ABSTRACT Magnetic resonance imaging (MRI) is a cornerstone of brain and spine diagnostics. Yet, access across Africa is limited by high installation costs, power requirements, and the need for specialized shielding and facilities. Low-and ultra low-field (ULF) MRI systems operating below 0.3 T are emerging as a practical alternative to expand neuroimaging capacity in resource-constrained settings. However, its faced with challenges that hinder its use in clinical setting. Technological advances that seek to tackle these challenges such as permanent Halbach array magnets, portable scanner designs such as those successfully deployed in Uganda and Malawi, and deep learning methods including convolutional neural network electromagnetic interference cancellation and residual U-Net image reconstruction have improved image quality and reduced noise, making ULF MRI increasingly viable. We review the state of low-field MRI technology, its application in point-of-care and rural contexts, and the specific limitations that remain, including reduced signal-to-noise ratio, larger voxel size requirements, and susceptibility to motion artifacts. Although not a replacement for high-field scanners in detecting subtle or small lesions, low-field MRI offers a promising pathway to broaden diagnostic imaging availability, support clinical decision-making, and advance equitable neuroimaging research in under-resourced regions. ABBREVIATIONS: CNN=Convolutional neural network; EMI=Electromagnetic interference; FID=Free induction wave; LMIC=Low and middle income countries; MRI=Magnetic Resonance Imaging; NCDs=Non communicable diseases; RF=Radiofrequency Pulse; SNR= Signal to noise ratio; TBI=Traumatic brain Injury
Ussi et al. (Thu,) studied this question.