Titre :

Gradient waveform design for variable density sampling in Magnetic Resonance Imaging

Auteur(s) :

Chauffert, Nicolas [Auteur]
Service NEUROSPIN [NEUROSPIN]
Modelling brain structure, function and variability based on high-field MRI data [PARIETAL]
Weiss, Pierre [Auteur]
Institut des Technologies Avancées en sciences du Vivant [ITAV]
Institut de Mathématiques de Toulouse UMR5219 [IMT]
Kahn, Jonas [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Ciuciu, Philippe [Auteur]
Modelling brain structure, function and variability based on high-field MRI data [PARIETAL]
Service NEUROSPIN [NEUROSPIN]

Mot(s)-clé(s) en anglais :

gradient hardware constraints
variable density sampling
k-space trajectories
gradient waveform design
mag-netic resonance imaging

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Mathématiques [math]/Optimisation et contrôle [math.OC]
Sciences du Vivant [q-bio]/Ingénierie biomédicale/Imagerie

Résumé en anglais : [en]

Fast coverage of k-space is a major concern to speed up data acquisition in Magnetic Resonance Imaging (MRI) and limit image distortions due to long echo train durations. The hardware gradient constraints (magnitude, slew ...
Lire la suite >Fast coverage of k-space is a major concern to speed up data acquisition in Magnetic Resonance Imaging (MRI) and limit image distortions due to long echo train durations. The hardware gradient constraints (magnitude, slew rate) must be taken into account to collect a sufficient amount of samples in a minimal amount of time. However, sampling strategies (e.g., Compressed Sensing) and optimal gradient waveform design have been developed separately so far. The major flaw of existing methods is that they do not take the sampling density into account, the latter being central in sampling theory. In particular, methods using optimal control tend to agglutinate samples in high curvature areas. In this paper, we develop an iterative algorithm to project any parameterization of k-space trajectories onto the set of feasible curves that fulfills the gradient constraints. We show that our projection algorithm provides a more efficient alternative than existinf approaches and that it can be a way of reducing acquisition time while maintaining sampling density for piece-wise linear trajectories.Lire moins >

Langue :

Anglais

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL