Parallel MRI with extended and averaged GRAPPA kernels (PEAK-GRAPPA): optimized spatiotemporal dynamic imaging.

Objective: To evaluate an optimized k-t-space related reconstruction method for dynamic magnetic resonance imaging (MRI), a method called PEAK-GRAPPA (Parallel MRI with Extended and Averaged GRAPPA Kernels) is presented which is based on an extended spatiotemporal GRAPPA kernel in combination with temporal averaging of coil weights.

Methods: The PEAK-GRAPPA kernel consists of a uniform geometry with several spatial and temporal source points from acquired k-space lines and several target points from missing k-space lines. In order to improve the quality of coil weight estimation sets of coil weights are averaged over the temporal dimension.

Results: The kernel geometry leads to strongly decreased reconstruction times compared to the recently introduced k-t-GRAPPA using different kernel geometries with only one target point per kernel to fit. Improved results were obtained in terms of the root mean square error and the signal-to-noise ratio as demonstrated by in vivo cardiac imaging.

Conclusions: Using a uniform kernel geometry for weight estimation with the properties of uncorrelated noise of different acquired timeframes, optimized results were achieved in terms of error level, signal-to-noise ratio, and reconstruction time.