This document discusses quality control for structural and functional MRI data. It explains that quality control is important for deciding which data to include in a study, diagnosing problems with data acquisition, and fixing issues before scanning new subjects. The document recommends performing quality control early and provides examples of checking for consistency across scans and subjects, as well as metrics to detect motion, artifacts, noise, and information quality in the data.

1. How much noise is too much?
Quality control for structural and functional MRI

2. Why bother?

3. Goals of quality control
Deciding which data to include in your study and which to
reject.
Deciding on using a public dataset (is it appropriate for my
design/study?)
Diagnosing fixable problems with data acquisition process:
Types of sequences
Scanner malfunctions
Head padding
Participant instructions

4. When to perform quality control?
Early! As soon as you get data:
Helps fix problems with the scanner before the next subject
Allows to recruit extra subjects if you know some data needs
to be discarded
QC (when done with the right tools) takes very
little effort - but can save a lot of money and time
in the long run!

5. Basics: data consistency
Check if:
Scans for a new subjects have the same (prescribed) parameters:
Resolution
Field of view
Number of timepoints (fMRI)
Each subject has all of the scans

6. Basics: data consistency
MRIQC
Bids-validator (http://incf.github.io/bids-validator):

7. Motion in structural scans (T1 weighted)
Picture courtesy of @le_feufollet

8. Motion in structural scans (T1 weighted)
A lot of motion
Some motion
No motion

9. Gibbs ringing
http://pubs.rsna.org/doi/pdf/10.1148/rg.261055134

10. Wrap around
https://practicalfmri.blogspot.com/2011/12/common-static-epi-artifacts-aliasing-or.html

11. Ghosting (Nyquist N/2 Ghosts)
https://practicalfmri.blogspot.com/2011/11/physics-for-understanding-fmri.html
Mean image Stddev image

12. Ghosting (chemical shift)

13. Spikes
https://practicalfmri.blogspot.com/2011/11/physics-for-understanding-fmri.html
t=0 t=1 t=2
Air mask

14. K-space (the final frontier)

15. K-space (the final frontier)

16. Spikes

17. Spin history effects
http://imaging.mrc-cbu.cam.ac.uk/imaging/CommonArtefacts

18. Motion and spin history effects

19. Motion and spin history effects

20. Motion and spin history effects

21. Motion and spin history effects
http://www.jonathanpower.net/2016-ni-the-plot.html

22. Motion and spin history effects
http://www.pnas.org/content/111/16/6058.full.pdf

23. QC metrics
Noise measurement
Signal-to-noise ratio (SNR) - higher is better
Contrast-to-noise ration (CNR) - higher is better
Sharpness (full-width half maximum estimations) - smaller FWHM is better
Goodness of fit of a noise model into the noise in the background (QI2) - lower is better
Coefficient of Joint Variation (CJV) - lower is better
Information theory
Foreground-Background Energy Ratio (FBER) - higher is better
Entropy Focus Criterion (EFC) - lower is better
Artifacts
Segmentation using mathematical morphology (QI1) - lower is better

24. QC metrics
Noise measurement: SNR, tSNR, temporal standard deviation
Information theory: EFC, FBER
Confounds and artifacts:
Framewise Displacement (FD) - lower is better
(Standardized) DVARS (D referring to temporal derivative of timecourses, VARS referring to RMS
variance over voxels) - lower is better
Ghost-to-Signal ratio (GSR) - lower is better
Global correlation (GCOR) - lower is better
Energy of spectrum (ES) - lower is better
AFNI’s outlier detection and quality indexes

25. More thoughts about QC
There are not strict rules which data to discard
Some artefacts and distortions can be recovered by smart
algorithms
QC can help you decide results from which subjects you should
interrogate more closely

26. Crowdsourcing artefacts
What happens when you ask Twitter for help...

27. bit.do/mri_qc
Example cases (with and without artifacts) along with QC reports
All reports generated using MRIQC (mriqc.readthedocs.io)