Python Sparse data Analysis Package external MRI plugin.

Note

This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the gallery for the big picture.

mri.reconstructors.utils.extract_sensitivity_maps.extract_k_space_center_and_locations(data_values, samples_locations, thr=None, img_shape=None, is_fft=False, density_comp=None)[source]¶

This class extract the k space center for a given threshold and extracts the corresponding sampling locations

Parameters

data_values : np.ndarray

The value of the samples

samples_locations : np.ndarray

The samples location in the k-sapec domain (between [-0.5, 0.5[)

thr : tuple or float

The threshold used to extract the k_space center

img_shape : tuple

The image shape to estimate the cartesian density

is_fft : bool default False

Checks if the incoming data is from FFT, in which case, masking can be done more directly

density_comp : np.ndarray default None

The density compensation for kspace data in case it exists and we use density compensated adjoint for Smap estimation

Returns

——-

The extracted center of the k-space, i.e. both the kspace locations and

kspace values. If the density compensators are passed, the corresponding

compensators for the center of k-space data will also be returned. The

return stypes for density compensation and kspace data is same as input