MRITorchKbNufft

class mrinufft.operators.interfaces.torchkbnufft.MRITorchKbNufft(samples, shape, density=False, n_coils=1, n_batchs=1, smaps=None, eps=1e-06, squeeze_dims=True, use_gpu=False, osf=2, **kwargs)

Bases: FourierOperatorBase

MRI Transform Operator using Torch NUFFT.

This class provides a Non-Uniform Fast Fourier Transform (NUFFT) operator for MRI data, utilizing the torchkbnufft library for performing the computations. It supports both CPU and GPU computations.

Parameters:

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

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

• density (bool or Tensor, optional) –

  Density compensation support. Default is False. - If a Tensor, it will be used for density. - If True, the density compensation will be automatically estimated

  using the fixed point method.

  • If False, density compensation will not be used.

• n_coils (int, optional) – Number of coils. Default is 1.

• n_batchs (int, optional) – Number of batches. Default is 1.

• smaps (Tensor, optional) – Sensitivity maps. Default is None.

• eps (float, optional) – A small epsilon value for numerical stability. Default is 1e-6.

• squeeze_dims (bool, optional) – If True, tries to remove singleton dimensions for batch and coils. Default is True.

• use_gpu (bool, optional) – Whether to use the GPU. Default is False.

• osf (int, optional) – Oversampling factor. Default is 2.

• **kwargs (dict) – Additional keyword arguments.

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.
op	Forward operation.
pipe	Compute the density compensation weights for a given set of kspace locations.
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
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, out=None)

Forward operation.

Parameters:

data (Tensor)

Returns:

Tensor

Return type:

Non-uniform Fourier transform of the input image.

adj_op(coeffs, out=None)

Backward Operation.

Parameters:

coeffs (Tensor)

Return type:

Tensor

_safe_squeeze(arr)

Squeeze the first two dimensions of shape of the operator.

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(kspace_loc, volume_shape, num_iterations=10, osf=2, normalize=True, use_gpu=False, **kwargs)

Compute the density compensation weights for a given set of kspace locations.

Parameters:

• kspace_loc (Tensor) – the kspace locations

• volume_shape (tuple) – the volume shape

• num_iterations (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

• use_gpu (bool, default False) – Whether to use the GPU