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Abstract #5030

Synthetising myelin water fraction from T1-weighted and T2-weighted data: an image-to-image translation approach

Matteo Mancini1, Carolyn McNabb1, Derek Jones1, and Mara Cercignani1
1Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom

Synopsis

Keywords: Machine Learning/Artificial Intelligence, BrainMyelin biomarkers are a fundamental tool for both neuroscience research and clinical applications. Despite several quantitative MRI methods available for their estimation, in several cases qualitative approaches are the only viable solution. To get the best of both the quantitative and qualitative worlds, here we propose an image-to-image translation method to learn the mapping between common routine scans and a quantitative myelin metric. To achieve this goal, we trained a generative adversarial network on a relatively large dataset of healthy subjects. Both the qualitative and quantitative results show good agreement between the predicted and the ground-truth maps.

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Keywords