Non‐Cartesian parallel imaging reconstruction

Abstract

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-space, which can be leveraged to reduce scan times. These trajectories can be undersampled to achieve even faster scan times, but the resulting images may contain aliasing artifacts. Just as Cartesian parallel imaging can be employed to reconstruct images from undersampled Cartesian data, non-Cartesian parallel imaging methods can mitigate aliasing artifacts by using additional spatial encoding information in the form of the non-homogeneous sensitivities of multi-coil phased arrays. This review will begin with an overview of non-Cartesian k-space trajectories and their sampling properties, followed by an in-depth discussion of several selected non-Cartesian parallel imaging algorithms. Three representative non-Cartesian parallel imaging methods will be described, including Conjugate Gradient SENSE (CG SENSE), non-Cartesian GRAPPA, and Iterative Self-Consistent Parallel Imaging Reconstruction (SPIRiT). After a discussion of these three techniques, several potential promising clinical applications of non-Cartesian parallel imaging will be covered.

Quantitative High-Resolution Renal Perfusion Imaging Using 3-Dimensional Through-Time Radial Generalized Autocalibrating Partially Parallel Acquisition

“…For this cylindrical-shaped trajectory, data were sampled in-plane with a radial trajectory that is replicated along the partition direction using Cartesian encoding. To accelerate the acquisition, only radial under sampling was used (as described in (25)). The in-plane radial trajectory was under sampled by a factor of eight such that 20 radial projections were acquired for each partition.…”

“…The 3D through-time radial GRAPPA reconstruction is entirely automated, and only requires the user to select calibration parameters (segment size, number of calibration repetitions and partitions) that have been previously optimized (22,23). Further reconstruction details can be found in (22,23) and open-source code for through-time radial GRAPPA can be found in (25). …”

Patch-Based Directional Redundant Wavelets in Compressed Sensing Parallel Magnetic Resonance Imaging with Radial Sampling Trajectory

“…NUFFT As mentioned above, radial sampling technique has advantages in sampling speed. 23 Special pulse sequences are used to achieve specific sampling patterns, e.g., radial sampling. 24 Consequently, the fast Fourier transform is no longer applicable directly on the sampled k-space data.…”

“…For example, compared with Cartesian undersampling, a non-Cartesian undersampling trajectory can enhance the artifact incoherence. 23 Furthermore, nonCartesian sampling trajectories usually lead to rapid k-space coverage thus speed up encoding 23 In this paper, we proposed to use patch-based directional redundant wavelets (PBDRW) as an adaptive sparsifying method in compressed sensing sensitivity encoding (CS-SENSE), and combine the new method with radial sampling to achieve higher acceleration factors. An alternating direction method with continuation algorithm is derived to solve this MRI reconstruction model.…”

CUDA accelerated uniform re-sampling for non-Cartesian MR reconstruction

“…Although data acquired uniformly under a on-off gradient can be reconstructed straightforwardly by Fast Fourier Transform (FFT), fast gradient switch is not feasible in practice [2]. Therefore, acquiring data with non-Cartesian trajectories under smoothly switched gradients have become the focus of intensive investigation by diverse groups of researchers in this field [3]. As signals from central part of the k-space determine the contrast and signal-to-noise ratio (SNR), the multi-strip central oversampling method, namely PROPELLER, is able to correct physiological and motion artifacts and therefore has been successfully applied in high-end MR equipments [4,5].…”