How much noise is too much?

Quality control for structural and functional MRI

Why bother?

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

When to perform quality control?

Early! As soon as you get data:Helps fix problems with the scanner before the next

subjectAllows 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!

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

Basics: data consistency

MRIQC

Bids-validator (http://incf.github.io/bids-validator):

Motion in structural scans (T1 weighted)

Picture courtesy of @le_feufollet

Motion in structural scans (T1 weighted)

A lot of motion

Some motion

No motion

Gibbs ringing

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

Wrap around

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

Ghosting (Nyquist N/2 Ghosts)

https://practicalfmri.blogspot.com/2011/11/physics-for-understanding-fmri.htmlMean image Stddev image

Ghosting (chemical shift)

Spikes

https://practicalfmri.blogspot.com/2011/11/physics-for-understanding-fmri.html

t=0 t=1 t=2

Air mask

K-space (the final frontier)

K-space (the final frontier)

Spikes

Spin history effects

http://imaging.mrc-cbu.cam.ac.uk/imaging/CommonArtefacts

Motion and spin history effects

Motion and spin history effects

Motion and spin history effects

Motion and spin history effects

http://www.jonathanpower.net/2016-ni-the-plot.html

Motion and spin history effects

http://www.pnas.org/content/111/16/6058.full.pdf

QC metrics

Noise measurementSignal-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 theoryForeground-Background Energy Ratio (FBER) - higher is better

Entropy Focus Criterion (EFC) - lower is better

ArtifactsSegmentation using mathematical morphology (QI1) - lower is better

Measurements on the estimated INU (intensity non-uniformity) - values around 1.0

Partial Volume Errors (PVE) - lower is better

Other: summary statistics, intracranial volume fractions (ICV)

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

More thoughts about QC

There are not strict rules which data to discardSome artefacts and distortions can be recovered by

smart algorithmsQC can help you decide results from which subjects

you should interrogate more closely

Crowdsourcing artefactsWhat happens when you ask Twitter for help...

bit.do/mri_qcExample cases (with and without artifacts) along with QC reports

All reports generated using MRIQC (mriqc.readthedocs.io)