Abstract
A pseudorandom, velocity-insensitive, volumetric k-space sampling trajectory is designed for use with balanced steady-state magnetic resonance imaging. Individual arcs are designed independently and do not fit together in the way that multishot spiral, radial or echo-planar trajectories do. Previously, it was shown that second-order cone optimization problems can be defined for each arc independent of the others, that nulling of zeroth and higher moments can be encoded as constraints, and that individual arcs can be optimized in seconds. For use in steady-state imaging, sampling duty cycles are predicted to exceed 95 percent. Using such pseudorandom trajectories, aliasing caused by under-sampling manifests itself as incoherent noise. In this paper, a genetic algorithm (GA) is formulated and numerically evaluated. A large set of arcs is designed using previous methods, and the GA choses particular fit subsets of a given size, corresponding to a desired acquisition time. Numerical simulations of 1 second acquisitions show good detail and acceptable noise for large-volume imaging with 32 coils.
Highlights
Reconstruction of magnetic resonance imaging (MRI) from data sampled using noncartesian sampling has recently received increasingly mathematically sophisticated treatment, for example [1], with notable improvements in reconstruction speed and accuracy
The psf and numerical phantoms presented here show that Durga is a very promising approach to designing trajectories for volumetric imaging
Using the genetic algorithm (GA) increases the flexibility of this method and increases the quality of the solutions
Summary
Reconstruction of magnetic resonance imaging (MRI) from data sampled using noncartesian sampling has recently received increasingly mathematically sophisticated treatment, for example [1], with notable improvements in reconstruction speed and accuracy. In [2], a novel pseudorandom volumetric k-space trajectory design method was presented This methodology, referred to as Durga, combines a number of ideas in trajectory design and general sampling design for the first time, including randomness, [3, 4] constrained optimization [5] to balance trajectories for steady-state imaging [6,7,8,9], genetic algorithms [10], under-sampling to trade acquisition time for (structured) noise [11,12,13], and target-oriented design rather than patterns of symmetric interleaving [14, 15]. These numbers are relative to gradient peak/slew limits of 40 mTm−1 and 150 Tm−1s−1 for Spiral [5] and Durga [2] and 27 mTm−1 and 72 Tm−1s−1 for Teardrop [7]
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