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.NUFFT(samples, shape, platform='cuda', Kd=None, Jd=None, n_coils=1, verbosity=0)[source]¶

GPU implementation of N-D non uniform Fast Fourrier Transform class.

Attributes

samples	(np.ndarray) the mask samples in the Fourier domain.
shape	(tuple of int) shape of the image (necessarly a square/cubic matrix).
nufftObj	(The pynufft object) depending on the required computational platform
platform	(string, ‘opencl’ or ‘cuda’) string indicating which hardware platform will be used to compute the NUFFT
Kd	(int or tuple) int or tuple indicating the size of the frequency grid, for regridding. if int, will be evaluated to (Kd,)*nb_dim of the image
Jd	(int or tuple) Size of the interpolator kernel. If int, will be evaluated to (Jd,)*dims image
n_coils	(int default 1) Number of coils used to acquire the signal in case of multiarray receiver coils acquisition. If n_coils > 1, please organize data as n_coils X data_per_coil

adj_op(x)[source]¶

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

Parameters: x : np.ndarray

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

inverse 3D discrete Fourier transform of the input coefficients.

numOfInstances = 0¶

op(img)[source]¶

This method calculates the masked non-cartesian Fourier transform of a 3-D image.

Parameters: img : np.ndarray

input 3D array with the same shape as shape.
Returns: x : np.ndarray

masked Fourier transform of the input image.