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Abstract #5354

EDDY QC: Automated quality control for diffusion MRI

Matteo Bastiani1, Jesper Andersson1, Michiel Cottaar1, Fidel Alfaro-Almagro1, Sean P Fitzgibbon1, Sana Suri2, Stamatios N Sotiropoulos1,3, and Saad Jbabdi1

1Wellcome Centre for Integrative Neuroscience (WIN) - FMRIB, University of Oxford, Oxford, United Kingdom, 2Department of Psychiatry, University of Oxford, Oxford, United Kingdom, 3Sir Peter Mansfield Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom

Given the very large number of individual datasets acquired in recent population imaging studies, it is becoming essential to automate data quality control (QC). Here we present an automated QC framework to assess diffusion MRI data both at the single subject and group levels. The QC metrics are derived through different stages of FSL’s pre-processing tools (TOPUP and EDDY). We show that using this framework, it is possible to distinguish between good and bad quality datasets and, importantly, identify subsets of the data that may need careful visual inspection. We hope this QC tool will help harmonisation efforts across sites/studies.

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