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Parallel workflow tools to facilitate human brain MRI post-processing

Multi-modal magnetic resonance imaging (MRI) techniques are widely applied in human brain studies. To obtain specific brain measures of interest from MRI datasets, a number of complex image post-processing steps are typically required. Parallel workflow tools have recently been developed, concatenat...

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Detalles Bibliográficos
Autores principales: Cui, Zaixu, Zhao, Chenxi, Gong, Gaolang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4429548/
https://www.ncbi.nlm.nih.gov/pubmed/26029043
http://dx.doi.org/10.3389/fnins.2015.00171
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author Cui, Zaixu
Zhao, Chenxi
Gong, Gaolang
author_facet Cui, Zaixu
Zhao, Chenxi
Gong, Gaolang
author_sort Cui, Zaixu
collection PubMed
description Multi-modal magnetic resonance imaging (MRI) techniques are widely applied in human brain studies. To obtain specific brain measures of interest from MRI datasets, a number of complex image post-processing steps are typically required. Parallel workflow tools have recently been developed, concatenating individual processing steps and enabling fully automated processing of raw MRI data to obtain the final results. These workflow tools are also designed to make optimal use of available computational resources and to support the parallel processing of different subjects or of independent processing steps for a single subject. Automated, parallel MRI post-processing tools can greatly facilitate relevant brain investigations and are being increasingly applied. In this review, we briefly summarize these parallel workflow tools and discuss relevant issues.
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spelling pubmed-44295482015-05-29 Parallel workflow tools to facilitate human brain MRI post-processing Cui, Zaixu Zhao, Chenxi Gong, Gaolang Front Neurosci Neuroscience Multi-modal magnetic resonance imaging (MRI) techniques are widely applied in human brain studies. To obtain specific brain measures of interest from MRI datasets, a number of complex image post-processing steps are typically required. Parallel workflow tools have recently been developed, concatenating individual processing steps and enabling fully automated processing of raw MRI data to obtain the final results. These workflow tools are also designed to make optimal use of available computational resources and to support the parallel processing of different subjects or of independent processing steps for a single subject. Automated, parallel MRI post-processing tools can greatly facilitate relevant brain investigations and are being increasingly applied. In this review, we briefly summarize these parallel workflow tools and discuss relevant issues. Frontiers Media S.A. 2015-05-13 /pmc/articles/PMC4429548/ /pubmed/26029043 http://dx.doi.org/10.3389/fnins.2015.00171 Text en Copyright © 2015 Cui, Zhao and Gong. 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
Cui, Zaixu
Zhao, Chenxi
Gong, Gaolang
Parallel workflow tools to facilitate human brain MRI post-processing
title Parallel workflow tools to facilitate human brain MRI post-processing
title_full Parallel workflow tools to facilitate human brain MRI post-processing
title_fullStr Parallel workflow tools to facilitate human brain MRI post-processing
title_full_unstemmed Parallel workflow tools to facilitate human brain MRI post-processing
title_short Parallel workflow tools to facilitate human brain MRI post-processing
title_sort parallel workflow tools to facilitate human brain mri post-processing
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4429548/
https://www.ncbi.nlm.nih.gov/pubmed/26029043
http://dx.doi.org/10.3389/fnins.2015.00171
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