cartesian_espirit#

mrinufft.extras.smaps.cartesian_espirit(kspace: ndarray[tuple[int, ...], dtype[_ScalarType_co]], shape: tuple[int, ...], calib_width: int | tuple[int, ...] = 24, kernel_width: int | tuple[int, ...] = 6, thresh: float = 0.02, crop: float = 0.95, decim: int = 1) → ndarray[tuple[int, ...], dtype[_ScalarType_co]][source]#

ESPIRIT algorithm on Cartesian data.

Parameters:

  • kspace (NDArray) – The k-space data in Cartesian grid. Shape (n_coils, *kspace_shape)

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

  • calib_width (int or tuple of int, optional) – The calibration region width. By default it is 24.

  • kernel_width (int or tuple of int, optional) – The kernel width. By default it is 6.

  • thresh (float, optional) – The threshold for the singular values. By default it is 0.02.

  • crop (float, optional) – The cropping threshold for the sensitivity maps. By default it is 0.95.

  • decim (int, optional) – The decimation factor for the caluclation of sensitivity maps. By default it is 1. This can be used to speed up the computation and significantly reduce memory usage. The final result is upsampled back to the original size through linear interpolation.

Returns:

Smaps – The sensitivity maps

Return type:

NDArray

Returns:

Smaps – The sensitivity maps.

Return type:

numpy.ndarray

References

Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM, Vasanawala SS, Lustig M. ESPIRiT–an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn Reson Med. 2014 Mar;71(3):990-1001. doi: 10.1002/mrm.24751. PMID: 23649942; PMCID: PMC4142121.