sigpy.mri.kspace_precond

sigpy.mri.kspace_precond(mps, weights=None, coord=None, lamda=0, device=<CPU Device>, oversamp=1.25)[source]

Compute a diagonal preconditioner in k-space.

Considers the optimization problem:

\[\min_P \| P A A^H - I \|_F^2\]

where A is the Sense operator.

Parameters:
  • mps (array) – sensitivity maps of shape [num_coils] + image shape.
  • weights (array) – k-space weights.
  • coord (array) – k-space coordinates of shape […] + [ndim].
  • lamda (float) – regularization.
Returns:

k-space preconditioner of same shape as k-space.

Return type:

array