low_frequency

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low_frequency#

mrinufft.extras.smaps.low_frequency(traj, shape, kspace_data, backend, threshold=0.1, density=None, window_fun='ellipse', blurr_factor=0.0, mask=False)[source]#

Calculate low-frequency sensitivity maps.

Parameters:

  • traj (numpy.ndarray) – The trajectory of the samples.

  • shape (tuple) – The shape of the image.

  • kspace_data (numpy.ndarray) – The k-space data.

  • threshold (float, or tuple of float, optional) – The threshold used for extracting the k-space center. By default it is 0.1

  • backend (str) – The backend used for the operator.

  • density (numpy.ndarray, optional) – The density compensation weights.

  • window_fun ("Hann", "Hanning", "Hamming", or a callable, default None.) – The window function to apply to the selected data. It is computed with the center locations selected. Only works with circular mask. If window_fun is a callable, it takes as input the array (n_samples x n_dims) of sample positions and returns an array of n_samples weights to be applied to the selected k-space values, before the smaps estimation.

  • blurr_factor (float or list, optional) – The blurring factor for smoothing the sensitivity maps. Applies a gaussian filter on the Smap images to get smoother Sensitivty maps. By default it is 0.0, i.e. no smoothing is done

  • mask (bool, optional default False) – Whether the Sensitivity maps must be masked

Returns:

  • Smaps (numpy.ndarray) – The low-frequency sensitivity maps.

  • SOS (numpy.ndarray) – The sum of squares of the sensitivity maps.

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