scgrog

Self calibrating GROG implementation.

Based on the MATLAB GROG implementation found here: https://github.com/edibella/Reconstruction

mr_utils.gridding.scgrog.scgrog.fracpowers(idx, Gx, Gy, dkxs, dkys)

Wrapper function to use during parallelization.

Parameters:

• idx (array_like) – Indices of current fractioinal power GRAPPA kernels
• Gx (array_like) – GRAPPA kernel in x
• Gy (array_like) – GRAPPA kernel in y
• dkxs (array_like) – Differential x k-space coordinates
• dkys (array_like) – Differential y k-space coordinates

Returns:

• ii (array_like) – row indices
• jj (array_like) – col indices
• Gxf (array_like) – Fractional power of GRAPPA kernel in x
• Gyf (array_like) – Fractional power of GRAPPA kernel in y

mr_utils.gridding.scgrog.scgrog.grog_interp(kspace, Gx, Gy, traj, cartdims)

Moves radial k-space points onto a cartesian grid via the GROG method.

Parameters:

• kspace (array_like) – A 3D (sx, sor, soc) slice of k-space
• Gx (array_like) – The unit horizontal cartesian GRAPPA kernel
• Gy (array_like) – Unit vertical cartesian GRAPPA kernel
• traj (array_like) – k-space trajectory
• cartdims (tuple) – (nrows, ncols), size of Cartesian grid

Returns:

Interpolated cartesian kspace.

Return type:

array_like

mr_utils.gridding.scgrog.scgrog.scgrog(kspace, traj, Gx, Gy, cartdims=None)

Self calibrating GROG interpolation.

Parameters:

• kspace (array_like) – A 4D (sx, sor, nof, soc) matrix of complex k-space data
• traj (array_like) – k-space trajectory
• Gx (array_like) – The unit horizontal cartesian GRAPPA kernel
• Gy (array_like) – Unit vertical cartesian GRAPPA kernel
• cartdims (tuple) – Size of Cartesian grid.

Returns:

• kspace_cart (array_like) – Cartesian gridded k-space.
• mask (array_like) – Boolean mask where kspace is nonzero.

Notes

If cartdims=None, we’ll guess the Cartesian dimensions are (kspace.shape[0], kspace.shape[0], kspace.shape[2], kspace.shape[3]).