MRIFourierCorrected

class mrinufft.operators.off_resonnance.MRIFourierCorrected(fourier_op, B, C, indices, backend='cpu')

Bases: FourierOperatorBase

Fourier Operator with B0 Inhomogeneities compensation.

This is a wrapper around the Fourier Operator to compensate for the B0 inhomogeneities in the k-space.

Parameters:

• fourier_op (object of class FourierBase) – the fourier operator to wrap

• B (numpy.ndarray)

• C (numpy.ndarray)

• indices (numpy.ndarray)

• backend (str, default 'cpu') – the backend to use for computations. Either ‘cpu’ or ‘gpu’.

Methods

__init__
adj_op	Compute Adjoint Operation with off-resonnance effect.
compute_density	Compute the density compensation weights and set it.
data_consistency	Compute the gradient data consistency.
get_grad	Compute the data consistency error.
get_lipschitz_cst	Return the Lipschitz constant of the operator.
op	Compute Forward Operation with off-resonnances effect.
with_off_resonnance_correction	Return a new operator with Off Resonnance Correction.

Attributes

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 operator uses sensitivity maps.

op(data, *args)

Compute Forward Operation with off-resonnances effect.

Parameters:

x (numpy.ndarray or cupy.ndarray) – N-D input image

Returns:

masked distorded N-D k-space

Return type:

numpy.ndarray or cupy.ndarray

adj_op(coeffs, *args)

Compute Adjoint Operation with off-resonnance effect.

Parameters:

x (numpy.ndarray or cupy.ndarray) – masked distorded N-D k-space

Return type:

inverse Fourier transform of the distorded input k-space.

get_grad(image_data, obs_data)

Compute the data consistency error.

Parameters:

• image_data (numpy.ndarray or cupy.ndarray) – N-D input image

• obs_data (numpy.ndarray or cupy.ndarray) – N-D observed k-space

Returns:

data consistency error in image space.

Return type:

numpy.ndarray or cupy.ndarray