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.non_cartesian.NFFT(samples, shape, n_coils=1)[source]¶

ND non catesian Fast Fourrier Transform class The NFFT will normalize like the FFT i.e. in a symetric way. This means that both direct and adjoint operator will be divided by the square root of the number of samples in the fourier domain.

Attributes

samples	(np.ndarray) the samples locations in the Fourier domain between [-0.5; 0.5[.
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

adj_op(x)[source]¶

This method calculates inverse masked non-cartesian Fourier transform of a 1-D coefficients array.

Parameters: x : np.ndarray

masked non-cartesian Fourier transform 1D data.
Returns: img : np.ndarray

inverse 2D discrete Fourier transform of the input coefficients.

op(img)[source]¶

This method calculates the masked non-cartesian Fourier transform of a N-D data.

Parameters: img : np.ndarray

input ND array with the same shape as the mask.
Returns: x : np.ndarray

masked Fourier transform of the input image.