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Abstract #0765

Efficient 3D low-discrepancy $$$k$$$-space sampling using highly adaptable Seiffert Spirals

Tobias Speidel1, Patrick Metze2, and Volker Rasche2

1Core-Facility Small Animal Imaging (CF-SANI), Ulm University, Ulm, Germany, 2Experimental Cardiovascular MRI (ExCaVI), Ulm University Medical Center, Ulm, Germany

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. Further acceleration is possible by making use of advantageous undersampling properties.

This work presents a versatile 3D centre-out $$$k$$$-space trajectory, based on Jacobian elliptic functions (Seiffert's spiral). The trajectory leads to a low-discrepancy coverage of $$$k$$$-space, using a considerably reduced number of read-outs compared to other approaches. Such a coverage is achieved for any number of interleaves and therefore even single-shot trajectories can be constructed. Simulations and in-vivo studies compare Seiffert's spiral to the established 3D Cones approach.

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