Abstract #1160

Synthetic Quantitative MRI through Relaxometry Modelling for Improved Brain Segmentation

Martina F Callaghan¹, Siawoosh Mohammadi^1,2, and Nikolaus Weiskopf^1,3

¹Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, United Kingdom, ²Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, ³Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

Here we exploit the inter-dependence of quantitative MRI (qMRI) parameters via relaxometry modelling to generate synthetic quantitative maps of magnetisation transfer saturation. The utility of the new concept of synthetic quantitative data is demonstrated by improving image segmentation of deep gray matter structures for neuroimaging applications.

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