Efficient 3D Low-Discrepancy -Space Sampling Using Highly Adaptable Seiffert Spirals

The overall duration of acquiring a Nyquist sampled 3D dataset can be significantly shortened by enhancing the efficiency of k-space sampling. This can be achieved by increasing the coverage of k-space for every trajectory interleave. Furthermore, acceleration is possible by making use of advantageo...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2019-08, Vol.38 (8), p.1833-1840
Main Authors: Speidel, T., Metze, P., Rasche, V.
Format: Article
Language:English
Subjects:
Acceleration
compressed sensing
Cones
Datasets
efficiency
Image reconstruction
Jacobian elliptic functions
Jacobian matrices
Knee
low-discrepancy
Magnetic resonance imaging
Point spread functions
Sampling
spiral
Spirals
Three-dimensional displays
Trajectories
Trajectory
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Summary:The overall duration of acquiring a Nyquist sampled 3D dataset can be significantly shortened by enhancing the efficiency of k-space sampling. This can be achieved by increasing the coverage of k-space for every trajectory interleave. Furthermore, acceleration is possible by making use of advantageous undersampling properties. In this paper, a versatile 3D center-out k-space trajectory based on Jacobian elliptic functions (Seiffert's spiral) is presented. The trajectory leads to a low-discrepancy coverage of k-space using a considerably reduced number of readouts compared with other approaches. Such a coverage is achieved for any number of interleaves, and, therefore, even single-shot trajectories can be constructed to be combined with, for example, hyperpolarized media. Furthermore, acceleration is achievable due to non-coherent undersampling properties of the trajectory in combination with non-linear reconstruction techniques like compressed sensing (CS). Simulations of point-spread functions and discrepancy evaluations compare Seiffert's spiral to the established 3D cones approach. Imaging capabilities are evaluated by comparison of in vivo knee images using Nyquist and undersampled datasets in combination with CS reconstructions.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2018.2888695