Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction.

Bastiani M., Cottaar M., Fitzgibbon SP., Suri S., Alfaro-Almagro F., Sotiropoulos SN., Jbabdi S., Andersson JLR.

Diffusion MRI data can be affected by hardware and subject-related artefacts that can adversely affect downstream analyses. Therefore, automated quality control (QC) is of great importance, especially in large population studies where visual QC is not practical. In this work, we introduce an automated diffusion MRI QC framework for single subject and group studies. The QC is based on a comprehensive, non-parametric approach for movement and distortion correction: FSL EDDY, which allows us to extract a rich set of QC metrics that are both sensitive and specific to different types of artefacts. Two different tools are presented: QUAD (QUality Assessment for DMRI), for single subject QC and SQUAD (Study-wise QUality Assessment for DMRI), which is designed to enable group QC and facilitate cross-studies harmonisation efforts.

DOI

10.1016/j.neuroimage.2018.09.073

Type

Journal article

Journal

Neuroimage

Publication Date

01/01/2019

Volume

184

Pages

801 - 812

Keywords

Diffusion MRI, Eddy current, Movement, Quality control, Susceptibility, Artifacts, Brain, Brain Mapping, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Female, Humans, Image Processing, Computer-Assisted, Male, Quality Control, Reproducibility of Results, Signal-To-Noise Ratio

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