Python Sparse data Analysis Package external MRI plugin.

Fourier operators for cartesian and non-cartesian space.

class mri.operators.fourier.cartesian.FFT(shape, n_coils=1, samples=None, mask=None, n_jobs=1)[source]¶

Standard unitary ND Fast Fourrier Transform class. The FFT will be normalized in a symmetric way

Attributes

samples: np.ndarray	the mask samples in the Fourier domain.
shape: tuple of int	shape of the image (not necessarly a square matrix).
n_coils: int, default 1	Number of coils used to acquire the signal in case of multiarray receiver coils acquisition. If n_coils > 1, data shape must be [n_coils, Nx, Ny, NZ]
n_jobs: int, default 1	Number of parallel workers to use for fourier computation

adj_op(x)[source]¶

This method calculates inverse masked Fourier transform of a ND image.

Parameters

x: np.ndarray

masked Fourier transform data. For multichannel images the coils dimension is put first

Returns

img: np.ndarray

inverse ND discrete Fourier transform of the input coefficients. For multichannel images the coils dimension is put first

op(img)[source]¶

This method calculates the masked Fourier transform of a ND image.

Parameters

img: np.ndarray

input ND array with the same shape as the mask. For multichannel images the coils dimension is put first

Returns

x: np.ndarray

masked Fourier transform of the input image. For multichannel images the coils dimension is put first