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Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans

Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurement...

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Autores principales: Nárai, Ádám, Hermann, Petra, Auer, Tibor, Kemenczky, Péter, Szalma, János, Homolya, István, Somogyi, Eszter, Vakli, Pál, Weiss, Béla, Vidnyánszky, Zoltán
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9576686/
https://www.ncbi.nlm.nih.gov/pubmed/36253426
http://dx.doi.org/10.1038/s41597-022-01694-8
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author Nárai, Ádám
Hermann, Petra
Auer, Tibor
Kemenczky, Péter
Szalma, János
Homolya, István
Somogyi, Eszter
Vakli, Pál
Weiss, Béla
Vidnyánszky, Zoltán
author_facet Nárai, Ádám
Hermann, Petra
Auer, Tibor
Kemenczky, Péter
Szalma, János
Homolya, István
Somogyi, Eszter
Vakli, Pál
Weiss, Béla
Vidnyánszky, Zoltán
author_sort Nárai, Ádám
collection PubMed
description Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches.
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spelling pubmed-95766862022-10-19 Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans Nárai, Ádám Hermann, Petra Auer, Tibor Kemenczky, Péter Szalma, János Homolya, István Somogyi, Eszter Vakli, Pál Weiss, Béla Vidnyánszky, Zoltán Sci Data Data Descriptor Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches. Nature Publishing Group UK 2022-10-17 /pmc/articles/PMC9576686/ /pubmed/36253426 http://dx.doi.org/10.1038/s41597-022-01694-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Nárai, Ádám
Hermann, Petra
Auer, Tibor
Kemenczky, Péter
Szalma, János
Homolya, István
Somogyi, Eszter
Vakli, Pál
Weiss, Béla
Vidnyánszky, Zoltán
Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans
title Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans
title_full Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans
title_fullStr Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans
title_full_unstemmed Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans
title_short Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans
title_sort movement-related artefacts (mr-art) dataset of matched motion-corrupted and clean structural mri brain scans
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9576686/
https://www.ncbi.nlm.nih.gov/pubmed/36253426
http://dx.doi.org/10.1038/s41597-022-01694-8
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