Python Sparse data Analysis Package external MRI plugin.

This module contains tools to extract sensitivity maps from undersampled MR acquisition with high density in the k space center.

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

mri.reconstructors.utils.extract_sensitivity_maps.get_Smaps(k_space, img_shape, samples, thresh, min_samples, max_samples, mode='Gridding', method='linear', density_comp=None, n_cpu=1, fourier_op_kwargs=None)[source]¶

This method estimate the sensitivity maps information from parallel mri acquisition and for variable density sampling scheme where teh k-space center had been heavily sampled. Reference : Self-Calibrating Nonlinear Reconstruction Algorithms for Variable Density Sampling and Parallel Reception MRI https://ieeexplore.ieee.org/abstract/document/8448776

Parameters

k_space: np.ndarray

The acquired kspace of shape (M,L), where M is the number of samples acquired and L is the number of coils used

img_shape: tuple

The final output shape of Sensitivity Maps.

samples: np.ndarray

The sample locations where the above k_space data was acquired

thresh: tuple

The value of threshold in kspace for thresholding k-space center

min_samples: tuple

The minimum values in k-space where gridding must be done

max_samples: tuple

The maximum values in k-space where gridding must be done

mode: string ‘FFT’ | ‘NFFT’ | ‘gridding’ | ‘Stack’, default=’gridding’

Defines the mode in which we would want to interpolate, NOTE: FFT should be considered only if the input has been sampled on the grid

method: string ‘linear’ | ‘cubic’ | ‘nearest’, default=’linear’

For gridding mode, it defines the way interpolation must be done

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

n_cpu: int default=1

Number of parallel jobs in case of parallel MRI

fourier_op_kwargs: dict, default None

The keyword arguments given to fourier_op initialization if mode == ‘NFFT’. If None, we choose implementation of fourier op to ‘gpuNUFFT’ if library is installed.

Returns

Smaps: np.ndarray

the estimated sensitivity maps of shape (img_shape, L) with L the number of channels

ISOS: np.ndarray

The sum of Square used to extract the sensitivity maps