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Abstract #1974

Mapping human brain development at new spatial resolutions using deep learning and high-resolution quantitative MRI

Georgia Doumou1,2, Hongxiang Lin2,3, Sara Lorio1, Lenka Vaculčiaková4, Kerrin J. Pine4, Nikolaus Weiskopf4,5, Jonathan O'Muircheartaigh6,7,8, Daniel C. Alexander2, and David W. Carmichael 1
1Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, 3Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China, 4Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 5Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany, 6Department of Forensic & Neurodevelopmental Sciences, King's College London, London, United Kingdom, 7Centre for the Developing Brain, Department of Perinatal Imaging and Health, King's College London, London, United Kingdom, 8MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom


High-resolution quantitative MRI using ultra-high field scanners (7T) could advance a range of research and clinical applications if limitations, both practical (e.g. long acquisitions) and technical (e.g. B1 non-uniformity), can be avoided. This could be achieved by using 7T information to enhance conventional field strength images. To test this approach, paired 3T-7T R1 maps were used to train a U-Net variant to enhance 3T R1 maps. Leave-one-out cross-validation, quantitative evaluation, as well as external validation on an external clinical dataset, demonstrated promising enhancement with visual and quantitative metrics more similar to 7T R1 maps than the 3T equivalents.

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