Measurement of signal‐to‐noise ratios in sum‐of‐squares MR images

Abstract

Dietrich et al (1) present an interesting comparison of different approaches in the estimation of signal-to-noise ratio (SNR) in MRI images and investigate the influence of the use of multichannel coils, parallel imaging, and reconstruction filters. One conclusion of the article is that the widely used SNR estimation method using a background region of interest (ROI) for the estimation of the signal standard deviation provides inaccurate results when multichannel coils are employed. For an eight-channel coil, such as the one employed in Ref.1, the factor between the noise mean in a background ROI (mair) and the noise standard deviation (σmean) obtained with Eq. [3] is 0.254 and is significantly different from the value of 0.797 obtained with Eq. [1]. This important difference probably explains why the ratios SNR(8CH)/SNR(1CH) presented in Table 1 and 2 of Ref.1 are significantly lower for SNRmean when compared to the values obtained with the other methods. The values of SNRstdv for the same ratios are in better agreement with the values obtained by using the other methods because Eqs. [2] and [4] give factors that are more similar (1.526 in comparison to 1.426). The evaluation of the signal standard deviation from a background ROI definitely appears risky when parallel imaging and reconstruction filters are employed or when the correlation between the channels is high. However, if correlation is low and as long as a standard sum-of-squares reconstruction without parallel imaging is employed, the use of the good factors should definitely improve the accuracy of the SNR estimation when multichannel coils are used, especially when coils with different number of channels are compared. Guillaume Gilbert BSc*, * Department of Radiology, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada.