Table 2 Summary table of image quality metrics for anatomical (T1w, T2w) MRI.

From: Crowdsourced MRI quality metrics and expert quality annotations for training of humans and machines

IQMs based on noise measurements
CJV The coefficient of joint variation of GM and WM was proposed as an objective function by Ganzetti et al.23 for the optimization of INU correction algorithms. Higher values are related to the presence of heavy head motion and large INU artifacts.
CNR The contrast-to-noise ratio24 is an extension of the SNR calculation to evaluate how separated the tissue distributions of GM and WM are. Higher values indicate better quality.
SNR MRIQC includes the signal-to-noise ratio calculation proposed by Dietrich et al.25, using the air background as noise reference. Additionally, for images that have undergone some noise reduction processing, or the more complex noise realizations of current parallel acquisitions, a simplified calculation using the within tissue variance is also provided.
QI2 The second quality index of Mortamet et al.8 is a calculation of the goodness-of-fit of a χ2 distribution on the air mask, once the artifactual intensities detected for computing the QI1 index have been removed. The description of the QI1 is found below.
IQMs based on information theory
EFC The entropy-focus criterion26 uses the Shannon entropy of voxel intensities as an indication of ghosting and blurring induced by head motion. Lower values are better.
FBER The foreground-background energy ratio10 is calculated as the mean energy of image values within the head relative to the mean energy of image values in the air mask. Consequently, higher values are better.
IQMs targeting specific artifacts
INU MRIQC measures the location and spread of the bias field extracted estimated by the intensity non-uniformity (INU) correction. The smaller spreads located around 1.0 are better.
QI1 Mortamet’s first quality index8 measures the number of artifactual intensities in the air surrounding the head above the nasio-cerebellar axis. The smaller QI1, the better.
WM2MAX The white-matter to maximum intensity ratio is the median intensity within the WM mask over the 95% percentile of the full intensity distribution, that captures the existence of long tails due to hyper-intensity of the carotid vessels and fat. Values should be around the interval [0.6, 0.8]
Other IQMs
FWHM The full-width half-maximum27 is an estimation of the blurriness of the image calculated with AFNI’s 3dFWHMx. Smaller is better.
ICVs Estimation of the intracranial volume (ICV) of each tissue calculated on the FSL fast’s segmentation. Normative values fall around 20%, 45% and 35% for cerebrospinal fluid (CSF), WM and GM, respectively.
rPVE The residual partial volume effect feature is a tissue-wise sum of partial volumes that fall in the range [5–95%] of the total volume of a pixel, computed on the partial volume maps generated by FSL fast. Smaller residual partial volume effects (rPVEs) are better.
SSTATs Several summary statistics (mean, standard deviation, percentiles 5% and 95%, and kurtosis) are computed within the following regions of interest: background, CSF, WM, and GM.
TPMs Overlap of tissue probability maps estimated from the image and the corresponding maps from the ICBM nonlinear-asymmetric 2009c template28.
  1. MRIQC produces a vector of 64 image quality metrics (IQMs) per input T1w or T2w scan. (Reproduced from our previous work9).