Abstract #3648
Robust GRAPPA Reconstruction and Its Evaluation with Perceptual Difference Model (PDM)
Wilson D, Huo D
Case Western Reserve University, Case Western Reserve University
In GRAPPA, a least-squares technique is used to solve the over-determined equations and get the fitting coefficients. We developed the Robust GRAPPA 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 compared to an ad hoc Fast Robust GRAPPA implementation. We evaluated these new algorithms using the Perceptual Difference Model (PDM). We systematically investigated 7500 images with independent variables combinations. We conclude Robust GRAPPA method gives significant improvements as compared to standard GRAPPA. PDM is very helpful in optimizing the MR reconstruction algorithms.
