MRICufiNUFFT

class mrinufft.operators.interfaces.cufinufft.MRICufiNUFFT(samples, shape, density=False, n_coils=1, n_batchs=1, smaps=None, smaps_cached=False, verbose=False, squeeze_dims=False, n_trans=1, **kwargs)

Bases: FourierOperatorBase

MRI Transform operator, build around cufinufft.

This operator adds density estimation and compensation (preconditioning) and multicoil support.

Parameters:

• samples (np.ndarray or GPUArray.) – The samples location of shape Nsamples x N_dimensions.

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

• n_coils (int) – Number of coils.

• n_batchs (int) – Size of the batch dimension.

• density (bool or array) –

  Density compensation support.

  • If array, use this for density compensation

  • If True, the density compensation will be automatically estimated, using the fixed point method.

  • If False, density compensation will not be used.

• smaps (np.ndarray or GPUArray , optional) –

  • If None: no Smaps wil be used.

  • If np.ndarray: Smaps will be copied on the device, according to smaps_cached.

  • If GPUArray, the smaps are already cached.

• smaps_cached (bool, default False) –

  • If False the smaps are copied on device and free at each iterations.

  • If True, the smaps are copied on device and stay on it.

• squeeze_dims (bool, default False) – If True, will try to remove the singleton dimension for batch and coils.

• n_trans (int, default 1) – Number of transform to perform in parallel by cufinufft.

• kwargs – Extra kwargs for the raw cufinufft operator

Notes

Cufinufft is able to run multiple transform in parallel, this is controlled by the n_trans parameter. The data provided should be of shape, (n_batch, n_coils, img_shape) for op (type2) and (n_batch, n_coils, n_samples) for adjoint (type1). and in contiguous memory order.

For now only single precision (float32 and complex64) is supported

See also

cufinufft.raw_operator.RawCufinufft

Methods

__init__
adj_op	Non Cartesian MRI adjoint operator.
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 estimation directly on gpu.
get_lipschitz_cst	Return the Lipschitz constant of the operator.
make_autograd	Make a new Operator with autodiff support.
op	Non Cartesian MRI forward operator.
toggle_grad_traj	Toggle between the gradient trajectory and the plan for type 1 transform.
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
bsize_img	Size in Bytes of the compute batch of images.
bsize_ksp	Size in Bytes of the compute batch of samples.
cpx_dtype	Return complex floating precision of the operator.
density	Density compensation of the operator.
dtype	Return floating precision of the operator.
eps	Return the underlying precision parameter.
img_size	Image size in bytes.
interfaces
ksp_size	k-space size in bytes.
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.

property smaps

Sensitivity maps of the operator.

property samples

Return the samples used by the operator.

property density

Density compensation of the operator.

op(data, ksp_d=None)

Non Cartesian MRI forward operator.

Parameters:

• data (np.ndarray or GPUArray)

• space. (The uniform (2D or 3D) data in image)

Return type:

Results array on the same device as data.

Notes

this performs for every coil ell: ..math:: mathcal{F}mathcal{S}_ell x

adj_op(coeffs, img_d=None)

Non Cartesian MRI adjoint operator.

Parameters:

coeffs (np.array or GPUArray)

Return type:

Array in the same memory space of coeffs. (ie on cpu or gpu Memory).

_adj_op_sense_device(coeffs, img_d=None)

Perform sense reconstruction when data is on device.

_adj_op_sense_host(coeffs, img_d=None)

Perform sense reconstruction when data is on host.

On device the following array are involved: - coil_img(S, T, 1, X,Y,Z) - ksp_batch(B, 1, X,Y,Z) - smaps_batched(S, T, X,Y,Z) - density_batched(T, K)

data_consistency(image_data, obs_data)

Compute the data consistency estimation directly on gpu.

This mixes the op and adj_op method to perform F_adj(F(x-y)) on a per coil basis. By doing the computation coil wise, it uses less memory than the naive call to adj_op(op(x)-y)

Parameters:

• image (array) – Image on which the gradient operation will be evaluated. N_coil x Image shape is not using sense.

• obs_data (array) – Observed data.

_dc_sense_host(image_data, obs_data)

Gradient computation when all data is on host.

_dc_sense_device(image_data, obs_data)

Gradient computation when all data is on device.

_dc_calibless_host(image_data, obs_data)

Calibrationless Gradient computation when all data is on host.

_dc_calibless_device(image_data, obs_data)

Calibrationless Gradient computation when all data is on device.

_safe_squeeze(arr)

Squeeze the first two dimensions of shape of the operator.

property eps

Return the underlying precision parameter.

property bsize_ksp

Size in Bytes of the compute batch of samples.

property bsize_img

Size in Bytes of the compute batch of images.

property img_size

Image size in bytes.

property ksp_size

k-space size in bytes.

property norm_factor

Norm factor of the operator.

get_lipschitz_cst(max_iter=10, **kwargs)

Return the Lipschitz constant of the operator.

Parameters:

• max_iter (int) – Number of iteration to perform to estimate the Lipschitz constant.

• kwargs – Extra kwargs for the cufinufft operator.

Returns:

Lipschitz constant of the operator.

Return type:

float

toggle_grad_traj()

Toggle between the gradient trajectory and the plan for type 1 transform.