Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging

Igwe, Kay C.; Lao, Patrick J.; Vorburger, Robert S.; Banerjee, Arit; Rivera, Andres; Chesebro, Anthony; Laing, Krystal; Manly, Jennifer J.; Brickman, Adam M.

doi:10.1016/j.mri.2021.10.007

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23357

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DC Field	Value	Language
dc.contributor.author	Igwe, Kay C.	-
dc.contributor.author	Lao, Patrick J.	-
dc.contributor.author	Vorburger, Robert S.	-
dc.contributor.author	Banerjee, Arit	-
dc.contributor.author	Rivera, Andres	-
dc.contributor.author	Chesebro, Anthony	-
dc.contributor.author	Laing, Krystal	-
dc.contributor.author	Manly, Jennifer J.	-
dc.contributor.author	Brickman, Adam M.	-
dc.date.accessioned	2021-10-30T12:33:20Z	-
dc.date.available	2021-10-30T12:33:20Z	-
dc.date.issued	2021-10	-
dc.identifier.issn	0730-725X	de_CH
dc.identifier.issn	1873-5894	de_CH
dc.identifier.uri	https://digitalcollection.zhaw.ch/handle/11475/23357	-
dc.description	https://pubmed.ncbi.nlm.nih.gov/34662699/	de_CH
dc.description.abstract	White matter hyperintensities (WMH) are areas of increased signal visualized on T2-weighted fluid attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) sequences. They are typically attributed to small vessel cerebrovascular disease in the context of aging. Among older adults, WMH are associated with risk of cognitive decline and dementia, stroke, and various other health outcomes. There has been increasing interest in incorporating quantitative WMH measurement as outcomes in clinical trials, observational research, and clinical settings. Here, we present a novel, fully automated, unsupervised detection algorithm for WMH segmentation and quantification. The algorithm uses a robust preprocessing pipeline, including brain extraction and a sample-specific mask that incorporates spatial information for automatic false positive reduction, and a half Gaussian mixture model (HGMM). The method was evaluated in 24 participants with varying degrees of WMH (4.9-78.6 cm3) from a community-based study of aging and dementia with dice coefficient, sensitivity, specificity, correlation, and bias relative to the ground truth manual segmentation approach performed by two expert raters. Results were compared with those derived from commonly used available WMH segmentation packages, including SPM lesion probability algorithm (LPA), SPM lesion growing algorithm (LGA), and Brain Intensity AbNormality Classification Algorithm (BIANCA). The HGMM algorithm derived WMH values that had a dice score of 0.87, sensitivity of 0.89, and specificity of 0.99 compared to ground truth. White matter hyperintensity volumes derived with HGMM were strongly correlated with ground truth values (r = 0.97, p = 3.9e-16), with no observable bias (-1.1 [-2.6, 0.44], p-value = 0.16). Our novel algorithm uniquely uses a robust preprocessing pipeline and a half-Gaussian mixture model to segment WMH with high agreement with ground truth for large scale studies of brain aging.	de_CH
dc.language.iso	en	de_CH
dc.publisher	Elsevier	de_CH
dc.relation.ispartof	Magnetic Resonance Imaging	de_CH
dc.rights	http://creativecommons.org/licenses/by-nc-nd/4.0/	de_CH
dc.subject	Automated segmentation	de_CH
dc.subject	Half Gaussian mixture model	de_CH
dc.subject	Mixture model	de_CH
dc.subject	Small vessel cerebrovascular disease	de_CH
dc.subject	White matter hyperintensity	de_CH
dc.subject.ddc	616.8: Neurologie und Krankheiten des Nervensystems	de_CH
dc.title	Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging	de_CH
dc.type	Beitrag in wissenschaftlicher Zeitschrift	de_CH
dcterms.type	Text	de_CH
zhaw.departement	Life Sciences und Facility Management	de_CH
zhaw.organisationalunit	Institut für Computational Life Sciences (ICLS)	de_CH
dc.identifier.doi	10.1016/j.mri.2021.10.007	de_CH
dc.identifier.doi	10.21256/zhaw-23357	-
dc.identifier.pmid	34662699	de_CH
zhaw.funding.eu	No	de_CH
zhaw.originated.zhaw	Yes	de_CH
zhaw.pages.end	79	de_CH
zhaw.pages.start	71	de_CH
zhaw.publication.status	acceptedVersion	de_CH
zhaw.volume	85	de_CH
zhaw.embargo.end	2022-10-16	de_CH
zhaw.publication.review	Peer review (Publikation)	de_CH
zhaw.webfeed	Data Management & Visualisation	de_CH
zhaw.author.additional	No	de_CH
zhaw.display.portrait	Yes	de_CH
Appears in collections:	Publikationen Life Sciences und Facility Management

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Igwe, K. C., Lao, P. J., Vorburger, R. S., Banerjee, A., Rivera, A., Chesebro, A., Laing, K., Manly, J. J., & Brickman, A. M. (2021). Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging. Magnetic Resonance Imaging, 85, 71–79. https://doi.org/10.1016/j.mri.2021.10.007

Igwe, K.C. et al. (2021) ‘Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging’, Magnetic Resonance Imaging, 85, pp. 71–79. Available at: https://doi.org/10.1016/j.mri.2021.10.007.

K. C. Igwe et al., “Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging,” Magnetic Resonance Imaging, vol. 85, pp. 71–79, Oct. 2021, doi: 10.1016/j.mri.2021.10.007.

IGWE, Kay C., Patrick J. LAO, Robert S. VORBURGER, Arit BANERJEE, Andres RIVERA, Anthony CHESEBRO, Krystal LAING, Jennifer J. MANLY und Adam M. BRICKMAN, 2021. Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging. Magnetic Resonance Imaging. Oktober 2021. Bd. 85, S. 71–79. DOI 10.1016/j.mri.2021.10.007

Igwe, Kay C., Patrick J. Lao, Robert S. Vorburger, Arit Banerjee, Andres Rivera, Anthony Chesebro, Krystal Laing, Jennifer J. Manly, and Adam M. Brickman. 2021. “Automatic Quantification of White Matter Hyperintensities on T2-Weighted Fluid Attenuated Inversion Recovery Magnetic Resonance Imaging.” Magnetic Resonance Imaging 85 (October): 71–79. https://doi.org/10.1016/j.mri.2021.10.007.

Igwe, Kay C., et al. “Automatic Quantification of White Matter Hyperintensities on T2-Weighted Fluid Attenuated Inversion Recovery Magnetic Resonance Imaging.” Magnetic Resonance Imaging, vol. 85, Oct. 2021, pp. 71–79, https://doi.org/10.1016/j.mri.2021.10.007.