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Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration
Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338502/ https://www.ncbi.nlm.nih.gov/pubmed/37438367 http://dx.doi.org/10.1038/s41597-023-02330-9 |
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author | Taha, Alaa Gilmore, Greydon Abbass, Mohamad Kai, Jason Kuehn, Tristan Demarco, John Gupta, Geetika Zajner, Chris Cao, Daniel Chevalier, Ryan Ahmed, Abrar Hadi, Ali Karat, Bradley G. Stanley, Olivia W. Park, Patrick J. Ferko, Kayla M. Hemachandra, Dimuthu Vassallo, Reid Jach, Magdalena Thurairajah, Arun Wong, Sandy Tenorio, Mauricio C. Ogunsanya, Feyi Khan, Ali R. Lau, Jonathan C. |
author_facet | Taha, Alaa Gilmore, Greydon Abbass, Mohamad Kai, Jason Kuehn, Tristan Demarco, John Gupta, Geetika Zajner, Chris Cao, Daniel Chevalier, Ryan Ahmed, Abrar Hadi, Ali Karat, Bradley G. Stanley, Olivia W. Park, Patrick J. Ferko, Kayla M. Hemachandra, Dimuthu Vassallo, Reid Jach, Magdalena Thurairajah, Arun Wong, Sandy Tenorio, Mauricio C. Ogunsanya, Feyi Khan, Ali R. Lau, Jonathan C. |
author_sort | Taha, Alaa |
collection | PubMed |
description | Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging datasets and templates. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (N = 132 subjects for a total of 15,232 fiducials) and 14 brain templates (4,288 fiducials), totalling more than 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1 – 2 mm (using more than 45,000 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure allowing for facile incorporation into neuroimaging analysis pipelines. |
format | Online Article Text |
id | pubmed-10338502 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103385022023-07-14 Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration Taha, Alaa Gilmore, Greydon Abbass, Mohamad Kai, Jason Kuehn, Tristan Demarco, John Gupta, Geetika Zajner, Chris Cao, Daniel Chevalier, Ryan Ahmed, Abrar Hadi, Ali Karat, Bradley G. Stanley, Olivia W. Park, Patrick J. Ferko, Kayla M. Hemachandra, Dimuthu Vassallo, Reid Jach, Magdalena Thurairajah, Arun Wong, Sandy Tenorio, Mauricio C. Ogunsanya, Feyi Khan, Ali R. Lau, Jonathan C. Sci Data Data Descriptor Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging datasets and templates. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (N = 132 subjects for a total of 15,232 fiducials) and 14 brain templates (4,288 fiducials), totalling more than 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1 – 2 mm (using more than 45,000 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure allowing for facile incorporation into neuroimaging analysis pipelines. Nature Publishing Group UK 2023-07-12 /pmc/articles/PMC10338502/ /pubmed/37438367 http://dx.doi.org/10.1038/s41597-023-02330-9 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 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 Taha, Alaa Gilmore, Greydon Abbass, Mohamad Kai, Jason Kuehn, Tristan Demarco, John Gupta, Geetika Zajner, Chris Cao, Daniel Chevalier, Ryan Ahmed, Abrar Hadi, Ali Karat, Bradley G. Stanley, Olivia W. Park, Patrick J. Ferko, Kayla M. Hemachandra, Dimuthu Vassallo, Reid Jach, Magdalena Thurairajah, Arun Wong, Sandy Tenorio, Mauricio C. Ogunsanya, Feyi Khan, Ali R. Lau, Jonathan C. Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration |
title | Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration |
title_full | Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration |
title_fullStr | Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration |
title_full_unstemmed | Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration |
title_short | Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration |
title_sort | magnetic resonance imaging datasets with anatomical fiducials for quality control and registration |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338502/ https://www.ncbi.nlm.nih.gov/pubmed/37438367 http://dx.doi.org/10.1038/s41597-023-02330-9 |
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