A method to assess spatially variant noise in dynamic MR image series.

Accurate measurement of spatially variant noise in MR images acquired using parallel imaging techniques is challenging. Image-based noise measurement methods such as the subtraction method proposed by the National Electrical Manufacturers Association or the multiple acquisition method often cannot be applied in vivo due to motion and/or dynamic contrast changes. Based on the Karhunen-Loeve transform and random matrix theory, we propose a novel method to accurately assess the noise variance in image series bearing temporal redundancy. The method fits the probability density function of eigenvalues from the temporal covariance matrix of the image series to the Marcenko-Pastur distribution. The accuracy of our method was validated using numerical simulation and an MR noise measurement experiment. The ability of this method to derive the g-factor map of a static phantom was validated against the multiple acquisition method. The method was applied to in vivo cardiac and brain image series and the results agreed with subtraction and multiple acquisition methods, respectively. This new image-based noise measurement method provides a practical means of retrospectively evaluating the noise level and/or g-factor map from multiframe image series.