The effect of reconstruction and acquisition parameters for GRAPPA-based parallel imaging on the image quality

Parallel imaging based on generalized autocalibrating partially parallel acquisitions is widely used in the clinical routine. To date, no detailed analysis has been presented describing the dependence of the image quality on the reconstruction and acquisition parameters such as the number of autocalibration signal (ACS) lines NACS, the reconstruction kernel size (bx×by), and the undersampling factor R. To evaluate their influence on the performance of generalized autocalibrating partially parallel acquisitions, two phantom data sets acquired with 12-channel and 32-channel receive coils and three in vivo measurements were analyzed. Reconstruction parameters were systematically varied between R=2-4, NACS=4-64, bx=1-9, and by=2-10 to characterize their influence on image quality and noise. A main aspect of the analysis was to optimize the parameter set with respect to the effectively achieved net image acceleration. Selecting the undersampling factor R as small as possible for a given net acceleration yielded the best result in a clear majority of cases. For all data sets and coil geometries, the optimal kernel sizes and number of ACS lines were similar for a chosen undersampling factor R. In summary, the number of ACS lines should not be chosen below NACS=10-16. A robust choice for the kernel size was bx=9 and by=2-4.