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.Stacked3DNFFT(kspace_loc, shape, implementation='cpu', n_coils=1)[source]¶

” 3-D non uniform Fast Fourier Transform class, fast implementation for Stacked samples. Note that the kspace locations must be in the form of a stack along z, with same locations in each plane.

Attributes

samples	(np.ndarray) the mask samples in the Fourier domain.
shape	(tuple of int) shape of the image (necessarly a square/cubic matrix).
implementation	(string, ‘cpu’ or ‘gpuNUFFT’) string indicating which implemenmtation of Noncartesian FFT must be carried out
n_coils	(int default 1) Number of coils used to acquire the signal in case of multiarray receiver coils acquisition

adj_op(coeff)[source]¶

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

Parameters: coeff : np.ndarray

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

inverse 3D discrete Fourier transform of the input coefficients.

op(data)[source]¶

This method calculates Fourier transform.

Parameters: data : np.ndarray

input image as array.
Returns: result : np.ndarray

Forward 3D Fourier transform of the image.