Optimal design of k-space trajectories using a multi-objective genetic algorithm

3D variable-density SPARKLING trajectories for high-resolution T2*-weighted magnetic resonance imaging

We have recently proposed a new optimization algorithm called SPARKLING (Spreading Projection Algorithm for Rapid K-space sampLING) to design efficient compressive sampling patterns for magnetic resonance imaging (MRI). This method has a few advantages over conventional non-Cartesian trajectories such as radial lines or spirals: i) it allows to sample the k-space along any arbitrary density while the other two are restricted to radial densities and ii) it optimizes the gradient waveforms for a given readout time. Here, we introduce an extension of the SPARKLING method for 3D imaging by considering both stacks-of-SPARKLING and fully 3D SPARKLING trajectories. Our method allowed to achieve an isotropic resolution of 600 μm in just 45 seconds for T2∗-weighted ex vivo brain imaging at 7 Tesla over a field-of-view of 200 × 200 × 140 mm³ . Preliminary in vivo human brain data shows that a stack-of-SPARKLING is less subject to off-resonance artifacts than a stack-of-spirals.

Design and optimization of pulsed Chemical Exchange Saturation Transfer MRI using a multiobjective genetic algorithm

Pulsed Chemical Exchange Saturation Transfer (CEST) MRI experimental parameters and RF saturation pulse shapes were optimized using a multiobjective genetic algorithm. The optimization was carried out for RF saturation duty cycles of 50% and 90%, and results were compared to continuous wave saturation and Gaussian waveform. In both simulation and phantom experiments, continuous wave saturation performed the best, followed by parameters and shapes optimized by the genetic algorithm and then followed by Gaussian waveform. We have successfully demonstrated that the genetic algorithm is able to optimize pulse CEST parameters and that the results are translatable to clinical scanners.

Trajectory optimization based on the signal-to-noise ratio for spatial encoding with nonlinear encoding fields

PURPOSE

Multiple nonlinear gradient fields offer many potential benefits for spatial encoding including reduced acquisition time, fewer artefacts and region-specific imaging, although designing a suitable trajectory for such a setup is difficult. This work aims to optimize encoding trajectories for multiple nonlinear gradient fields based on the image signal-to-noise ratio.

THEORY AND METHODS

Image signal-to-noise ratio is directly linked to the covariance of the reconstructed pixels, which can be calculated recursively for each projection of the trajectory under a Bayesian formulation. An evolutionary algorithm is used to find the higher-dimensional projections that minimize the pixel covariance, incorporating receive coil profiles, intravoxel dephasing, and reconstruction regularization. The resulting trajectories are tested through simulations and experiments.

RESULTS

The optimized trajectories produce images with higher resolution and fewer artefacts compared with traditional approaches, particularly for high undersampling. However, higher-dimensional projection experiments strongly depend on accurate hardware and calibration.

CONCLUSION

Computer-based optimization provides an efficient means to explore the large trajectory space created by the use of multiple nonlinear encoding fields. The optimization framework, as presented here, is necessary to fully exploit the advantages of nonlinear fields. Magn Reson Med 76:104-117, 2016. © 2015 Wiley Periodicals, Inc.

Random volumetric MRI trajectories via genetic algorithms

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.

A fast method for designing time-optimal gradient waveforms for arbitrary k-space trajectories

A fast and simple algorithm for designing time-optimal waveforms is presented. The algorithm accepts a given arbitrary multidimensional k-space trajectory as the input and outputs the time-optimal gradient waveform that traverses k-space along that path in minimum time. The algorithm is noniterative, and its run time is independent of the complexity of the curve, i.e., the number of switches between slew-rate limited acceleration, slew-rate limited deceleration, and gradient amplitude limited regions. The key in the method is that the gradient amplitude is designed as a function of arc length along the k-space trajectory, rather than as a function of time. Several trajectory design examples are presented.

Sectoral sampling in centric-scan SPRITE magnetic resonance imaging

A new approach to the construction of k-space trajectories for centric-scan SPRITE in both 2D and 3D is presented. All benefits of previous SPRITE methods are retained, most importantly the ability to image objects with short T*(2). This new approach gives more flexibility in the choice of number of interleaves with points more evenly distributed across k-space. All these improvements positively contribute to image quality and resolution, which can be also traded off against experimental speed. Sectoral sampling will have significant benefits for magnetisation preparation contrast imaging.

Consistent non-cartesian off-axis MRI quality: Calibrating and removing multiple sources of demodulation phase errors

The consistency of off-axis MRI with non-Cartesian sequences across a large number of scanners is highly variable. Improper timing alignment of the gradient fields, data acquisition system, and real-time frequency demodulation reference signal, which are necessary for off-axis imaging, is an important source of this variability. In addition, eddy currents and anisotropic gradient delays cause deviations in k-space trajectories that in turn make the demodulation reference signals inaccurate. A method is presented to quickly measure the timing error in the frequency demodulation reference signal and separate it from anisotropic gradient delays. k-Space deviations, as measured with a previous gradient calibration technique, are shown to be a second source of demodulation phase errors that degrade image quality. Using the timing delay and k-space deviations, a retrospective phase correction is applied to each k-space sample before the data are regridded during reconstruction. The timing delays of four MR scanners were measured to be 4.2-7.5 micros below the manufacturer's suggested delay. Significant degradation in 3D radial (3D projection reconstruction (PR)) knee and breast images are retrospectively corrected while a partial prospective correction is applied for spiral imaging. The method allows for more consistent performance of non-Cartesian sequences across multiple scanners without operator intervention.