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Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease

Author

Listed:
  • Annabel J. Sorby-Adams

    (Massachusetts General Hospital and Harvard Medical School
    Massachusetts General Hospital)

  • Jennifer Guo

    (Massachusetts General Hospital and Harvard Medical School
    Massachusetts General Hospital)

  • Pablo Laso

    (Massachusetts General Hospital and Harvard Medical School)

  • John E. Kirsch

    (Massachusetts General Hospital and Harvard Medical School)

  • Julia Zabinska

    (Yale New Haven Hospital and Yale School of Medicine)

  • Ana-Lucia Garcia Guarniz

    (Massachusetts General Hospital and Harvard Medical School)

  • Pamela W. Schaefer

    (Massachusetts General Hospital and Harvard Medical School)

  • Seyedmehdi Payabvash

    (Yale New Haven Hospital and Yale University School of Medicine)

  • Adam Havenon

    (Yale New Haven Hospital and Yale School of Medicine)

  • Matthew S. Rosen

    (Massachusetts General Hospital and Harvard Medical School)

  • Kevin N. Sheth

    (Yale New Haven Hospital and Yale School of Medicine)

  • Teresa Gomez-Isla

    (Massachusetts General Hospital and Harvard Medical School)

  • J. Eugenio Iglesias

    (Massachusetts General Hospital and Harvard Medical School)

  • W. Taylor Kimberly

    (Massachusetts General Hospital and Harvard Medical School
    Massachusetts General Hospital)

Abstract

Portable, low-field magnetic resonance imaging (LF-MRI) of the brain may facilitate point-of-care assessment of patients with Alzheimer’s disease (AD) in settings where conventional MRI cannot. However, image quality is limited by a lower signal-to-noise ratio. Here, we optimize LF-MRI acquisition and develop a freely available machine learning pipeline to quantify brain morphometry and white matter hyperintensities (WMH). We validate the pipeline and apply it to outpatients presenting with mild cognitive impairment or dementia due to AD. We find hippocampal volumes from ≤ 3 mm isotropic LF-MRI scans have agreement with conventional MRI and are more accurate than anisotropic counterparts. We also show WMH volume has agreement between manual segmentation and the automated pipeline. The increased availability and reduced cost of LF-MRI, in combination with our machine learning pipeline, has the potential to increase access to neuroimaging for dementia.

Suggested Citation

  • Annabel J. Sorby-Adams & Jennifer Guo & Pablo Laso & John E. Kirsch & Julia Zabinska & Ana-Lucia Garcia Guarniz & Pamela W. Schaefer & Seyedmehdi Payabvash & Adam Havenon & Matthew S. Rosen & Kevin N., 2024. "Portable, low-field magnetic resonance imaging for evaluation of Alzheimer’s disease," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54972-x
    DOI: 10.1038/s41467-024-54972-x
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    References listed on IDEAS

    as
    1. Mercy H. Mazurek & Bradley A. Cahn & Matthew M. Yuen & Anjali M. Prabhat & Isha R. Chavva & Jill T. Shah & Anna L. Crawford & E. Brian Welch & Jonathan Rothberg & Laura Sacolick & Michael Poole & Char, 2021. "Portable, bedside, low-field magnetic resonance imaging for evaluation of intracerebral hemorrhage," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Martijn J Mulder & Max C Keuken & Pierre-Louis Bazin & Anneke Alkemade & Birte U Forstmann, 2019. "Size and shape matter: The impact of voxel geometry on the identification of small nuclei," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-19, April.
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