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Random volumetric MRI trajectories via genetic algorithms.

Andrew Thomas Curtis¹, Christopher Kumar Anand

¹Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada N6A 5C1. acurtis@imaging.robarts.ca

International Journal of Biomedical Imaging

|July 8, 2008

Summary

This study introduces a novel pseudorandom k-space sampling trajectory for faster, high-quality magnetic resonance imaging (MRI). The method uses a genetic algorithm to optimize sampling, reducing aliasing artifacts for clearer large-volume scans.

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Area of Science:

Medical Imaging
Magnetic Resonance Imaging (MRI)
Computational Imaging

Background:

Current MRI techniques face limitations in speed and resolution for volumetric imaging.
Aliasing artifacts due to under-sampling degrade image quality in fast MRI sequences.
Optimizing k-space trajectories is crucial for efficient data acquisition in steady-state MRI.

Purpose of the Study:

To design and evaluate a pseudorandom, velocity-insensitive, volumetric k-space sampling trajectory for balanced steady-state MRI.
To utilize a genetic algorithm (GA) for selecting optimal subsets of sampling arcs.
To assess the performance of the proposed trajectory in terms of image quality and acquisition time.

Main Methods:

Development of a pseudorandom k-space sampling trajectory with independent arc optimization.

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Encoding moment nulling constraints within second-order cone optimization problems for each arc.

Formulation and numerical evaluation of a genetic algorithm (GA) to select optimal arc subsets for desired acquisition times.

Simulation of 1-second acquisitions using the GA-selected trajectories with 32 coils.

Main Results:

The proposed pseudorandom trajectory allows for independent optimization of individual arcs.
High sampling duty cycles (>95%) are predicted for steady-state imaging.
Under-sampling manifests as incoherent noise rather than aliasing artifacts.
Numerical simulations demonstrate good image detail and acceptable noise levels for large-volume imaging within 1 second.

Conclusions:

The developed pseudorandom, velocity-insensitive trajectory, optimized via a genetic algorithm, offers a promising approach for rapid, high-quality volumetric MRI.
This method effectively mitigates aliasing artifacts, presenting them as manageable noise.
The technique shows potential for significantly improving the efficiency of large-volume steady-state MRI acquisitions.