Gradient waveform design for variable density sampling in Magnetic Resonance Imaging

Abstract : 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.
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Contributeur : Nicolas Chauffert <>
Soumis le : mardi 30 décembre 2014 - 11:27:43
Dernière modification le : vendredi 16 mars 2018 - 01:13:48
Document(s) archivé(s) le : samedi 15 avril 2017 - 12:00:20


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  • HAL Id : hal-01095320, version 2
  • ARXIV : 1412.4621


Nicolas Chauffert, Pierre Weiss, Jonas Kahn, Philippe Ciuciu. Gradient waveform design for variable density sampling in Magnetic Resonance Imaging. 2014. 〈hal-01095320v2〉



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