Abstract #0090

Capturing clinical MRI complexity: a first step towards realizing the maximum research value of neuroradiological MRI.

Marzena Wylezinska-Arridge¹, Mark J White^1,2, Indran Davagnanam¹, M Jorge Cardoso³, Sjoerd B Vos^3,4, Sebastien Ourselin³, Olga Ciccarelli⁵, Tarek Yousry¹, and John Thornton^1,2

¹Neuroradiological Academic Unit, UCL Institute of Neurology, University College London, London, United Kingdom, ²Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom, ³Translation Imaging Group, Centre for Medical Imaging Computing, University College London, London, United Kingdom, ⁴MRI Unit, Epilepsy Society, Chalfont, St Peters, United Kingdom, ⁵Institute of Neurology, University College London, London, United Kingdom

The huge number of hospital MRI examinations routinely obtained for clinical purposes offers a potentially valuable “big data” resource for largescale experimental neurology. However, acquisition-scheme variation may compromise the research value of clinical imaging data. A first step towards reducing variation by prospective protocol harmonization is to systematically capture sequence-use statistics. Using an in-house tool developed to automate capture of long-term, MRI sequence deployment statistics in routine practice within our neuroradiological service, we identified “core“, most used sequences and the deployment frequency of their respective variants, to enable efficient, targeted protocol harmonization.

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