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A hitchhiker's guide to diffusion tensor imaging

Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on...

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Detalles Bibliográficos
Autores principales: Soares, José M., Marques, Paulo, Alves, Victor, Sousa, Nuno
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594764/
https://www.ncbi.nlm.nih.gov/pubmed/23486659
http://dx.doi.org/10.3389/fnins.2013.00031
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author Soares, José M.
Marques, Paulo
Alves, Victor
Sousa, Nuno
author_facet Soares, José M.
Marques, Paulo
Alves, Victor
Sousa, Nuno
author_sort Soares, José M.
collection PubMed
description Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.
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spelling pubmed-35947642013-03-13 A hitchhiker's guide to diffusion tensor imaging Soares, José M. Marques, Paulo Alves, Victor Sousa, Nuno Front Neurosci Neuroscience Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI. Frontiers Media S.A. 2013-03-12 /pmc/articles/PMC3594764/ /pubmed/23486659 http://dx.doi.org/10.3389/fnins.2013.00031 Text en Copyright © 2013 Soares, Marques, Alves and Sousa. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
spellingShingle Neuroscience
Soares, José M.
Marques, Paulo
Alves, Victor
Sousa, Nuno
A hitchhiker's guide to diffusion tensor imaging
title A hitchhiker's guide to diffusion tensor imaging
title_full A hitchhiker's guide to diffusion tensor imaging
title_fullStr A hitchhiker's guide to diffusion tensor imaging
title_full_unstemmed A hitchhiker's guide to diffusion tensor imaging
title_short A hitchhiker's guide to diffusion tensor imaging
title_sort hitchhiker's guide to diffusion tensor imaging
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3594764/
https://www.ncbi.nlm.nih.gov/pubmed/23486659
http://dx.doi.org/10.3389/fnins.2013.00031
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