compute_mfi_coefficients

compute_mfi_coefficients#

mrinufft.extras.field_map.compute_mfi_coefficients(field_map, readout_time, mask, L=9, n_bins=1024, lazy=True)[source]#

Compute off-resonance correction coefficients using Mixed-Frequency-Interpolator.

Parameters:

  • field_map (NDArray) – The field map (off-resonance map) in rad/s, If complex-valued, the real part is interpreted as R2* mapping. If real-valued this is the field inhomogeneities in Hz. and will be multiplied by \(2j\pi\)

  • readout_time (NDArray) – The vector of time points (in seconds) at which to compute phase evolution.

  • mask (NDArray) – Binary mask indicating object region for field map/statistics.

  • L (int, optional) – Number of virtual centers or basis functions retained (default is -1, automatically estimated).

  • n_bins (int, optional) – Number of histogram bins for off-resonance value clustering (default is 1000).

  • lazy (bool, default False) – If True, use a lazy evaluation scheme for the space interpolator C, saving memory.

Returns:

  • B (NDArray) – [L, nbins] phase basis matrix in the time domain.

  • C (NDArray) – [L, nt] interpolation matrix to transform weighted basis to phase at the time points; nt = len(readout_time).

  • E (NDArray) – [nbins, nt] exponential off-resonance phase matrix at input histogram bins.

References

Man, L.-C., Pauly, J.M. and Macovski, A. (1997), Multifrequency interpolation for fast off-resonance correction. Magn. Reson. Med., 37: 785-792. https://doi.org/10.1002/mrm.1910370523

Note

This function uses numpy for all CPU arrays, and cupy for all on-gpu array. It will convert all its array argument to the respective array library. The outputs will be converted back to the original array module and device.

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