MRITensorflowNUFFT

class mrinufft.operators.interfaces.tfnufft.MRITensorflowNUFFT(samples, shape, n_coils=1, density=False, smaps=None, eps=1e-06)

Bases: FourierOperatorBase

MRI Transform Operator using Tensorflow NUFFT.

Parameters:

• samples (Tensor) – 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.

• density (bool or Tensor) –

  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 (Tensor)

Methods

__init__
adj_op	Backward Operation.
compute_density	Compute the density compensation weights and set it.
data_consistency	Compute the data consistency.
get_lipschitz_cst	Return the Lipschitz constant of the operator.
op	Forward operation.
pipe	Estimate the density compensation using the pipe method.
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 operator uses sensitivity maps.

op(data)

Forward operation.

Parameters:

data (Tensor)

Return type:

Tensor

adj_op(data)

Backward Operation.

Parameters:

data (Tensor)

Return type:

Tensor

data_consistency(data, obs_data)

Compute the data consistency.

Parameters:

• data (Tensor) – Image data

• obs_data (Tensor) – Observed data

Returns:

The data consistency error in image space.

Return type:

Tensor

classmethod pipe(shape, n_iter=15)

Estimate the density compensation using the pipe method.

Parameters:

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

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

• n_iter (int) – Number of iterations.

Returns:

The estimated density compensation.

Return type:

Tensor