On the undersampling strategies to accelerate time-resolved 3D imaging using k-t-GRAPPA.

The purpose of this study was to explore how to optimally undersample and reconstruct time-resolved 3D data using a k-t-space-based GRAPPA technique. The performance of different reconstruction strategies was evaluated using data sets with different ratios of phase (N(y)) and partition (N(z)) encoding lines (N(y) × N(z) = 64-128 × 40-64) acquired in a moving phantom. Image reconstruction was performed for different kernel configurations and different reduction factors (R = 5, 6, 8, and 10) and was evaluated using regional error quantification and SNR analysis. To analyze the temporal fidelity of the different kernel configurations in vivo, time-resolved 3D phase contrast data were acquired in the thoracic aorta of two healthy volunteers. Results demonstrated that kernel configurations with a small kernel extension yielded superior results especially for more asymmetric data matrices as typically used in clinical applications. The application of k-t-GRAPPA to in vivo data demonstrated the feasibility of undersampling of time-resolved 3D phase contrast data set with a nominal reduction factors of up to R(net) = 8, while maintaining the temporal fidelity of the measured velocity field. Extended GRAPPA-based parallel imaging with optimized multidimensional reconstruction kernels has the potential to substantially accelerate data acquisitions in time-resolved 3D MRI.