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HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations
We introduce HumanBrainAtlas, an initiative to construct a highly detailed, open-access atlas of the living human brain that combines high-resolution in vivo MR imaging and detailed segmentations previously possible only in histological preparations. Here, we present and evaluate the first step of t...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516788/ https://www.ncbi.nlm.nih.gov/pubmed/37277567 http://dx.doi.org/10.1007/s00429-023-02653-8 |
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author | Schira, Mark M. Isherwood, Zoey J. Kassem, Mustafa S. Barth, Markus Shaw, Thomas B. Roberts, Michelle M. Paxinos, George |
author_facet | Schira, Mark M. Isherwood, Zoey J. Kassem, Mustafa S. Barth, Markus Shaw, Thomas B. Roberts, Michelle M. Paxinos, George |
author_sort | Schira, Mark M. |
collection | PubMed |
description | We introduce HumanBrainAtlas, an initiative to construct a highly detailed, open-access atlas of the living human brain that combines high-resolution in vivo MR imaging and detailed segmentations previously possible only in histological preparations. Here, we present and evaluate the first step of this initiative: a comprehensive dataset of two healthy male volunteers reconstructed to a 0.25 mm isotropic resolution for T1w, T2w, and DWI contrasts. Multiple high-resolution acquisitions were collected for each contrast and each participant, followed by averaging using symmetric group-wise normalisation (Advanced Normalisation Tools). The resulting image quality permits structural parcellations rivalling histology-based atlases, while maintaining the advantages of in vivo MRI. For example, components of the thalamus, hypothalamus, and hippocampus are often impossible to identify using standard MRI protocols—can be identified within the present data. Our data are virtually distortion free, fully 3D, and compatible with the existing in vivo Neuroimaging analysis tools. The dataset is suitable for teaching and is publicly available via our website (hba.neura.edu.au), which also provides data processing scripts. Instead of focusing on coordinates in an averaged brain space, our approach focuses on providing an example segmentation at great detail in the high-quality individual brain. This serves as an illustration on what features contrasts and relations can be used to interpret MRI datasets, in research, clinical, and education settings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-023-02653-8. |
format | Online Article Text |
id | pubmed-10516788 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-105167882023-09-24 HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations Schira, Mark M. Isherwood, Zoey J. Kassem, Mustafa S. Barth, Markus Shaw, Thomas B. Roberts, Michelle M. Paxinos, George Brain Struct Funct Original Article We introduce HumanBrainAtlas, an initiative to construct a highly detailed, open-access atlas of the living human brain that combines high-resolution in vivo MR imaging and detailed segmentations previously possible only in histological preparations. Here, we present and evaluate the first step of this initiative: a comprehensive dataset of two healthy male volunteers reconstructed to a 0.25 mm isotropic resolution for T1w, T2w, and DWI contrasts. Multiple high-resolution acquisitions were collected for each contrast and each participant, followed by averaging using symmetric group-wise normalisation (Advanced Normalisation Tools). The resulting image quality permits structural parcellations rivalling histology-based atlases, while maintaining the advantages of in vivo MRI. For example, components of the thalamus, hypothalamus, and hippocampus are often impossible to identify using standard MRI protocols—can be identified within the present data. Our data are virtually distortion free, fully 3D, and compatible with the existing in vivo Neuroimaging analysis tools. The dataset is suitable for teaching and is publicly available via our website (hba.neura.edu.au), which also provides data processing scripts. Instead of focusing on coordinates in an averaged brain space, our approach focuses on providing an example segmentation at great detail in the high-quality individual brain. This serves as an illustration on what features contrasts and relations can be used to interpret MRI datasets, in research, clinical, and education settings. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-023-02653-8. Springer Berlin Heidelberg 2023-06-05 2023 /pmc/articles/PMC10516788/ /pubmed/37277567 http://dx.doi.org/10.1007/s00429-023-02653-8 Text en © The Author(s) 2023 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Schira, Mark M. Isherwood, Zoey J. Kassem, Mustafa S. Barth, Markus Shaw, Thomas B. Roberts, Michelle M. Paxinos, George HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations |
title | HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations |
title_full | HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations |
title_fullStr | HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations |
title_full_unstemmed | HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations |
title_short | HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations |
title_sort | humanbrainatlas: an in vivo mri dataset for detailed segmentations |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516788/ https://www.ncbi.nlm.nih.gov/pubmed/37277567 http://dx.doi.org/10.1007/s00429-023-02653-8 |
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