low_frequency#

mrinufft.extras.smaps.low_frequency(traj: ndarray[tuple[int, ...], dtype[_ScalarType_co]], shape: tuple[int, ...], kspace_data: ndarray[tuple[int, ...], dtype[_ScalarType_co]], backend: str, threshold: float | tuple[float, ...] = 0.1, density: ndarray[tuple[int, ...], dtype[_ScalarType_co]] | None = None, max_iter: int = 10, window_fun: str | Callable[[ndarray[tuple[int, ...], dtype[_ScalarType_co]]], ndarray[tuple[int, ...], dtype[_ScalarType_co]]] = 'ellipse', blurr_factor: int | float | tuple[float, ...] = 0.0, mask: bool | ndarray[tuple[int, ...], dtype[_ScalarType_co]] = False) → ndarray[tuple[int, ...], dtype[_ScalarType_co]][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.

  • max_iter (int, optional) – The max iterations for internal pinv computations

  • 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 – The sensitivity maps.

Return type:

numpy.ndarray

References

Loubna El Gueddari, C. Lazarus, H Carrié, A. Vignaud, Philippe Ciuciu. Self-calibrating nonlinear reconstruction algorithms for variable density sampling and parallel reception MRI. 10th IEEE Sensor Array and Multichannel Signal Processing workshop, Jul 2018, Sheffield, United Kingdom. ⟨hal-01782428v1⟩