Diffusion MRI (dMRI) data can be affected by hardware and subject-related artefacts that can significantly bias downstream analyses. Therefore, automated quality control (QC) is of great importance to detect data acquisition and pre-processing issues. This is especially critical in large population studies, where manual QC is not practical. The automated EDDY QC framework allows to assess dMRI 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). 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.
If you use EDDY QC in your research, please make sure that you reference the article listed below:
Bastiani, M., Cottaar, M., Fitzgibbon, S.P., Suri, S., Alfaro-Almagro, F., Sotiropoulos, S.N., Jbabdi, S., and Andersson, J.L.R. Automated quality control for within and between diffusion MRI studies using a non-parametric framework for movement and distortion correction. NeuroImage, 184:801-812, 2019.
The main reference that should be cited when using EDDY is:
Andersson, J.L.R. and Sotiropoulos, S.N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 125:1063-1078, 2016.
If you use the --repol (replace outliers) option, please also reference:
Andersson, J.L.R., Graham, M.S., Zsoldos, E., and Sotiropoulos, S.N. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. NeuroImage, 141:556-572, 2016.
If you use the slice-to-volume motion model (accessed by the --mporder option) please also reference:
Andersson, J.L.R., Graham, M.S., Drobnjak, I., Zhang, H., Filippini, N., and Bastiani, M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. NeuroImage, 152:450-466, 2017.