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.gpuNUFFT(samples, shape, n_coils=1, density_comp=None, kernel_width=3, sector_width=8, osf=2, balance_workload=True, smaps=None)[source]¶

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

Attributes

samples	(np.ndarray) the normalized kspace location values in the Fourier domain.
shape	(tuple of int) shape of the image
operator	(The NUFFTOp object) to carry out operation
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(coeff, grid_data=False)[source]¶

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

Parameters

coeff : np.ndarray

masked non-uniform Fourier transform 1D data.

grid_data : bool, default False

if True, the kspace data is gridded and returned, this is used for density compensation

Returns

——-

np.ndarray

adjoint operator of Non Uniform Fourier transform of the input coefficients.

op(image, interpolate_data=False)[source]¶

This method calculates the masked non-cartesian Fourier transform of a 2D / 3D image.

Parameters

image : np.ndarray

input array with the same shape as shape.

interpolate_data : bool, default False

if set to True, the image is just apodized and interpolated to kspace locations. This is used for density estimation.

Returns

np.ndarray

Non Uniform Fourier transform of the input image.