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A low-cost and shielding-free ultra-low-field brain MRI scanner

Author

Listed:
  • Yilong Liu

    (The University of Hong Kong
    The University of Hong Kong)

  • Alex T. L. Leong

    (The University of Hong Kong
    The University of Hong Kong)

  • Yujiao Zhao

    (The University of Hong Kong
    The University of Hong Kong)

  • Linfang Xiao

    (The University of Hong Kong
    The University of Hong Kong)

  • Henry K. F. Mak

    (The University of Hong Kong)

  • Anderson Chun On Tsang

    (The University of Hong Kong)

  • Gary K. K. Lau

    (The University of Hong Kong)

  • Gilberto K. K. Leung

    (The University of Hong Kong)

  • Ed X. Wu

    (The University of Hong Kong
    The University of Hong Kong
    The University of Hong Kong)

Abstract

Magnetic resonance imaging is a key diagnostic tool in modern healthcare, yet it can be cost-prohibitive given the high installation, maintenance and operation costs of the machinery. There are approximately seven scanners per million inhabitants and over 90% are concentrated in high-income countries. We describe an ultra-low-field brain MRI scanner that operates using a standard AC power outlet and is low cost to build. Using a permanent 0.055 Tesla Samarium-cobalt magnet and deep learning for cancellation of electromagnetic interference, it requires neither magnetic nor radiofrequency shielding cages. The scanner is compact, mobile, and acoustically quiet during scanning. We implement four standard clinical neuroimaging protocols (T1- and T2-weighted, fluid-attenuated inversion recovery like, and diffusion-weighted imaging) on this system, and demonstrate preliminary feasibility in diagnosing brain tumor and stroke. Such technology has the potential to meet clinical needs at point of care or in low and middle income countries.

Suggested Citation

  • Yilong Liu & Alex T. L. Leong & Yujiao Zhao & Linfang Xiao & Henry K. F. Mak & Anderson Chun On Tsang & Gary K. K. Lau & Gilberto K. K. Leung & Ed X. Wu, 2021. "A low-cost and shielding-free ultra-low-field brain MRI scanner," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27317-1
    DOI: 10.1038/s41467-021-27317-1
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    References listed on IDEAS

    as
    1. Bo Zhu & Jeremiah Z. Liu & Stephen F. Cauley & Bruce R. Rosen & Matthew S. Rosen, 2018. "Image reconstruction by domain-transform manifold learning," Nature, Nature, vol. 555(7697), pages 487-492, March.
    2. Julian Nowogrodzki, 2018. "The world’s strongest MRI machines are pushing human imaging to new limits," Nature, Nature, vol. 563(7729), pages 24-26, November.
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