Parametric Noise Analysis in Parallel MRI
Parallel imaging (pMRI) methods allow increasing the acquisition rate in MRI via subsampling of the k-space. As a consequence of the subsampling image in the x-space presents some artifacts that must be corrected with the so-called pMRI methods. Among them, SENSE and GRAPPA are two of the most popular. In most pMRI methods, the reconstruction process yields to a value of the variance of noise in the final image that depends on the position. Hence, traditional noise estimation methods, based on a single noise level for the whole image, fail. In this chapter, we review a methodology to estimate the spatial dependent pattern of the variance of noise in SENSE and GRAPPA reconstructed images. The estimation follows a parametric methodology: the models for noise are known and some parameters of those models are assumed to be known beforehand: the sensitivity maps of each receiver coil, the reconstruction weights, and the correlation among coils. Although we only focus on two pMRI methods, the results here presented are easily extrapolated to other parallel algorithms.