas of the brain, which necessitates clinically impractical long image acquisition times. We explored a SNR enhancement strategy using region-of-interest (ROI)-based diffusion tensor for quantification. DTI scans from a healthy male were acquired 15 times and combined into sets with different number of signal averages (NSA = 1–4, 15) at one 1.5-T Philips and three 3-T (Philips, Siemens and GE) scanners. Equivalence test was performed to determine NSA thresholds for bias-free FA and MD quantifications by comparison with reference values derived from images with NSA = 15. We examined brain areas with low FA values including caudate nucleus, globus pallidus, putamen, superior temporal gyrus, and substructures within thalamus (lateral dorsal, ventral anterior and posterior nuclei), where bias-free FA is difficult to obtain using a conventional approach. Our results showed that bias-free FA can be obtained with NSA = 2 or 3 in some cases using ROI-based analysis. ROI-based analysis allows reliable FA and MD quantifications in various brain structures previously difficult to study with clinically feasible data acquisition schemes. Nature Publishing Group UK 2019-09-11 /pmc/articles/PMC6739503/ /pubmed/31511553 http://dx.doi.org/10.1038/s41598-019-49311-w Text en © The Author(s) 2019 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Seo, Youngseob
Rollins, Nancy K.
Wang, Zhiyue J.
Reduction of bias in the evaluation of fractional anisotropy and mean diffusivity in magnetic resonance diffusion tensor imaging using region-of-interest methodology
title Reduction of bias in the evaluation of fractional anisotropy and mean diffusivity in magnetic resonance diffusion tensor imaging using region-of-interest methodology
title_full Reduction of bias in the evaluation of fractional anisotropy and mean diffusivity in magnetic resonance diffusion tensor imaging using region-of-interest methodology
title_fullStr Reduction of bias in the evaluation of fractional anisotropy and mean diffusivity in magnetic resonance diffusion tensor imaging using region-of-interest methodology
title_full_unstemmed Reduction of bias in the evaluation of fractional anisotropy and mean diffusivity in magnetic resonance diffusion tensor imaging using region-of-interest methodology
title_short Reduction of bias in the evaluation of fractional anisotropy and mean diffusivity in magnetic resonance diffusion tensor imaging using region-of-interest methodology
title_sort reduction of bias in the evaluation of fractional anisotropy and mean diffusivity in magnetic resonance diffusion tensor imaging using region-of-interest methodology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6739503/
https://www.ncbi.nlm.nih.gov/pubmed/31511553
http://dx.doi.org/10.1038/s41598-019-49311-w
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