MRIfinufft

class mrinufft.operators.interfaces.finufft.MRIfinufft(samples, shape, density=False, n_coils=1, n_batchs=1, n_trans=1, smaps=None, squeeze_dims=True, **kwargs)

Bases: FourierOperatorCPU

MRI Transform Operator using finufft.

Parameters:

• samples (array) – The samples location of shape Nsamples x N_dimensions. It should be C-contiguous.

• shape (tuple) – Shape of the image space.

• n_coils (int) – Number of coils.

• n_batchs (int) – Number of batchs .

• n_trans (int) – Number of parallel transform

• density (bool or array) –

  Density compensation support.

  • If a Tensor, it will be used for the density.

  • If True, the density compensation will be automatically estimated, using the fixed point method.

  • If False, density compensation will not be used.

• smaps (array) – Sensitivity maps of shape N_coils x *shape.

• squeeze_dims (bool) – If True, the dimensions of size 1 for the coil and batch dimension will be squeezed.

Methods

__init__
adj_op	Non Cartesian MRI adjoint operator.
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 gradient data consistency.
get_lipschitz_cst	Return the Lipschitz constant of the operator.
make_autograd	Make a new Operator with autodiff support.
make_linops	Create a Scipy Linear Operator from the NUFFT operator.
op	Non Cartesian MRI forward operator.
pinv_solver	Solves the linear system Ax = y.
pipe	Compute the density compensation weights for a given set of kspace locations.
toggle_grad_traj	Toggle between the gradient trajectory and the plan for type 1 transform.
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.
img_full_shape	Full image shape with batch and coil dimensions.
interfaces
ksp_full_shape	Full kspace shape with batch and coil dimensions.
n_batchs	Number of coils for the operator.
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 operator uses sensitivity maps.

Examples using mrinufft.operators.interfaces.finufft.MRIfinufft

Off-resonance corrected NUFFT operator

Off-resonance corrected NUFFT operator

Minimal example script

Minimal example script

Subspace NUFFT Operator

Subspace NUFFT Operator

property samples: ndarray[tuple[int, ...], dtype[_ScalarType_co]]

Return the samples used by the operator.

toggle_grad_traj()

Toggle between the gradient trajectory and the plan for type 1 transform.

classmethod pipe(kspace_loc, volume_shape, max_iter=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

• max_iter (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