Combining RF encoding with parallel imaging: a simulation study

Abstract

The aim of this work was to investigate combining spatial encoding by radio frequency (RF) excitation with conventional parallel imaging (PI) methods to determine whether this could improve overall imaging performance. A simulation framework was developed to predict imaging performance for regular, central and random under-sampled parallel imaging methods augmented by RF spatial signal modulation. Optimisation methods were used to find the RF modulation patterns that produce optimal image reconstruction using the condition number of the PI encoding matrix as a quality metric. The diverse patterns of raw data sampling produced were compared using a measure of data uniformity across k-space. Regular under-sampling of k-space provided the best reconstruction quality. When other under-sampling schemes were employed then RF modulation could be used to improve reconstruction, with the optimum achieved by redistributing the signal in k-space to return to regular sub-sampling. For all tested under-sampling patterns, no further improvements in image quality were attained. Using the simulation framework and metrics described the interaction of different spatial encoding approaches could be investigated. Regular sub-sampling provided optimal reconstruction, independent of whether the spatial encoding was achieved by gradients only or a combination of gradient and RF.