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Improved Turnaround Times | Median time to first decision: 12 days

Research ArticleNeurovascular/Stroke Imaging

Predicting White Matter Hyperintensity: Leveraging Portable MRI for Accessible Brain Health Screening

Ian P. Johnson, Hailey Brigger, Joel Smith, Emma Peasley, Alison Champagne, Lauren Littig, Dheeraj Lalwani, Gordon Sze, Seyedmehdi Payabvash, Basmah Safdar, Gail D’Onofrio, Charles Wira, Juan Eugenio Iglesias, Matthew S. Rosen, Annabel Sorby-Adams, W. Taylor Kimberly, Kevin N. Sheth and Adam de Havenon
American Journal of Neuroradiology September 2025, 46 (9) 1786-1792; DOI: https://doi.org/10.3174/ajnr.A8734
Ian P. Johnson
aFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Hailey Brigger
aFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Joel Smith
aFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Emma Peasley
aFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Alison Champagne
aFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Lauren Littig
aFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Dheeraj Lalwani
aFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Gordon Sze
bDepartment of Radiology (G.S., S.P.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Seyedmehdi Payabvash
bDepartment of Radiology (G.S., S.P.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Basmah Safdar
cEmergency Medicine (B.S., G.D., C.W.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Gail D’Onofrio
cEmergency Medicine (B.S., G.D., C.W.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Charles Wira
cEmergency Medicine (B.S., G.D., C.W.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Juan Eugenio Iglesias
dAthinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
eCentre for Medical Image Computing (J.E.I.), University College London, London, UK
fComputer Science and Artificial Intelligence Laboratory (J.E.I.), Massachusetts Institute of Technology, Cambridge, Massachusetts
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Matthew S. Rosen
dAthinoula A. Martinos Center for Biomedical Imaging (J.E.I., M.S.R.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
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Annabel Sorby-Adams
gDepartment of Neurology (A.S.-A., W.T.K.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
hCenter for Genomic Medicine (A.S.-A., W.T.K.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
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W. Taylor Kimberly
gDepartment of Neurology (A.S.-A., W.T.K.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
hCenter for Genomic Medicine (A.S.-A., W.T.K.), Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
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Kevin N. Sheth
aFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Adam de Havenon
aFrom the Department of Neurology (I.P.J., H.B., J.S., E.P., A.C., L.L., D.L., K.N.S., A.d.H.), Yale New Haven Hospital and Yale School of Medicine, New Haven, Connecticut
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Abstract

BACKGROUND AND PURPOSE: Portable MRI (pMRI) has emerged as a cost-effective and accessible tool for the identification of white matter hyperintensities (WMH), an independent risk factor for stroke and dementia. Our objective was to confirm that pMRI can produce accurate WMH measurements and to develop and validate a risk model to predict WMH on pMRI for the purpose of identifying patients who may benefit from pMRI screening.

MATERIALS AND METHODS: The development (n = 143) and validation (n = 127) cohorts included patients without acute neurologic pathology who received a pMRI at a tertiary care hospital between May 2020 and July 2024. The development cohort included pMRIs collected as part of a prospective WMH screening pilot program in the emergency department. The validation cohort was a retrospective collection of pMRIs obtained for separate research purposes. Conventional MRIs (cMRIs) in the validation cohort obtained within 3 months of pMRIs were used for additional validation and device agreement. The primary outcome was WMH burden greater than 10 mL, assessed via an axial T2-FLAIR sequence acquired on a 0.064T pMRI and quantified by using a WMH segmentation software developed to process sequences of any resolution. We used backwards selection to screen candidate variables and report the area under the curve of the resulting model.

RESULTS: The final model, which included age, systolic blood pressure >140, atrial fibrillation, and tobacco use, achieved an area under the curve (AUC) of 0.83 (95% CI, 0.75–0.90) in the development cohort (n = 143, 62.4 ± 12.6 years, 44% female, 36% nonwhite race) and 0.85 (95% CI, 0.77–0.92) in the validation cohort (n = 127, 65.2 ± 16.8 years, 51% female, 34% nonwhite race), with similar results by using WMH measurements derived from cMRI (n = 120, P = .98, AUC = 0.86, 95% CI, 0.77–0.93). Additionally, we confirmed agreement in WMH volumes between pMRI and cMRI (n = 120, r = 0.93, 95% CI, 0.90–0.95, P < .001).

CONCLUSIONS: The WMH risk score demonstrated accurate performance and reproducibility across cohorts, supporting its potential as a screening tool for identifying patients at risk of moderate WMH burden. Appropriately targeted pMRI screening in high-risk individuals could allow providers and patients to proactively manage vascular risk factors and improve neurologic outcomes.

ABBREVIATIONS:

AFib
atrial fibrillation
AUC
area under the curve
cMRI
conventional MRI
DM
diabetes mellitus
HLD
hyperlipidemia
HTN
hypertension
IQR
interquartile range
pMRI
portable MRI
ROC
receiver operating characteristic
SBP
systolic blood pressure
WMH
white matter hyperintensities
  • © 2025 by American Journal of Neuroradiology
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American Journal of Neuroradiology: 46 (9)
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Ian P. Johnson, Hailey Brigger, Joel Smith, Emma Peasley, Alison Champagne, Lauren Littig, Dheeraj Lalwani, Gordon Sze, Seyedmehdi Payabvash, Basmah Safdar, Gail D’Onofrio, Charles Wira, Juan Eugenio Iglesias, Matthew S. Rosen, Annabel Sorby-Adams, W. Taylor Kimberly, Kevin N. Sheth, Adam de Havenon
Predicting White Matter Hyperintensity: Leveraging Portable MRI for Accessible Brain Health Screening
American Journal of Neuroradiology Sep 2025, 46 (9) 1786-1792; DOI: 10.3174/ajnr.A8734

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Predicting White Matter Hyperintensity Risk
Ian P. Johnson, Hailey Brigger, Joel Smith, Emma Peasley, Alison Champagne, Lauren Littig, Dheeraj Lalwani, Gordon Sze, Seyedmehdi Payabvash, Basmah Safdar, Gail D’Onofrio, Charles Wira, Juan Eugenio Iglesias, Matthew S. Rosen, Annabel Sorby-Adams, W. Taylor Kimberly, Kevin N. Sheth, Adam de Havenon
American Journal of Neuroradiology Sep 2025, 46 (9) 1786-1792; DOI: 10.3174/ajnr.A8734
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