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Nature Reviews Bioengineering
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Brain imaging with portable low-field MRI

Nature Reviews Bioengineeringvolume 1, pages617–630 (2023)Cite this article

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APublisher Correction to this article was published on 04 August 2023

This article has beenupdated

Abstract

The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging. Low power requirements and transportability have enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies. Maximizing the information extracted from the reduced signal-to-noise ratio of LF-MRI is crucial to developing clinically useful diagnostic images. Progress in electromagnetic noise cancellation and machine learning reconstruction algorithms from sparsek-space data as well as new approaches to image enhancement have now enabled these advancements. Coupling technological innovation with bedside imaging creates new prospects in visualizing the healthy brain and detecting acute and chronic pathological changes. Ongoing development of hardware, improvements in pulse sequences and image reconstruction, and validation of clinical utility will continue to accelerate this field. As further innovation occurs, portable LF-MRI will facilitate the democratization of MRI and create new applications not previously feasible with conventional systems.

Key points

  • Portable, low-field MRI (LF-MRI) has enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies.

  • Advancements in electromagnetic noise cancellation and machine learning reconstruction algorithms as well as new approaches to image enhancement seek to maximize the information extracted from the reduced signal-to-noise ratio of LF-MRI.

  • The reduced fringe field and the transportability of LF-MR have expanded the imaging capacity for neurological conditions such as stroke, intracerebral haemorrhage, cardiac arrest, hydrocephalus and multiple sclerosis.

  • Hardware developments, improvements in pulse sequences and image reconstruction, and validation of clinical utility across a range of environments will continue to accelerate LF-MRI into the future.

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Fig. 1: Types of MRI geometries for portable LF-MRI.
Fig. 2: Examples of images acquired on LF-MRI compared with conventional HF-MRI.
Fig. 3: The evolution of LF-MR neuroimaging.

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ArticleOpen access05 April 2022

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Acknowledgements

W.T.K., J.E.I., M.S.R. and K.N.S. are funded by a National Institute of Biomedical Imaging and Bioengineering R01 (EB031114-01A1). A.J.S.-A. is funded by the Fulbright Commission. A.G.W. is supported by an ERC Advanced Grant (101021218, PASMAR), an NWO-Open Technology grant (18981), and an NWO Stevin Prijs. E.X.W. is supported by Hong Kong Research Grant Council (R7003-19, HKU17112120, HKU17127121 and HKU17127022) and Lam Woo Foundation. F.X.S work is supported by NIH/NIMH grant RF1MH123698 on “Highly Portable and Cloud-Enabled Neuroimaging Research: Confronting Ethics in Field Research with New Populations.” The content is solely the responsibility of the authors and does not necessarily represent the official views of NIMH or NIH. S.J.S. is supported by NIH Director’s Transformative Award (1R01AI145057), and NIH grants (2R01HD085853-07, 1R01HD096693-01, 1U01NS107486 and 1UG3NS123307). J.E.I. is funded by Alzheimer’s Research UK (ARUK-IRG2019A-003), NIH BRAIN Initiative (1RF1MH123195, 1UM1MH130981) and NIH grant (1R01AG07098). M.S.R. acknowledges the gracious support of the Kiyomi and Ed Baird MGH Research Scholar Award. All other co-authors report no relevant disclosures or funding.

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Authors and Affiliations

  1. Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    W. Taylor Kimberly & Annabel J. Sorby-Adams

  2. Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

    Andrew G. Webb

  3. Laboratory of Biomedical Imaging and Signal Processing, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China

    Ed X. Wu

  4. Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, Yale Center for Brain & Mind Health, New Haven, CT, USA

    Rachel Beekman & Kevin N. Sheth

  5. Departments of Neurosurgery and Neurology, McGovern Medical School, University of Texas Health Neurosciences, Houston, TX, USA

    Ritvij Bowry

  6. Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA

    Steven J. Schiff

  7. Division of Vascular Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT, USA

    Adam de Havenon

  8. Harvard Medical School Center for Bioethics, Harvard law School, Boston, MA, USA

    Francis X. Shen

  9. Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA

    Francis X. Shen

  10. Department of Radiology, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT, USA

    Gordon Sze

  11. Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Pamela Schaefer

  12. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Juan Eugenio Iglesias & Matthew S. Rosen

  13. Centre for Medical Image Computing, University College London, London, UK

    Juan Eugenio Iglesias

  14. Computer Science and AI Laboratory, Massachusetts Institute of Technology, Boston, MA, USA

    Juan Eugenio Iglesias

Authors
  1. W. Taylor Kimberly

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  2. Annabel J. Sorby-Adams

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  3. Andrew G. Webb

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  4. Ed X. Wu

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  5. Rachel Beekman

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  6. Ritvij Bowry

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  7. Steven J. Schiff

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  8. Adam de Havenon

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  11. Pamela Schaefer

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  12. Juan Eugenio Iglesias

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  13. Matthew S. Rosen

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  14. Kevin N. Sheth

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All authors contributed to the planning, drafting and editing of this Review.

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Correspondence toW. Taylor Kimberly orKevin N. Sheth.

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Competing interests

M.S.R. is a founder and equity holder of Hyperfine Inc. W.T.K. and K.N.S. have sponsored research agreements between their respective institutions and Hyperfine Inc. All other co-authors declare no conflict of interest.

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Kimberly, W.T., Sorby-Adams, A.J., Webb, A.G.et al. Brain imaging with portable low-field MRI.Nat Rev Bioeng1, 617–630 (2023). https://doi.org/10.1038/s44222-023-00086-w

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