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Research ArticleORIGINAL RESEARCH

Predicting White Matter Hyperintensity: Leveraging Portable Magnetic Resonance Imaging 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 March 2025, ajnr.A8734; DOI: https://doi.org/10.3174/ajnr.A8734
Ian P. Johnson
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Hailey Brigger
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Joel Smith
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Emma Peasley
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Alison Champagne
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Lauren Littig
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Dheeraj Lalwani
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Gordon Sze
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Seyedmehdi Payabvash
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Basmah Safdar
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Gail D’Onofrio
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Charles Wira
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Juan Eugenio Iglesias
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Matthew S. Rosen
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Annabel Sorby-Adams
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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W. Taylor Kimberly
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Kevin N. Sheth
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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Adam de Havenon
From the Department of Neurology (I.P.J, H.B, J.S, E.P, A.C, L.L, D.L, K.N.S, A.D), Department of Radiology (G.S, S.P), and Department of Emergency Medicine (G.D, C.W), Yale School of Medicine, New Haven, CT, USA; Department of Neurology (A.S, W.T.K), Center for Genomic Medicine (A.S, W.T.K), and Athinoula A. Martinos Center for Biomedical Imaging (J.E.I, M.S.R), Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Centre for Medical Image Computing (J.E.I), University College London, London, UK; Computer Science and Artificial Intelligence Laboratory (J.E.I), Massachusetts Institute of Technology, Cambridge, MA, USA.
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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 0.064 T pMRI and quantified 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 AUC of 0.83 (95% CI 0.75-0.90) in the development cohort (N=143, 62.4±12.6 years, 44% female, 36% non-white race) and 0.85 (95% CI 0.77-0.92) in the validation cohort (N=127, 65.2±16.8 years, 51% female, 34% non-white race), with similar results using WMH measurements derived from cMRI (N=120, p=0.98, 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<0.001).

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 significant WMH burden. Appropriately targeted pMRI screening in high-risk individuals could allow providers and patients to proactively manage vascular risk factors and improve neurological outcomes.

ABBREVIATIONS: pMRI = portable magnetic resonance imaging; cMRI = conventional magnetic resonance imaging; WMH = white matter hyperintensity; hypertension = HTN; diabetes = DM; atrial fibrillation = AFib; systolic blood pressure = SBP; hyperlipidemia = HLD; area under the curve = AUC; receiver operating characteristic = ROC.

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Accepted Manuscript
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 Magnetic Resonance Imaging for Accessible Brain Health Screening
American Journal of Neuroradiology Mar 2025, ajnr.A8734; DOI: 10.3174/ajnr.A8734

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Accepted Manuscript
Predicting White Matter Hyperintensity: Leveraging Portable Magnetic Resonance Imaging 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, Adam de Havenon
American Journal of Neuroradiology Mar 2025, ajnr.A8734; DOI: 10.3174/ajnr.A8734
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