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.

Calibration-Less Multi-coil MR image reconstruction

“…In this study, we will show that radial MRI yields good results for not only our proposed method but also for previously developed methods like GROG (GRAPPA Operator Re-gridding) [49], SPIRiT, and SENSE. …”

Image reconstructions with the rotating RF coil

“…Presently, reconstruction from non-Cartesian sampling patterns is often inconvenient on many MRI installations that rely heavily on Cartesian coordinates. Recent years have seen a notable increase in the development of polar reconstruction methods such as the Polar Fourier Transform (PFT) [28], k-space regridding and interpolation techniques [29], which should render regular use of non-Cartesian k-space trajectories practical in the future. Single shot sequences such as the Echo Planar Imaging (EPI) sequence conform well to the serial acquisition nature of RRFC in that the entire k-space can be acquired in one or more complete coil revolutions.…”

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

“…Susceptibility artifacts: the benefit from parallel acceleration techniques (GRAPPA, SENSE), selective RF pulse (zonal oblique multi-slice EPI (ZOOM-EPI)), or RF multi-band technology It is well-known that parallel imaging techniques (generalized autocalibrating partially parallel acquisition (GRAPPA), array spatial sensitivity encoding technique (ASSET), sensitivity encoding (SENSE)) [37][38][39][40][41][42][43][44][45][46] allow substantial reduction of echo time (TE) and related susceptibility artifacts (see Fig. 3) leading to a subsequent improvement of image quality close to air-tissue interfaces.…”