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Automated detection of cerebral microbleeds on T2*-weighted MRI

Cerebral microbleeds, observed as small, spherical hypointense regions on gradient echo (GRE) or susceptibility weighted (SWI) magnetic resonance imaging (MRI) sequences, reflect small hemorrhagic infarcts, and are associated with conditions such as vascular dementia, small vessel disease, cerebral...

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Autores principales: Chesebro, Anthony G., Amarante, Erica, Lao, Patrick J., Meier, Irene B., Mayeux, Richard, Brickman, Adam M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7889861/
https://www.ncbi.nlm.nih.gov/pubmed/33597663
http://dx.doi.org/10.1038/s41598-021-83607-0
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author Chesebro, Anthony G.
Amarante, Erica
Lao, Patrick J.
Meier, Irene B.
Mayeux, Richard
Brickman, Adam M.
author_facet Chesebro, Anthony G.
Amarante, Erica
Lao, Patrick J.
Meier, Irene B.
Mayeux, Richard
Brickman, Adam M.
author_sort Chesebro, Anthony G.
collection PubMed
description Cerebral microbleeds, observed as small, spherical hypointense regions on gradient echo (GRE) or susceptibility weighted (SWI) magnetic resonance imaging (MRI) sequences, reflect small hemorrhagic infarcts, and are associated with conditions such as vascular dementia, small vessel disease, cerebral amyloid angiopathy, and Alzheimer’s disease. The current gold standard for detecting and rating cerebral microbleeds in a research context is visual inspection by trained raters, a process that is both time consuming and subject to poor reliability. We present here a novel method to automate microbleed detection on GRE and SWI images. We demonstrate in a community-based cohort of older adults that the method is highly sensitive (greater than 92% of all microbleeds accurately detected) across both modalities, with reasonable precision (fewer than 20 and 10 false positives per scan on GRE and SWI, respectively). We also demonstrate that the algorithm can be used to identify microbleeds over longitudinal scans with a higher level of sensitivity than visual ratings (50% of longitudinal microbleeds correctly labeled by the algorithm, while manual ratings was 30% or lower). Further, the algorithm identifies the anatomical localization of microbleeds based on brain atlases, and greatly reduces time spent completing visual ratings (43% reduction in visual rating time). Our automatic microbleed detection instrument is ideal for implementation in large-scale studies that include cross-sectional and longitudinal scanning, as well as being capable of performing well across multiple commonly used MRI modalities.
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spelling pubmed-78898612021-02-22 Automated detection of cerebral microbleeds on T2*-weighted MRI Chesebro, Anthony G. Amarante, Erica Lao, Patrick J. Meier, Irene B. Mayeux, Richard Brickman, Adam M. Sci Rep Article Cerebral microbleeds, observed as small, spherical hypointense regions on gradient echo (GRE) or susceptibility weighted (SWI) magnetic resonance imaging (MRI) sequences, reflect small hemorrhagic infarcts, and are associated with conditions such as vascular dementia, small vessel disease, cerebral amyloid angiopathy, and Alzheimer’s disease. The current gold standard for detecting and rating cerebral microbleeds in a research context is visual inspection by trained raters, a process that is both time consuming and subject to poor reliability. We present here a novel method to automate microbleed detection on GRE and SWI images. We demonstrate in a community-based cohort of older adults that the method is highly sensitive (greater than 92% of all microbleeds accurately detected) across both modalities, with reasonable precision (fewer than 20 and 10 false positives per scan on GRE and SWI, respectively). We also demonstrate that the algorithm can be used to identify microbleeds over longitudinal scans with a higher level of sensitivity than visual ratings (50% of longitudinal microbleeds correctly labeled by the algorithm, while manual ratings was 30% or lower). Further, the algorithm identifies the anatomical localization of microbleeds based on brain atlases, and greatly reduces time spent completing visual ratings (43% reduction in visual rating time). Our automatic microbleed detection instrument is ideal for implementation in large-scale studies that include cross-sectional and longitudinal scanning, as well as being capable of performing well across multiple commonly used MRI modalities. Nature Publishing Group UK 2021-02-17 /pmc/articles/PMC7889861/ /pubmed/33597663 http://dx.doi.org/10.1038/s41598-021-83607-0 Text en © The Author(s) 2021 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/.
spellingShingle Article
Chesebro, Anthony G.
Amarante, Erica
Lao, Patrick J.
Meier, Irene B.
Mayeux, Richard
Brickman, Adam M.
Automated detection of cerebral microbleeds on T2*-weighted MRI
title Automated detection of cerebral microbleeds on T2*-weighted MRI
title_full Automated detection of cerebral microbleeds on T2*-weighted MRI
title_fullStr Automated detection of cerebral microbleeds on T2*-weighted MRI
title_full_unstemmed Automated detection of cerebral microbleeds on T2*-weighted MRI
title_short Automated detection of cerebral microbleeds on T2*-weighted MRI
title_sort automated detection of cerebral microbleeds on t2*-weighted mri
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7889861/
https://www.ncbi.nlm.nih.gov/pubmed/33597663
http://dx.doi.org/10.1038/s41598-021-83607-0
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