pygrappa.tgrappa

TGRAPPA implementation.

pygrappa.tgrappa.tgrappa(kspace, calib_size=(20, 20), kernel_size=(5, 5), coil_axis=-2, time_axis=-1)[source]

Temporal GRAPPA.

Parameters:
  • kspace (array_like) – 2+1D multi-coil k-space data to reconstruct from (total of 4 dimensions). Missing entries should have exact zeros in them.
  • calib_size (array_like, optional) – Size of calibration region at the center of kspace.
  • kernel_size (tuple, optional) – Desired shape of the in-plane calibration regions: (kx, ky).
  • coil_axis (int, optional) – Dimension holding coil data.
  • time_axis (int, optional) – Dimension holding time data.
Returns:

res – Reconstructed k-space data.
Return type:

array_like
Raises:

ValueError – When no complete ACS region can be found.

Notes

Implementation of the method proposed in [1].

The idea is to form ACS regions using data from adjacent time frames. For example, in the case of 1D undersampling using undersampling factor R, at least R time frames must be merged to form a completely sampled ACS. Then we can simply supply the undersampled data and the synthesized ACS to GRAPPA. Thus the heavy lifting of this function will be in determining the ACS calibration region at each time frame.

References

[1]Breuer, Felix A., et al. “Dynamic autocalibrated parallel imaging using temporal GRAPPA (TGRAPPA).” Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 53.4 (2005): 981-985.