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.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