SPARKLING: variable-density k-space filling curves for accelerated T -weighted MRI.

Purpose: To present a new optimition-driven design of optimal k-space trajectories in the context of compressed sensing: Spreading Projection Algorithm for Rapid K-space sampLING (SPARKLING).

Theory: The SPARKLING algorithm is a versatile method inspired from stippling techniques that automatically generates optimized sampling patterns compatible with MR hardware constraints on maximum gradient amplitude and slew rate. These non-Cartesian sampling curves are designed to comply with key criteria for optimal sampling: a controlled distribution of samples (e.

A Projection Algorithm for Gradient Waveforms Design in Magnetic Resonance Imaging.

Collecting the maximal amount of information in a given scanning time is a major concern in magnetic resonance imaging (MRI) to speed up image acquisition. The hardware constraints (gradient magnitude, slew rate, etc.), physical distortions (e.