IQMs for functional images

Measures for the structural information

Definitions are given in the summary of structural IQMs.

  • Entropy-focus criterion (efc()).
  • Foreground-Background energy ratio (fber(), [Shehzad2015]).
  • Full-width half maximum smoothness (fwhm_*).
  • Signal-to-noise ratio (snr()).
  • Summary statistics (summary_*_*).

Measures for the temporal information

  • DVARS - D referring to temporal derivative of timecourses, VARS referring to RMS variance over voxels (dvars), calculated with nipype before motion correction.
  • Ghost to Signal Ratio (gsr(), ghost_*: along the two possible phase-encoding axes x, y.
  • Global Correlation (gcor(), gcor).
  • Temporal SNR (tSNR, tsnr) is the median value of the tSNR map.

Measures for artifacts and other

  • Framewise Displacement (mean_fd, [Power2012]).
  • Number of timepoints above FD theshold (num_fd): the threshold is defined at 0.20mm, so FD \(> 0.20mm\)
  • Percent of ``num_fd`` w.r.t. the timeseries.
  • Outlier fraction (outlier) - Mean fraction of outliers per fMRI volume as given by AFNI.
  • Quality index (quality) - Mean quality index as computed by AFNI.


[Atkinson1997]Atkinson et al., Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion, IEEE Trans Med Imag 16(6):903-910, 1997. doi:10.1109/42.650886.
[Friedman2008]Friedman, L et al., Test–retest and between‐site reliability in a multicenter fMRI study. Hum Brain Mapp, 29(8):958–972, 2008. doi:10.1002/hbm.20440.
[Giannelli2010]Giannelli et al., Characterization of Nyquist ghost in EPI-fMRI acquisition sequences implemented on two clinical 1.5 T MR scanner systems: effect of readout bandwidth and echo spacing. J App Clin Med Phy, 11(4). 2010. doi:10.1120/jacmp.v11i4.3237.
[Jenkinson2002]Jenkinson et al., Improved Optimisation for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 17(2), 825-841, 2002. doi:10.1006/nimg.2002.1132.
[Nichols2013]Nichols, Notes on Creating a Standardized Version of DVARS, 2013.
[Power2012]Power et al., Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, NeuroImage 59(3):2142-2154, 2012, doi:10.1016/j.neuroimage.2011.10.018.
[Saad2013]Saad et al. Correcting Brain-Wide Correlation Differences in Resting-State FMRI, Brain Conn 3(4):339-352, 2013, doi:10.1089/brain.2013.0156.

mriqc.qc.functional module

mriqc.qc.functional.gcor(func, mask=None)[source]

Compute the GCOR [Saad2013].

  • func (numpy.ndarray) – input fMRI dataset, after motion correction
  • mask (numpy.ndarray) – 3D brain mask

the computed GCOR value

mriqc.qc.functional.gsr(epi_data, mask, direction=u'y', ref_file=None, out_file=None)[source]

Computes the GSR [Giannelli2010]. The procedure is as follows:

  1. Create a Nyquist ghost mask by circle-shifting the original mask by \(N/2\).
  2. Rotate by \(N/2\)
  3. Remove the intersection with the original mask
  4. Generate a non-ghost background
  5. Calculate the GSR


This should be used with EPI images for which the phase encoding direction is known.

  • epi_file (str) – path to epi file
  • mask_file (str) – path to brain mask
  • direction (str) – the direction of phase encoding (x, y, all)

the computed gsr