SPARKLING: variable-density k-space filling curves for accelerated T
compressed sensing
k-space trajectories
optimization
variable density
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
06 2019
06 2019
Historique:
received:
31
07
2018
revised:
18
12
2018
accepted:
08
01
2019
pubmed:
19
2
2019
medline:
19
5
2020
entrez:
19
2
2019
Statut:
ppublish
Résumé
To 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). 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. Ex vivo and in vivo prospective 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 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.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3643-3661Informations de copyright
© 2019 International Society for Magnetic Resonance in Medicine.