MRISubspace

class mrinufft.operators.subspace.MRISubspace(fourier_op, subspace_basis, use_gpu=False)

Bases: FourierOperatorBase

Fourier Operator with subspace projection.

This is a wrapper around the Fourier Operator to project data onto a low-rank subspace (e.g., dynamic and multi-contrast MRI).

Parameters:

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

• subspace_basis (np.ndarray) – Low rank subspace basis of shape (K, T), where K is the rank of the subspace and T is the number of time frames or contrasts in the original image series. Also supports Cupy arrays and Torch tensors.

• use_gpu (bool, optional) – Whether to use the GPU. Default is False. Ignored if the Fourier operator internally use only GPU (e.g., Cupy) or CPU (e.g., Numpy)

Notes

This extension adds a new axis for both image and k-space data:

• Image: (B, C, XYZ) -> (B, S, C, XYZ)

• K-Space: (B, C, K) -> (B, T, C, K)

with S representing the subspace index and T representing time domain or contrast space (for dynamic and multi-contrast MR, respectively).

Similarly, k-space trajectory is expected to have the following shape: (<N_frames or N_contrasts>, N_shots, N_samples, dim). The flatten version is also accepted: (<N_frames or N_contrasts>, N_shots * N_samples, dim)

Methods

__init__
adj_op	Compute Adjoint Operation with subspace projection.
cg	Conjugate Gradient method to solve the inverse problem.
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 gradient data consistency.
get_lipschitz_cst	Return the Lipschitz constant of the operator.
make_autograd	Make a new Operator with autodiff support.
op	Compute Forward Operation time/contrast-domain backprojection.
with_autograd	Return a Fourier operator with autograd capabilities.
with_off_resonance_correction	Return a new operator with Off Resonnance Correction.

Attributes

autograd_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_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	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.
backend
available

Examples using mrinufft.operators.subspace.MRISubspace

Subspace NUFFT Operator

Subspace NUFFT Operator

op(data, *args)

Compute Forward Operation time/contrast-domain backprojection.

Parameters:

data (numpy.ndarray) – N-D subspace-projected image. Also supports Cupy arrays and Torch tensors.

Returns:

Time/contrast-domain k-space data.

Return type:

numpy.ndarray

adj_op(coeffs, *args)

Compute Adjoint Operation with subspace projection.

Parameters:

coeffs (numpy.ndarray) – Time/contrast-domain k-space data. Also supports Cupy arrays and Torch tensors.

Returns:

Inverse Fourier transform of the subspace-projected k-space.

Return type:

numpy.ndarray

property n_samples

Return the number of samples used by the operator.