Non‐Cartesian parallel imaging has played an important role in reducing data acquisition
time in MRI. The use of non‐Cartesian trajectories can enable more efficient coverage of k …

Spiral acquisition schemes offer unique advantages such as flow compensation, efficient k-
space sampling and robustness against motion that make this option a viable choice among …

Purpose Conventional non‐Cartesian compressed sensing requires multiple nonuniform
Fourier transforms every iteration, which is computationally expensive. Accordingly, time …

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 …

Self‐calibrating GRAPPA operator gridding (GROG) is a method by which non‐Cartesian
MRI data can be gridded using spatial information from a multichannel coil array without the …

Background Compressive sensing can provide a promising framework for accelerating fMRI
image acquisition by allowing reconstructions from a limited number of frequency-domain …

Parallel MRI reconstruction in k‐space has several advantages, such as tolerance to
calibration data errors and efficient non‐Cartesian data processing. These benefits largely …

Self-calibrating GRAPPA operator gridding (SC-GROG) is a method by which non-Cartesian
(NC) data in magnetic resonance imaging (MRI) are shifted to the Cartesian k-space grid …

Magnetic resonance imaging (MRI) is a powerful tool for studying the anatomy, physiology,
and metabolism of biological systems. Despite the fact that MRI was introduced decades …

This paper presents an iterative image reconstruction method for radial encodings in MRI
based on a total variation (TV) regularization. The algebraic reconstruction method …