Python Sparse data Analysis Package external MRI plugin.

Note

This page is a reference documentation. It only explains the class signature, and not how to use it. Please refer to the gallery for the big picture.

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