Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank

Alfaro-Almagro, Fidel and Jenkinson, Mark and Bangerter, Neal K. and Andersson, Jesper L.R. and Griffanti, Ludovica and Douaud, Gwenaëlle and Sotiropoulos, Stamatios N. and Jbabdi, Saad and Hernandez-Fernandez, Moises and Vallee, Emmanuel and Vidaurre, Diego and Webster, Matthew and McCarthy, Paul and Rorden, Christopher and Daducci, Alessandro and Alexander, Daniel C. and Zhang, Hui and Dragonu, Iulius and Matthews, Paul M. and Miller, Karla L. and Smith, Stephen M. (2017) Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. NeuroImage . ISSN 1095-9572 (In Press)

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Abstract

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.

Item Type: Article
Keywords: Epidemiological studies; Image analysis pipeline; Multi-modal data integration; Quality control; Big data imaging; Machine learning
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Medicine > Units > Radiology and Imaging Sciences
Identification Number: 10.1016/j.neuroimage.2017.10.034
Depositing User: Eprints, Support
Date Deposited: 25 Oct 2017 09:41
Last Modified: 25 Oct 2017 16:55
URI: http://eprints.nottingham.ac.uk/id/eprint/47544

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