MRICartesianOperator

class mrinufft.operators.cartesian.MRICartesianOperator(mask: ndarray[tuple[int, ...], dtype[_ScalarType_co]], shape: tuple[int, ...], density: Literal[False] = False, n_coils: int = 1, n_batchs: int = 1, n_trans: int = 1, smaps: ndarray[tuple[int, ...], dtype[_ScalarType_co]] | None = None, squeeze_dims: bool = True)

Bases: FourierOperatorSimple

Cartesian Operator for MRI reconstruction.

This class implements the forward and adjoint operations for Cartesian MRI reconstruction. It inherits from the FourierOperatorBase class, which provides common functionality for Fourier-based operators.

Methods

__init__	Initialize the MRICartesianOperator.
adj_op	Non Cartesian MRI adjoint operator.
check_shape	Validate the shapes of the image or k-space data against operator shapes.
compute_density	Density is not supported for Cartesian Operator.
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_deepinv_phy	Make a new DeepInv Physics with NUFFT operator.
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.
update_samples	Update the samples of the NUFFT 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.
img_full_shape	Full image shape with batch and coil dimensions.
interfaces
ksp_full_shape	Full kspace shape with batch and coil dimensions.
mask	Mask of the Cartesian sampling pattern.
n_batchs	Number of coils for the operator.
n_coils	Number of coils for the operator.
n_samples	Number of samples in the Cartesian sampling pattern.
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.

property mask

Mask of the Cartesian sampling pattern.

property n_samples

Number of samples in the Cartesian sampling pattern.

compute_density(method=None)

Density is not supported for Cartesian Operator.