SPARKLING: variable-density k-space filling curves for accelerated T 2 * -weighted MRI

Carole Lazarus 1 Pierre Weiss 2 Nicolas Chauffert 1 Franck Mauconduit 3 Loubna El Gueddari 1 Christophe Destrieux 4 Ilyess Zemmoura 5 Alexandre Vignaud 1 Philippe Ciuciu 6
1 NEUROSPIN - Service NEUROSPIN
2 RMeS - Regenerative Medicine and Skeleton research lab
3 Inserm U836, équipe 7, Nanomédecine et cerveau
GIN - Grenoble Institut des Neurosciences
4 Service de neuroradiologie [Tours]
5 Imagerie et cerveau
6 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
Abstract : Funding information Purpose: To present a new optimization-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.g., variable density) and a locally uniform k-space coverage. Methods: Ex vivo and in vivo prospective T * 2-weighted acquisitions were performed on a 7 Tesla scanner using the SPARKLING tra-jectories for various setups and target densities. Our method was compared to radial and variable-density spiral trajectories for high resolution imaging. Results: Combining 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 T * 2-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. Conclusion: The 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.
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Submitted on : Wednesday, November 13, 2019 - 11:57:19 AM
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Carole Lazarus, Pierre Weiss, Nicolas Chauffert, Franck Mauconduit, Loubna El Gueddari, et al.. SPARKLING: variable-density k-space filling curves for accelerated T 2 * -weighted MRI. Magnetic Resonance in Medicine, Wiley, 2019, 81 (6), pp.3643-3661. ⟨10.1002/mrm.27678⟩. ⟨hal-02361265⟩

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