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Abstract #1269

Automated pre-processing pipeline and quality control for neonatal diffusion MRI in the developing Human Connectome Project (dHCP)

Matteo Bastiani1, Jesper Andersson1, Lucilio Cordero-Grande2, Maria Murgasova2, Jana Hutter2, Anthony N. Price2, Antonios Makropoulos3, Emer Hughes2, Johannes Steinweg2, Nora Tusor2, Daniel Rueckert3, A. David Edwards2, Stephen Smith1, Jacques-Donald Tournier2, Joseph V. Hajnal2, Saad Jbabdi1, and Stamatios Sotiropoulos1

1Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford University, Oxford, United Kingdom, 2Centre for the Developing Brain, King’s College London, London, United Kingdom, 3Department of Computing, Imperial College London, London, United Kingdom

The developing Human Connectome Project (dHCP) is a collaborative 6-year project set to create a 4-dimensional map of structural and functional changes occurring throughout early development. Up to 1300 multi-modal MRI scans of foetuses and neonates (20 to 44 weeks gestational age) are currently being acquired. We present a fully automated pre-processing pipeline that allows us to efficiently analyse in-vivo diffusion MRI (dMRI) data despite the considerable technical challenges specific to neonatal imaging. We developed a quality control (QC) framework that allows us to identify issues or inconsistencies. This is especially useful when processing a very large number of subjects.

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