MRIGpuNUFFT

class mrinufft.operators.interfaces.gpunufft.MRIGpuNUFFT(samples, shape, n_coils=1, n_batchs=1, n_trans=1, density=None, smaps=None, squeeze_dims=True, eps=0.001, **kwargs)

Bases: FourierOperatorBase

Interface for the gpuNUFFT backend.

Parameters:

• samples (np.ndarray (Mxd)) – the samples locations in the Fourier domain where M is the number of samples and d is the dimensionnality of the output data (2D for an image, 3D for a volume).

• 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

• density (bool or np.ndarray default None) – if True, the density compensation is estimated from the samples locations. If an array is passed, it is used as the density compensation.

• squeeze_dims (bool, default True) – If True, will try to remove the singleton dimension for batch and coils.

• smaps (np.ndarray default None) – Holds the sensitivity maps for SENSE reconstruction.

• n_trans (int, default =1) – This has no effect for now.

• kwargs (extra keyword args) – these arguments are passed to gpuNUFFT operator. This is used only in gpuNUFFT

Methods

__init__
adj_op	Compute adjoint Non Uniform Fourier Transform.
compute_density	Compute the density compensation weights and set it.
data_consistency	Compute the gradient data consistency.
get_lipschitz_cst	Return the Lipschitz constant of the operator.
op	Compute forward non-uniform Fourier Transform.
pipe	Compute the density compensation weights for a given set of kspace locations.
with_off_resonnance_correction	Return a new operator with Off Resonnance Correction.

Attributes

available
backend
cpx_dtype	Return complex floating precision of the operator.
density	Density compensation of the operator.
dtype	Return floating precision of the operator.
interfaces
n_coils	Number of coils for the operator.
n_samples	Return the number of samples used by the operator.
ndim	Number of dimensions in image space of the operator.
norm_factor	Normalization factor of the operator.
samples	Return the samples used by the operator.
shape	Shape of the image space of the operator.
smaps	Sensitivity maps of the operator.
uses_density	Return True if the operator uses density compensation.
uses_sense	Return True if the Fourier Operator uses the SENSE method.

op(data, coeffs=None)

Compute forward non-uniform Fourier Transform.

Parameters:

• img (np.ndarray) – input N-D array with the same shape as self.shape.

• coeffs (np.ndarray, optional) – output Array. Should be pinned memory for best performances.

Returns:

Masked Fourier transform of the input image.

Return type:

np.ndarray

adj_op(coeffs, data=None)

Compute adjoint Non Uniform Fourier Transform.

Parameters:

• coeffs (np.ndarray) – masked non-uniform Fourier transform 1D data.

• data (np.ndarray, optional) – output image array. Should be pinned memory for best performances.

Returns:

Inverse discrete Fourier transform of the input coefficients.

Return type:

np.ndarray

property uses_sense

Return True if the Fourier Operator uses the SENSE method.

classmethod pipe(kspace_loc, volume_shape, num_iterations=10, osf=2, **kwargs)

Compute the density compensation weights for a given set of kspace locations.

Parameters:

• kspace_loc (np.ndarray) – the kspace locations

• volume_shape (np.ndarray) – the volume shape

• num_iterations (int default 10) – the number of iterations for density estimation

• osf (float or int) – The oversampling factor the volume shape

get_lipschitz_cst(max_iter=10, tolerance=1e-05, **kwargs)

Return the Lipschitz constant of the operator.

tolerance: float, optional default 1e-5

Tolerance for the spectral radius estimation.

kwargs:

Extra kwargs for the operator.

Returns:

Lipschitz constant of the operator.

Return type:

float

_safe_squeeze(arr)

Squeeze the first two dimensions of shape of the operator.