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RawGpuNUFFT

RawGpuNUFFT#

class mrinufft.operators.interfaces.gpunufft.RawGpuNUFFT(samples, shape, n_coils=1, density_comp=None, kernel_width=3, sector_width=8, osf=2, upsampfac=None, balance_workload=True, smaps=None, pinned_smaps=None, pinned_image=None, pinned_kspace=None, use_gpu_direct=False)[source]#

Bases: object

GPU implementation of N-D non-uniform fast Fourier Transform class.

samples#

the normalized kspace location values in the Fourier domain.

Type:

np.ndarray

shape#

shape of the image

Type:

tuple of int

operator#

to carry out operation

Type:

The NUFFTOp object

n_coils#

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

Type:

int default 1

Methods

__init__

Initialize the 'NUFFT' class.

adj_op

Compute adjoint of non-uniform Fourier transform.

adj_op_direct

Compute adjoint of non-uniform Fourier transform.

op

Compute the masked non-Cartesian Fourier transform.

op_direct

Compute the masked non-Cartesian Fourier transform.
_reshape_image(image, direction='op')[source]#

Reshape the image to the correct format.

op_direct(image, kspace=None, interpolate_data=False)[source]#

Compute the masked non-Cartesian Fourier transform.

The incoming data is on GPU already and we return a GPU array.

Parameters:

  • image (np.ndarray) – input array with the same shape as self.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:

Non-uniform Fourier transform of the input image.

Return type:

cp.ndarray

op(image, kspace=None, interpolate_data=False)[source]#

Compute the masked non-Cartesian Fourier transform.

Parameters:

  • image (np.ndarray) – input array with the same shape as self.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:

Non-uniform Fourier transform of the input image.

Return type:

np.ndarray

adj_op(coeffs, image=None, grid_data=False)[source]#

Compute adjoint of non-uniform Fourier transform.

Parameters:

  • coeff (np.ndarray) – masked non-uniform Fourier transform data.

  • grid_data (bool, default False) – if True, the kspace data is gridded and returned, this is used for density compensation

Returns:

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

Return type:

np.ndarray

adj_op_direct(coeffs, image=None, grid_data=False)[source]#

Compute adjoint of non-uniform Fourier transform.

Parameters:

  • coeff (np.ndarray) – masked non-uniform Fourier transform data.

  • grid_data (bool, default False) – if True, the kspace data is gridded and returned, this is used for density compensation

Returns:

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

Return type:

np.ndarray

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