initialize_3D_turbine

initialize_3D_turbine#

mrinufft.trajectories.trajectory3D.initialize_3D_turbine(Nc, Ns_readouts, Ns_transitions, nb_blades, blade_tilt='uniform', nb_trains='auto', skip_factor=1, in_out=True)[source]#

Initialize 3D TURBINE trajectory.

This is an implementation of the TURBINE (Trajectory Using Radially Batched Internal Navigator Echoes) trajectory proposed in [MGM10]. It consists of EPI-like multi-echo planes rotated around any axis (here \(k_z\)-axis) in a radial fashion.

Note that our implementation also proposes to segment the planes into several shots instead of just one, and includes the proposition from [GMC22] to also accelerate within the blades by skipping lines but while alternating them between blades.

Parameters:

  • Nc (int) – Number of shots

  • Ns_readouts (int) – Number of samples per readout

  • Ns_transitions (int) – Number of samples per transition between two readouts

  • nb_blades (int) – Number of line stacks over the \(k_z\)-axis axis

  • blade_tilt (str, float, optional) – Tilt between individual blades, by default “uniform”

  • nb_trains (int, str, optional) – Number of resulting shots, or readout trains, such that each of them will be composed of \(n\) readouts with Nc = n * nb_trains. If "auto" then nb_trains is set to nb_blades.

  • skip_factor (int, optional) – Factor defining the way different blades alternate to skip lines, forming groups of skip_factor non-redundant blades.

  • in_out (bool, optional) – Whether the curves are going in-and-out or start from the center

Returns:

3D TURBINE trajectory

Return type:

array_like

References

[MGM10]

McNab, Jennifer A., Daniel Gallichan, and Karla L. Miller. “3D steady‐state diffusion‐weighted imaging with trajectory using radially batched internal navigator echoes (TURBINE).” Magnetic Resonance in Medicine 63, no. 1 (2010): 235-242.

[GMC22]

Graedel, Nadine N., Karla L. Miller, and Mark Chiew. “Ultrahigh resolution fMRI at 7T using radial‐cartesian TURBINE sampling.” Magnetic Resonance in Medicine 88, no. 5 (2022): 2058-2073.

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