title: Non-Cartesian data reconstruction using GRAPPA operator gridding (GROG).
abstract: A novel approach that uses the concepts of parallel imaging to grid data sampled along a non-Cartesian trajectory using GRAPPA operator gridding (GROG) is described. GROG shifts any acquired data point to its nearest Cartesian location, thereby converting non-Cartesian to Cartesian data. Unlike other parallel imaging methods, GROG synthesizes the net weight for a shift in any direction from a single basis set of weights along the logical k-space directions. Given the vastly reduced size of the basis set, GROG calibration and reconstruction requires fewer operations and less calibration data than other parallel imaging methods for gridding. Instead of calculating and applying a density compensation function (DCF), GROG requires only local averaging, as the reconstructed points fall upon the Cartesian grid. Simulations are performed to demonstrate that the root mean square error (RMSE) values of images gridded with GROG are similar to those for images gridded using the gold-standard convolution gridding. Finally, GROG is compared to the convolution gridding technique using data sampled along radial, spiral, rosette, and BLADE (a.k.a. periodically rotated overlapping parallel lines with enhanced reconstruction [PROPELLER]) trajectories.
authors.affiliation: Department of Experimental Physics 5, University of Würzburg, Würzburg, Germany. neseiber@physik.uni-wuerzburg.de
authors.forename: Nicole
authors.initials: N
authors.lastname: Seiberlich
authors.forename: Felix A
authors.initials: FA
authors.lastname: Breuer
authors.forename: Martin
authors.initials: M
authors.lastname: Blaimer
authors.forename: Kestutis
authors.initials: K
authors.lastname: Barkauskas
authors.forename: Peter M
authors.initials: PM
authors.lastname: Jakob
authors.forename: Mark A
authors.initials: MA
authors.lastname: Griswold
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity