On the Optimal Acceleration of Time-Resolved 3D Imaging Using GRAPPA

Bernd Andr Jung¹, Simon Bauer¹, Michael Markl¹

The aim of this work was to explore how to optimally undersample and reconstruct time-resolved 3D data using GRAPPA. Two different data sets were acquired in a moving phantom with isotropic and anisotropic data matrices. Reconstruction was performed with 3D- (kx,ky,t) and 4D-kernel (kx,ky,kz,t) configurations. For the symmetric data matrix, it was demonstrated that the 4D-kernel configuration leads to better results in terms of error behaviour. However, in a more realistic anisotropic data matrix typically used in clinical applications the different kernel configurations show an opposite behaviour. Furthermore, noise enhancement for 4D-kernel configuration was more pronounced compared to 3D-configurations.