Recommended Usage

Guidelines are provisional, as they are evolving, but the following are unlikely to change

General suggestions:

• For the best results from outlier detection process, it is recommended to divide list of IDs into known groups (healthy, disease1, disease2, young, old etc) based on non-imaging parameters (such as clinical diagnosis, age etc), to perform the QC process independently on each group.
• Be generous in the number of slices you use to review in each view (even if they appear small in the collage), as you have the ability to zoom-in anywhere you please for detailed inspection.
• Routinely toggle overlays to ensure composite overlays are not affecting your perception of GM/WB boundaries in scans with unusual intensity distributions (low or high contrast, dark or too bright etc).
• All the commands shown in the documentation are to be started from the command line/terminal/shell, and you need to use the file paths (delimited by quotes) as necessary and appropriate for your platform (Linux, Mac, Windows)

For Freesurfer outputs:

• Inspect the quality of raw T1 MRI scans first, using visualqc, for presence of any artefacts, such as motion, ringing, ghosting, and anything else.
• Install and run Freesufer, on ALL subjects in your dataset.
• Follow the troubleshooting guide by the Freesurfer team, that includes atleast the following checks. These slides are a fantastic start to get an idea of what to focus on.
  • Review the accuracy of white and pial surfaces (this is the default), and identify subjects for further inspection (errors in the preceding steps of the pipeline)
  • Review the segmentation of white matter is accurate (overlay wm.mgz on T1.mgz) for each subject, and identify those to be rerun or to be corrected for minor errors.
  • Review the accuracy of skull-stripping for each subject, and identify the subjects that need to be rerun with special flags (for major errors), or corrected manually (for minor errors).

Alignment checks (Registration quality)

• When comparing across modalities (e.g. EPI to T1, or PET to T1), big dissimilarities in intensity distributions might (if PET distribtuion too narrow, while T1w has a broad distribution) produce useless composites. In such cases, edge overlay or animation could work.
• You could even attempt to rescale them beforehand as well, whichever is suitable for your application.