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✅   Deep learning super-resolution of paediatric ultra-low-field MRI without paired high-field scans

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Brain magnetic resonance imaging (MRI) is essential for diagnosis and neurodevelopmental research, but the high cost and infrastructure demands of high-field MRI scanners restrict their use to high-income settings. To address this, more affordable and energy-efficient ultra-low-field MRI scanners have been developed. However, the reduced resolution and signal-to-noise ratio of the resulting scans limit their clinical utility, motivating the development of super-resolution techniques. The current state-of-the-art super-resolution methods require either three anisotropic ultra-low-field scans acquired at different orientations (axial, coronal, sagittal) to reconstruct a higher-resolution image using multi-resolution registration (MRR), or the training of deep learning super-resolution models using paired ultra-low- and high-field scans.

BioRxiv     Dec 01, 2024