Learning-based method for k-space trajectory design in MRI

Variable density sampling of the k-space in MRI is an integral part of trajectory design. It has been observed that data-driven trajectory design methods provide a better image reconstruction as compared to trajectories obtained from a fixed or a parametric density function. In this paper, a data-driven strategy has been proposed to obtain non...

Towards a general framework for fast and feasible k-space trajectories for MRI based on projection methods

The design of feasible trajectories to traverse the k-space for sampling in magnetic resonance imaging (MRI) is important while considering ways to reduce the scan time. Over the recent years, non-Cartesian trajectories have been observed to result in benign artifacts and being less sensitive to motion. In this paper, we propose a generalized...

K-Space Trajectory Design for Reduced MRI Scan Time

The development of compressed sensing (CS) techniques for magnetic resonance imaging (MRI) is enabling a speedup of MRI scanning. To increase the incoherence in the sampling, a random selection of points on the k-space is deployed and a continuous trajectory is obtained by solving a traveling salesman problem (TSP) through these points. A...

Space Filling Curves for MRI Sampling

A novel class of k-space trajectories for magnetic resonance imaging (MRI) sampling using space filling curves (SFCs) is presented here. More specifically, Peano, Hilbert and Sierpinski curves are used. We propose 1-shot and 4-shot variable density SFCs by utilizing the space coverage provided by SFCs in different iterations. The proposed...