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.
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.
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	Forward operation.
pinv_solver	Solves the linear system Ax = y.
pipe	Estimate the density compensation using the pipe method.
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	Norm 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

Note

This function uses tensorflow internally, and will convert all its array argument to tensorflow tensors. but will respect the device they are allocated on. The outputs will be converted back to the original array module and device.

adj_op(coeffs)

Backward Operation.

Parameters:

coeffs (Tensor)

Return type:

Tensor

Note

This function uses tensorflow internally, and will convert all its array argument to tensorflow tensors. but will respect the device they are allocated on. The outputs will be converted back to the original array module and device.

property norm_factor

Norm factor of the operator.

data_consistency(image_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

Note

This function uses tensorflow internally, and will convert all its array argument to tensorflow tensors. but will respect the device they are allocated on. The outputs will be converted back to the original array module and device.

classmethod pipe(samples, shape, max_iter=10, osf=2, normalize=True)

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.

• max_iter (int) – Number of iterations.

• osf (int, default 2) – Currently, we support only OSF=2 and this value cannot be changed. Changing will raise an error.

Returns:

The estimated density compensation.

Return type:

Tensor