Automated detection of cerebral microbleeds on T2*-weighted MRI

Abstract 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,...

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Autores principales:	Anthony G. Chesebro, Erica Amarante, Patrick J. Lao, Irene B. Meier, Richard Mayeux, Adam M. Brickman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f45d604bb84c40efbea746f700a1250c
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spelling	oai:doaj.org-article:f45d604bb84c40efbea746f700a1250c2021-12-02T10:54:14ZAutomated detection of cerebral microbleeds on T2*-weighted MRI10.1038/s41598-021-83607-02045-2322https://doaj.org/article/f45d604bb84c40efbea746f700a1250c2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83607-0https://doaj.org/toc/2045-2322Abstract 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.Anthony G. ChesebroErica AmarantePatrick J. LaoIrene B. MeierRichard MayeuxAdam M. BrickmanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anthony G. Chesebro Erica Amarante Patrick J. Lao Irene B. Meier Richard Mayeux Adam M. Brickman Automated detection of cerebral microbleeds on T2*-weighted MRI
description	Abstract 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.
format	article
author	Anthony G. Chesebro Erica Amarante Patrick J. Lao Irene B. Meier Richard Mayeux Adam M. Brickman
author_facet	Anthony G. Chesebro Erica Amarante Patrick J. Lao Irene B. Meier Richard Mayeux Adam M. Brickman
author_sort	Anthony G. Chesebro
title	Automated detection of cerebral microbleeds on T2*-weighted MRI
title_short	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_sort	automated detection of cerebral microbleeds on t2*-weighted mri
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f45d604bb84c40efbea746f700a1250c
work_keys_str_mv	AT anthonygchesebro automateddetectionofcerebralmicrobleedsont2weightedmri AT ericaamarante automateddetectionofcerebralmicrobleedsont2weightedmri AT patrickjlao automateddetectionofcerebralmicrobleedsont2weightedmri AT irenebmeier automateddetectionofcerebralmicrobleedsont2weightedmri AT richardmayeux automateddetectionofcerebralmicrobleedsont2weightedmri AT adammbrickman automateddetectionofcerebralmicrobleedsont2weightedmri
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Automated detection of cerebral microbleeds on T2*-weighted MRI

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