We have recently proposed a new optimization algorithm called SPARKLING (Spreading Projection Algorithm for Rapid K-space sampLING) to design efficient compressive sampling patterns for magnetic resonance imaging (MRI). This method has a few advantages over conventional non-Cartesian trajectories such as radial lines or spirals. First, it allows the sampling of k-space along any arbitrary density while the other two are restricted to radial densities. Second it optimizes the gradient waveforms for a given readout time. Here, we introduce an extension of the SPARKLING method for 3D imaging by considering both stacks-of-SPARKLING and fully 3D SPARKLING trajectories. Our method reaches an isotropic resolution of 600 μm in just 45 seconds for T2∗-weighted ex vivo brain imaging at 7 Tesla over a field-of-view of 200 × 200 × 140 mm3. Preliminary in vivo human brain data shows that a stack-of-SPARKLING is less subject to off-resonance artifacts than a stack-of-spirals.

I presented in this talk the recent progresses of my team related to SPARKLING for shorter scan times in MRI and XPDNet for improved image reconstruction using our deep neural nets.