Robust GRAPPA reconstruction

GRAPPA is a popular reconstruction technique in parallel imaging. In GRAPPA, a least-squares technique is used to solve the over-determined equations and get the "fitting" coefficients for the reconstruction. We developed the robust GRAPPA method whereby robust estimation techniques are used to estimate the coefficients with discounting of k-space data outliers. One implementation, slow robust GRAPPA used iteratively re-weighted techniques, and it was compared to an ad hoc fast robust GRAPPA implementation. We evaluated these new algorithms using the perceptual difference model (PDM). PDM has already been successfully applied to a variety of MR applications. We systematically investigated independent variables including algorithm, outer reduction factor, total reduction factor, outlier ratio, and noise across multiple image datasets, giving 7500 images. We conclude that fast robust GRAPPA method gives results very similar to slow robust GRAPPA and that both give significant improvements as compared to standard GRAPPA. PDM is very helpful in designing and optimizing the MR reconstruction algorithms.