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Abstract #1529

SPARKLING: Novel Non-Cartesian Sampling Schemes for Accelerated 2D Anatomical Imaging at 7T Using Compressed Sensing

Carole Lazarus1, Pierre Weiss2, Nicolas Chauffert1, Franck Mauconduit3, Michel Bottlaender4, Alexandre Vignaud4, and Philippe Ciuciu1

1CEA/NeuroSpin, INRIA/Parietal, Gif-sur-Yvette Cedex, France, 2CNRS/ITAV, Toulouse, France, 3Siemens Healthineers, Saint-Denis, France, 4CEA/NeuroSpin, Gif-sur-Yvette Cedex, France

We present for the first time the implementation of novel non-Cartesian trajectories on a 7T scanner for 2D anatomical imaging. The proposed SPARKLING curves (Segmented Projection Algorithm for Random K-space sampLING) are a new type of non-Cartesian segmented sampling trajectories which allow fast and efficient coverage of the k-space according to a chosen variable density [1]. To demonstrate their potential, a high-resolution (0.4x0.4x3.0mm3) T2*-weighted image was acquired with an 8-fold undersampled SPARKLING trajectory. Images were reconstructed using non-linear iterative reconstructions derived from the Compressed Sensing theory.

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