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International Journal of Biomedical Imaging
Volume 2008, Article ID 297089, 6 pages
Research Article

Random Volumetric MRI Trajectories via Genetic Algorithms

1Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada N6A 5C1
2Department of Computing and Software, McMaster University, Hamilton, ON, Canada L8S 4K1

Received 23 September 2007; Revised 5 April 2008; Accepted 22 May 2008

Academic Editor: Erik Meijering

Copyright © 2008 Andrew Thomas Curtis and Christopher Kumar Anand. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.