PurposeTo 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).TheoryThe 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.g., variable density) and a locally uniform k‐space coverage.MethodsEx vivo and in vivo prospective ‐weighted acquisitions were performed on a 7‐Tesla scanner using the SPARKLING trajectories for various setups and target densities. Our method was compared to radial and variable‐density spiral trajectories for high‐resolution imaging.ResultsCombining sampling efficiency with compressed sensing, the proposed sampling patterns allowed up to 20‐fold reductions in MR scan time (compared to fully sampled Cartesian acquisitions) for two‐dimensional ‐weighted imaging without deterioration of image quality, as demonstrated by our experimental results at 7 Tesla on in vivo human brains for a high in‐plane resolution of 390 μm. In comparison to existing non‐Cartesian sampling strategies, the proposed technique also yielded superior image quality.ConclusionsThe proposed optimization‐driven design of k‐space trajectories is a versatile framework that is able to enhance MR sampling performance in the context of compressed sensing.