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Abstract #2889

Non-Cartesian Parallel Imaging Reconstruction using PRUNO-GROG

Jian Zhang1, Ajit Shankaranarayanan2

1Global Applied Science Lab, GE HealthCare, Bethesda, MD, United States; 2Global Applied Science Lab, GE HealthCare, Menlo Park, CA, United States

Pseudo-Cartesian GRAPPA in conjunction with GROG (PCG-GROG) is an effective algorithm to reconstruct non-Cartesian parallel imaging data. However, the PCG reconstruction is usually a cumbersome task as different fitting patterns need to be determined point by point. PRUNO is a generalized auto-calibrated reconstruction algorithm, in which local ing kernels are independent of the acceleration factor and undersampling patterns. We can thus replace the PCG step with PRUNO to bring out a more convenient and accurate algorithm, termed PRUNO-GROG. Promising preliminary results are shown on both phantom and in vivo images.