期刊
NEUROIMAGE
卷 184, 期 -, 页码 801-812出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2018.09.073
关键词
Diffusion MRI; Quality control; Movement; Susceptibility; Eddy current
资金
- European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013/ERC) [319456]
- EPSRC UK [EP/L023067]
- UK Medical Research Council [MR/L009013/1]
- Wellcome-Trust Strategic Award [098369/Z/12/Z]
- NIH Human Connectome Project [1U01MH109589-01, 1U01AG052564-01]
- Wellcome Trust [203139/Z/16/Z]
- 16 NIH Institutes [1U54MH091657]
- NIH Blueprint for Neuroscience Research
- McDonnell Center for Systems Neuroscience at Washington University
- UK Medical Research Council
- Wellcome Trust via UK Biobank
- HDH Wills 1965 Charitable Trust [1117747]
- Lifelong Health and Wellbeing Programme Grant: Predicting MRI abnormalities with longitudinal data of the Whitehall II Substudy (UK Medical Research Council) [G1001354]
- EPSRC [EP/L023067/1] Funding Source: UKRI
- MRC [MR/L009013/1, MR/K013351/1, G1001354] Funding Source: UKRI
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.
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