Abstract #0235

ZEBRA – from rich-multi-dimensional data to anatomical profiles

Jana Hutter^1,2, Jonathan O'Muircheartaigh^1,2, Paddy Slator³, Daan Christiaens^1,2, Sophie Arulkumaran², Lucilio Cordero Grande^1,2, Rui Pedro A G Teixeira¹, Anthony N Price^1,2, J-Donald Tournier^1,2, and Joseph V Hajnal^1,2

¹Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ²Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, ³University College London, London, United Kingdom

An efficient joint multi-parametric diffusion-relaxometry MRI acquisition, ZEBRA, is presented. Improvements to optimize the joint sampling in several dimensions include logarithmic TI sampling, superblock strategies and globally and locally optimized gradient schemes. These are introduced together with a proposed whole-brain protocol (resolution 2.5mm isotropic). The data is analysed by an assumption free clustering step – designed to extract tissue information and anatomical profiles directly from the signal. Depiction of several clusters – including the deep grey matter and cerebellar substructures - illustrate the richness of the obtained data.

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