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Research ArticleORIGINAL RESEARCH

Automated quantification of cerebral microbleeds in susceptibility-weighted MRI: association with vascular risk factors, white matter hyperintensity burden, and cognitive function

Ji Su Ko, Yangsean Choi, Eun Seon Jeong, Hyun-Jung Kim, Grace Yoojin Lee, Ji Eun Park, Namkug Kim and Ho Sung Kim
American Journal of Neuroradiology October 2024, ajnr.A8552; DOI: https://doi.org/10.3174/ajnr.A8552
Ji Su Ko
Department of Radiology and Research Institute of Radiology, (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.) University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea, and Department of Convergence Medicine (H.J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea.
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Yangsean Choi
Department of Radiology and Research Institute of Radiology, (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.) University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea, and Department of Convergence Medicine (H.J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea.
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Eun Seon Jeong
Department of Radiology and Research Institute of Radiology, (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.) University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea, and Department of Convergence Medicine (H.J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea.
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Hyun-Jung Kim
Department of Radiology and Research Institute of Radiology, (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.) University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea, and Department of Convergence Medicine (H.J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea.
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Grace Yoojin Lee
Department of Radiology and Research Institute of Radiology, (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.) University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea, and Department of Convergence Medicine (H.J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea.
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Ji Eun Park
Department of Radiology and Research Institute of Radiology, (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.) University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea, and Department of Convergence Medicine (H.J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea.
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Namkug Kim
Department of Radiology and Research Institute of Radiology, (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.) University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea, and Department of Convergence Medicine (H.J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea.
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Ho Sung Kim
Department of Radiology and Research Institute of Radiology, (J.S.K., Y.C., E.S.J., J.E.P., H.S.K.) University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea, and Department of Convergence Medicine (H.J.K., G.Y.L., N.K.), University of Ulsan College of Medicine, Asan Medical Centre, Seoul, Republic of Korea.
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ABSTRACT

BACKGROUND AND PURPOSE: To train and validate a deep learning (DL)-based segmentation model for cerebral microbleeds (CMB) on susceptibility-weighted MRI; and to find associations between CMB, cognitive impairment, and vascular risk factors.

MATERIALS AND METHODS: Participants in this single-institution retrospective study underwent brain MRI to evaluate cognitive impairment between January–September 2023. For training the DL model, the nnU-Net framework was used without modifications. The DL model’s performance was evaluated on independent internal and external validation datasets. Linear regression analysis was used to find associations between log-transformed CMB numbers, cognitive function (mini-mental status examination [MMSE]), white matter hyperintensity (WMH) burden, and clinical vascular risk factors (age, sex, hypertension, diabetes, lipid profiles, and body mass index).

RESULTS: Training of the DL model (n = 287) resulted in a robust segmentation performance with an average dice score of 0.73 (95% CI, 0.67–0.79) in an internal validation set, (n = 67) and modest performance in an external validation set (dice score = 0.46, 95% CI, 0.33–0.59, n = 68). In a temporally independent clinical dataset (n = 448), older age, hypertension, and WMH burden were significantly associated with CMB numbers in all distributions (total, lobar, deep, and cerebellar; all P <.01). MMSE was significantly associated with hyperlipidemia (β = 1.88, 95% CI, 0.96–2.81, P <.001), WMH burden (β = -0.17 per 1% WMH burden, 95% CI, -0.27-0.08, P <.001), and total CMB number (β = -0.01 per 1 CMB, 95% CI, -0.02-0.001, P = .04) after adjusting for age and sex.

CONCLUSIONS: The DL model showed a robust segmentation performance for CMB. In all distributions, CMB had significant positive associations with WMH burden. Increased WMH burden and CMB numbers were associated with decreased cognitive function.

ABBREVIATIONS: CMB = cerebral microbleed; DL = deep learning, DSC = dice similarity coefficient; MMSE = mini-mental status examination; SVD = small vessel disease; SWI = susceptibility-weighted image; WMH = white matter hyperintensity.

Footnotes

  • The authors declare no conflicts of interest related to the content of this article

  • © 2024 by American Journal of Neuroradiology

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Accepted Manuscript
Ji Su Ko, Yangsean Choi, Eun Seon Jeong, Hyun-Jung Kim, Grace Yoojin Lee, Ji Eun Park, Namkug Kim, Ho Sung Kim
Automated quantification of cerebral microbleeds in susceptibility-weighted MRI: association with vascular risk factors, white matter hyperintensity burden, and cognitive function
American Journal of Neuroradiology Oct 2024, ajnr.A8552; DOI: 10.3174/ajnr.A8552

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Accepted Manuscript
Automated quantification of cerebral microbleeds in susceptibility-weighted MRI: association with vascular risk factors, white matter hyperintensity burden, and cognitive function
Ji Su Ko, Yangsean Choi, Eun Seon Jeong, Hyun-Jung Kim, Grace Yoojin Lee, Ji Eun Park, Namkug Kim, Ho Sung Kim
American Journal of Neuroradiology Oct 2024, ajnr.A8552; DOI: 10.3174/ajnr.A8552
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