title: Reconstruction of undersampled non-Cartesian data sets using pseudo-Cartesian GRAPPA in conjunction with GROG.
abstract: Most k-space-based parallel imaging reconstruction techniques, such as Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA), necessitate the acquisition of regularly sampled Cartesian k-space data to reconstruct a nonaliased image efficiently. However, non-Cartesian sampling schemes offer some inherent advantages to the user due to their better coverage of the center of k-space and faster acquisition times. On the other hand, these sampling schemes have the disadvantage that the points acquired generally do not lie on a grid and have complex k-space sampling patterns. Thus, the extension of Cartesian GRAPPA to non-Cartesian sequences is nontrivial. This study introduces a simple, novel method for performing Cartesian GRAPPA reconstructions on undersampled non-Cartesian k-space data gridded using GROG (GRAPPA Operator Gridding) to arrive at a nonaliased image. Because the undersampled non-Cartesian data cannot be reconstructed using a single GRAPPA kernel, several Cartesian patterns are selected for the reconstruction. This flexibility in terms of both the appearance and number of patterns allows this pseudo-Cartesian GRAPPA to be used with undersampled data sets acquired with any non-Cartesian trajectory. The successful implementation of the reconstruction algorithm using several different trajectories, including radial, rosette, spiral, one-dimensional non-Cartesian, and zig-zag trajectories, is demonstrated.
authors.affiliation: Department of Experimental Physics 5, University of Würzburg, Am Hubland, Würzburg, Germany. neseiber@physik.uni-wuerzburg.de
authors.forename: Nicole
authors.initials: N
authors.lastname: Seiberlich
authors.forename: Felix
authors.initials: F
authors.lastname: Breuer
authors.forename: Robin
authors.initials: R
authors.lastname: Heidemann
authors.forename: Martin
authors.initials: M
authors.lastname: Blaimer
authors.forename: Mark
authors.initials: M
authors.lastname: Griswold
authors.forename: Peter
authors.initials: P
authors.lastname: Jakob
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D005240:Feasibility Studies; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity