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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.
A low cost MRI scanner may have the potential to meet clinical needs at point of care or in low and middle income countries. Here the authors describe a low cost 0.055 Tesla MRI scanner that operates using a standard AC power outlet, and demonstrate its preliminary feasibility in diagnosing brain tumor and stroke.
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Details
; Leong Alex T L 1 ; Zhao Yujiao 1 ; Xiao Linfang 1 ; Mak Henry K F 2
; Tsang Anderson Chun On 3
; Lau Gary K K 4 ; Leung Gilberto K K 3 ; Wu, Ed X 5
1 The University of Hong Kong, Laboratory of Biomedical Imaging and Signal Processing, Pokfulam, China (GRID:grid.194645.b) (ISNI:0000000121742757); The University of Hong Kong, Department of Electrical and Electronic Engineering, Pokfulam, China (GRID:grid.194645.b) (ISNI:0000000121742757)
2 The University of Hong Kong, Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, Pokfulam, China (GRID:grid.194645.b) (ISNI:0000000121742757)
3 The University of Hong Kong, Department of Surgery, Li Ka Shing Faculty of Medicine, Pokfulam, China (GRID:grid.194645.b) (ISNI:0000000121742757)
4 The University of Hong Kong, Division of Neurology, Department of Medicine, Li Ka Shing Faculty of Medicine, Pokfulam, China (GRID:grid.194645.b) (ISNI:0000000121742757)
5 The University of Hong Kong, Laboratory of Biomedical Imaging and Signal Processing, Pokfulam, China (GRID:grid.194645.b) (ISNI:0000000121742757); The University of Hong Kong, Department of Electrical and Electronic Engineering, Pokfulam, China (GRID:grid.194645.b) (ISNI:0000000121742757); The University of Hong Kong, School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, Pokfulam, China (GRID:grid.194645.b) (ISNI:0000000121742757)




