Measurement of signal‐to‐noise ratios in MR images: Influence of multichannel coils, parallel imaging, and reconstruction filters

By O. Dietrich, J. Raya, S. Reeder, M. Reiser, S. Schönberg. Journal of Magnetic Resonance Imaging, 2007

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MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites (2016, methodology): “MRIQC includes the the signal-to-nose ratio calculation proposed by Dietrich et al. [32], using the air background as noise reference.”
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging (2013, methodology): “1 [Dietrich et al., 2007].”
Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution (2011, methodology): “SNR within the b 0 s/mm 2 images was approximately 30, calculated using the difference method to compensate for geometric noise variations in parallel images [Dietrich et al., 2007].”
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator (2014, methodology): “The signal-to-noise ratio (SNR) of this data is 33, which is measured on the magnitude image according to SNR ¼ l d ; ð18Þ where l is the mean of image and d is the standard deviation of noise extracted from the background of image (Dietrich et al., 2007; Henkelman, 1985; Qu et al., 2012).”