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A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging
The mean diffusivity (MD) value has been used to describe microstructural properties in Diffusion Tensor Imaging (DTI) in cortical gray matter (GM). Recently, researchers have applied a cortical surface generated from the T1-weighted volume. When the DTI data are analyzed using the cortical surface,...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4500906/ https://www.ncbi.nlm.nih.gov/pubmed/26236180 http://dx.doi.org/10.3389/fnins.2015.00236 |
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author | Kwon, Oh-Hun Park, Hyunjin Seo, Sang-Won Na, Duk L. Lee, Jong-Min |
author_facet | Kwon, Oh-Hun Park, Hyunjin Seo, Sang-Won Na, Duk L. Lee, Jong-Min |
author_sort | Kwon, Oh-Hun |
collection | PubMed |
description | The mean diffusivity (MD) value has been used to describe microstructural properties in Diffusion Tensor Imaging (DTI) in cortical gray matter (GM). Recently, researchers have applied a cortical surface generated from the T1-weighted volume. When the DTI data are analyzed using the cortical surface, it is important to assign an accurate MD value from the volume space to the vertex of the cortical surface, considering the anatomical correspondence between the DTI and the T1-weighted image. Previous studies usually sampled the MD value using the nearest-neighbor (NN) method or Linear method, even though there are geometric distortions in diffusion-weighted volumes. Here we introduce a Surface Guided Diffusion Mapping (SGDM) method to compensate for such geometric distortions. We compared our SGDM method with results using NN and Linear methods by investigating differences in the sampled MD value. We also projected the tissue classification results of non-diffusion-weighted volumes to the cortical midsurface. The CSF probability values provided by the SGDM method were lower than those produced by the NN and Linear methods. The MD values provided by the NN and Linear methods were significantly greater than those of the SGDM method in regions suffering from geometric distortion. These results indicate that the NN and Linear methods assigned the MD value in the CSF region to the cortical midsurface (GM region). Our results suggest that the SGDM method is an effective way to correct such mapping errors. |
format | Online Article Text |
id | pubmed-4500906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-45009062015-07-31 A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging Kwon, Oh-Hun Park, Hyunjin Seo, Sang-Won Na, Duk L. Lee, Jong-Min Front Neurosci Neuroscience The mean diffusivity (MD) value has been used to describe microstructural properties in Diffusion Tensor Imaging (DTI) in cortical gray matter (GM). Recently, researchers have applied a cortical surface generated from the T1-weighted volume. When the DTI data are analyzed using the cortical surface, it is important to assign an accurate MD value from the volume space to the vertex of the cortical surface, considering the anatomical correspondence between the DTI and the T1-weighted image. Previous studies usually sampled the MD value using the nearest-neighbor (NN) method or Linear method, even though there are geometric distortions in diffusion-weighted volumes. Here we introduce a Surface Guided Diffusion Mapping (SGDM) method to compensate for such geometric distortions. We compared our SGDM method with results using NN and Linear methods by investigating differences in the sampled MD value. We also projected the tissue classification results of non-diffusion-weighted volumes to the cortical midsurface. The CSF probability values provided by the SGDM method were lower than those produced by the NN and Linear methods. The MD values provided by the NN and Linear methods were significantly greater than those of the SGDM method in regions suffering from geometric distortion. These results indicate that the NN and Linear methods assigned the MD value in the CSF region to the cortical midsurface (GM region). Our results suggest that the SGDM method is an effective way to correct such mapping errors. Frontiers Media S.A. 2015-07-14 /pmc/articles/PMC4500906/ /pubmed/26236180 http://dx.doi.org/10.3389/fnins.2015.00236 Text en Copyright © 2015 Kwon, Park, Seo, Na and Lee. 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) or licensor 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 Kwon, Oh-Hun Park, Hyunjin Seo, Sang-Won Na, Duk L. Lee, Jong-Min A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging |
title | A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging |
title_full | A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging |
title_fullStr | A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging |
title_full_unstemmed | A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging |
title_short | A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging |
title_sort | framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4500906/ https://www.ncbi.nlm.nih.gov/pubmed/26236180 http://dx.doi.org/10.3389/fnins.2015.00236 |
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