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Ula Briski

As seen in: Nature, BioRxiv
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Articles

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

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By Ula Briski, Niall Bourke, Kirsten A Donald, Layla E Bradford, Michal R. Zieff, Sadia Parkar, Sidra Kaleem, Sean C. L. Deoni, Steven Williams, Rosalyn J. Moran, Levente Baljer, František Váša, Hajer Karoui, Salman Osmani
| Nature Verified
Abstract Brain magnetic resonance imaging (MRI) is essential for diagnosis and neurodevelopmental research, but the high cost and infrastructure demands of high-field MRI limit its use to high-income settings. Ultra-low-field MRI scanners offer a more affordable and energy-efficient alternative, but their reduced resolution and signal-to-noise ratio restrict research and clinical utility, prompting the need for super-resolution techniques.
Open in Who Shared Issue with article?

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

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By Niall Bourke, Kirsten A Donald, Ula Briski, Layla E Bradford
| BioRxiv
Abstract 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.
Open in Who Shared Issue with article?
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