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The SPARKLING algorithm was originally developed for accelerated 2D magneticresonance imaging (MRI) in the compressed sensing (CS) context. It yieldsnon-Cartesian sampling trajectories that jointly fulfill a target samplingdensity while each individual trajectory complies with MR hardware constraints.However, the two main limitations of SPARKLING are first that the optimaltarget sampling density is unknown and thus a user-defined parameter and secondthat this sampling pattern generation remains disconnected from MR imagereconstruction thus from the optimization of image quality. Recently,datadriven learning schemes such as LOUPE have been proposed to learn adiscrete sampling pattern, by jointly optimizing the whole pipeline from dataacquisition to image reconstruction. In this work, we merge these methods witha state-of-the-art deep neural network for image reconstruction, called XPDNET,to learn the optimal target sampling density. Next, this density is used asinput parameter to SPARKLING to obtain 20x accelerated non-Cartesiantrajectories. These trajectories are tested on retrospective compressed sensing(CS) studies and show superior performance in terms of image quality with bothdeep learning (DL) and conventional CS reconstruction schemes.

Learning the sampling density in 2D SPARKLING MRI acquisition for optimized image reconstruction