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Nighres: processing tools for high-resolution neuroimaging

With recent improvements in human magnetic resonance imaging (MRI) at ultra-high fields, the amount of data collected per subject in a given MRI experiment has increased considerably. Standard image processing packages are often challenged by the size of these data. Dedicated methods are needed to l...

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Detalles Bibliográficos
Autores principales: Huntenburg, Julia M, Steele, Christopher J, Bazin, Pierre-Louis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065481/
https://www.ncbi.nlm.nih.gov/pubmed/29982501
http://dx.doi.org/10.1093/gigascience/giy082
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author Huntenburg, Julia M
Steele, Christopher J
Bazin, Pierre-Louis
author_facet Huntenburg, Julia M
Steele, Christopher J
Bazin, Pierre-Louis
author_sort Huntenburg, Julia M
collection PubMed
description With recent improvements in human magnetic resonance imaging (MRI) at ultra-high fields, the amount of data collected per subject in a given MRI experiment has increased considerably. Standard image processing packages are often challenged by the size of these data. Dedicated methods are needed to leverage their extraordinary spatial resolution. Here, we introduce a flexible Python toolbox that implements a set of advanced techniques for high-resolution neuroimaging. With these tools, segmentation and laminar analysis of cortical MRI data can be performed at resolutions up to 500 μm in reasonable times. Comprehensive online documentation makes the toolbox easy to use and install. An extensive developer’s guide encourages contributions from other researchers that will help to accelerate progress in the promising field of high-resolution neuroimaging.
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spelling pubmed-60654812018-08-08 Nighres: processing tools for high-resolution neuroimaging Huntenburg, Julia M Steele, Christopher J Bazin, Pierre-Louis Gigascience Technical Note With recent improvements in human magnetic resonance imaging (MRI) at ultra-high fields, the amount of data collected per subject in a given MRI experiment has increased considerably. Standard image processing packages are often challenged by the size of these data. Dedicated methods are needed to leverage their extraordinary spatial resolution. Here, we introduce a flexible Python toolbox that implements a set of advanced techniques for high-resolution neuroimaging. With these tools, segmentation and laminar analysis of cortical MRI data can be performed at resolutions up to 500 μm in reasonable times. Comprehensive online documentation makes the toolbox easy to use and install. An extensive developer’s guide encourages contributions from other researchers that will help to accelerate progress in the promising field of high-resolution neuroimaging. Oxford University Press 2018-07-04 /pmc/articles/PMC6065481/ /pubmed/29982501 http://dx.doi.org/10.1093/gigascience/giy082 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Technical Note
Huntenburg, Julia M
Steele, Christopher J
Bazin, Pierre-Louis
Nighres: processing tools for high-resolution neuroimaging
title Nighres: processing tools for high-resolution neuroimaging
title_full Nighres: processing tools for high-resolution neuroimaging
title_fullStr Nighres: processing tools for high-resolution neuroimaging
title_full_unstemmed Nighres: processing tools for high-resolution neuroimaging
title_short Nighres: processing tools for high-resolution neuroimaging
title_sort nighres: processing tools for high-resolution neuroimaging
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065481/
https://www.ncbi.nlm.nih.gov/pubmed/29982501
http://dx.doi.org/10.1093/gigascience/giy082
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