Python Sparse data Analysis Package external MRI plugin.

This function calculates the gridded Inverse fourier transform from Interpolated non-Cartesian data into a cartesian grid. However, the IFFT is done similar to Stacked Fourier transform. We expect the kspace data to be limited to a grid on z, we calculate the inverse fourier transform by- 1) Grid data in each plane (for all points in a plane) 2) Interpolate data along z, if we have undersampled data along z 3) Apply an IFFT on the 3D data that was gridded and interpolated in z.

Parameters

kspace_data_sorted : np.ndarray

The sorted k-space data corresponding to kspace_plane_loc above

kspace_plane_loc : np.ndarray

The N-D k_space locations of size [M, N]. These hold locations only in plane, extracted using get_stacks_fourier function

idx_mask_z : np.ndarray

contains the indices of the acquired Fourier plane. Extracted using get_stacks_fourier function

grid : tuple

The Gridded matrix for which you want to calculate k_space Smaps. Should be given as a tuple of ndarray

volume_shape : tuple

Reconstructed volume shape

method : {‘linear’, ‘nearest’, ‘cubic’}, optional

Method of interpolation for more details see scipy.interpolate.griddata documentation

Returns

np.ndarray

The gridded inverse fourier transform of given kspace data