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A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries
Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5306162/ https://www.ncbi.nlm.nih.gov/pubmed/27873151 http://dx.doi.org/10.1007/s12021-016-9320-y |
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author | Dunås, Tora Wåhlin, Anders Ambarki, Khalid Zarrinkoob, Laleh Malm, Jan Eklund, Anders |
author_facet | Dunås, Tora Wåhlin, Anders Ambarki, Khalid Zarrinkoob, Laleh Malm, Jan Eklund, Anders |
author_sort | Dunås, Tora |
collection | PubMed |
description | Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by trained radiologists. The objective of this work was to lay the foundation for such automated pipelining by constructing and evaluating a probabilistic atlas describing the shape and location of the major cerebral arteries. Specifically, we investigated how the implementation of a non-linear normalization into Montreal Neurological Institute (MNI) space improved the alignment of individual arterial branches. In a population-based cohort of 167 subjects, age 64–68 years, we performed 4D flow MRI with whole brain volumetric coverage, yielding both angiographic and anatomical data. For each subject, sixteen cerebral arteries were manually labeled to construct the atlas. Angiographic data were normalized to MNI space using both rigid-body and non-linear transformations obtained from anatomical images. The alignment of arterial branches was significantly improved by the non-linear normalization (p < 0.001). Validation of the atlas was based on its applicability in automatic arterial labeling. A leave-one-out validation scheme revealed a labeling accuracy of 96 %. Arterial labeling was also performed in a separate clinical sample (n = 10) with an accuracy of 92.5 %. In conclusion, using non-linear spatial normalization we constructed an artery-specific probabilistic atlas, useful for cerebral arterial labeling. |
format | Online Article Text |
id | pubmed-5306162 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-53061622017-02-24 A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries Dunås, Tora Wåhlin, Anders Ambarki, Khalid Zarrinkoob, Laleh Malm, Jan Eklund, Anders Neuroinformatics Original Article Improved whole brain angiographic and velocity-sensitive MRI is pushing the boundaries of noninvasively obtained cerebral vascular flow information. The complexity of the information contained in such datasets calls for automated algorithms and pipelines, thus reducing the need of manual analyses by trained radiologists. The objective of this work was to lay the foundation for such automated pipelining by constructing and evaluating a probabilistic atlas describing the shape and location of the major cerebral arteries. Specifically, we investigated how the implementation of a non-linear normalization into Montreal Neurological Institute (MNI) space improved the alignment of individual arterial branches. In a population-based cohort of 167 subjects, age 64–68 years, we performed 4D flow MRI with whole brain volumetric coverage, yielding both angiographic and anatomical data. For each subject, sixteen cerebral arteries were manually labeled to construct the atlas. Angiographic data were normalized to MNI space using both rigid-body and non-linear transformations obtained from anatomical images. The alignment of arterial branches was significantly improved by the non-linear normalization (p < 0.001). Validation of the atlas was based on its applicability in automatic arterial labeling. A leave-one-out validation scheme revealed a labeling accuracy of 96 %. Arterial labeling was also performed in a separate clinical sample (n = 10) with an accuracy of 92.5 %. In conclusion, using non-linear spatial normalization we constructed an artery-specific probabilistic atlas, useful for cerebral arterial labeling. Springer US 2016-11-21 2017 /pmc/articles/PMC5306162/ /pubmed/27873151 http://dx.doi.org/10.1007/s12021-016-9320-y Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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. |
spellingShingle | Original Article Dunås, Tora Wåhlin, Anders Ambarki, Khalid Zarrinkoob, Laleh Malm, Jan Eklund, Anders A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries |
title | A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries |
title_full | A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries |
title_fullStr | A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries |
title_full_unstemmed | A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries |
title_short | A Stereotactic Probabilistic Atlas for the Major Cerebral Arteries |
title_sort | stereotactic probabilistic atlas for the major cerebral arteries |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5306162/ https://www.ncbi.nlm.nih.gov/pubmed/27873151 http://dx.doi.org/10.1007/s12021-016-9320-y |
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