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.
check_shape	Validate the shapes of the image or k-space data against operator shapes.
compute_density	Compute the density compensation weights and set it.
compute_smaps	Compute the sensitivity maps and set it.
data_consistency	Compute the data consistency estimation directly on gpu.
get_lipschitz_cst	Return the Lipschitz constant of the operator.
make_autograd	Make a new Operator with autodiff support.
op	Compute forward non-uniform Fourier Transform.
pipe	Compute the density compensation weights for a given set of kspace locations.
toggle_grad_traj	Toggle the gradient trajectory of the operator.
with_autograd	Return a Fourier operator with autograd capabilities.
with_off_resonance_correction	Return a new operator with Off Resonnance Correction.

Attributes

autograd_available
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.

property smaps

Sensitivity maps of the operator.

property samples

Return the samples used by the operator.

property density

Density compensation of the operator.

classmethod pipe(kspace_loc, volume_shape, num_iterations=10, osf=2, normalize=True, **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

• normalize (bool) – Whether to normalize the density compensation. We normalize such that the energy of PSF = 1

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.

data_consistency(image_data, obs_data)

Compute the data consistency estimation directly on gpu.

This mixes the op and adj_op method to perform F_adj(F(x-y)) on a per coil basis. By doing the computation coil wise, it uses less memory than the naive call to adj_op(op(x)-y)

Parameters:

• image (array) – Image on which the gradient operation will be evaluated. N_coil x Image shape is not using sense.

• obs_data (array) – Observed data.

toggle_grad_traj()

Toggle the gradient trajectory of the operator.