from typing import List, Optional, Tuple
from torch import Tensor
from .._nufft.fft import fft_and_scale, ifft_and_scale
from .interp import (
kb_spmat_interp,
kb_spmat_interp_adjoint,
kb_table_interp,
kb_table_interp_adjoint,
)
[docs]def kb_spmat_nufft(
image: Tensor,
scaling_coef: Tensor,
im_size: Tensor,
grid_size: Tensor,
interp_mats: Tuple[Tensor, Tensor],
norm: Optional[str] = None,
) -> Tensor:
"""Kaiser-Bessel NUFFT with sparse matrix interpolation.
See :py:class:`~torchkbnufft.KbNufft` for an overall description of
the forward NUFFT.
To calculate the sparse matrix tuple, see
:py:meth:`~torchkbnufft.calc_tensor_spmatrix`.
Args:
image: Image to be NUFFT'd to scattered data.
scaling_coef: Image-domain coefficients to pre-compensate for
interpolation errors.
im_size: Size of image with length being the number of dimensions.
grid_size: Size of grid to use for interpolation, typically 1.25 to 2
times ``im_size``.
interp_mats: 2-tuple of real, imaginary sparse matrices to use for
sparse matrix KB interpolation.
norm: Whether to apply normalization with the FFT operation. Options
are ``"ortho"`` or ``None``.
Returns:
``image`` calculated at scattered Fourier locations.
"""
image = fft_and_scale(
image=image,
scaling_coef=scaling_coef,
im_size=im_size,
grid_size=grid_size,
norm=norm,
)
return kb_spmat_interp(
image=image,
interp_mats=interp_mats,
)
[docs]def kb_spmat_nufft_adjoint(
data: Tensor,
scaling_coef: Tensor,
im_size: Tensor,
grid_size: Tensor,
interp_mats: Tuple[Tensor, Tensor],
norm: Optional[str] = None,
) -> Tensor:
"""Kaiser-Bessel adjoint NUFFT with sparse matrix interpolation.
See :py:class:`~torchkbnufft.KbNufftAdjoint` for an overall description of
the adjoint NUFFT.
To calculate the sparse matrix tuple, see
:py:meth:`~torchkbnufft.calc_tensor_spmatrix`.
Args:
data: Scattered data to be iNUFFT'd to an image.
scaling_coef: Image-domain coefficients to compensate for interpolation
errors.
im_size: Size of image with length being the number of dimensions.
grid_size: Size of grid to use for interpolation, typically 1.25 to 2
times ``im_size``.
interp_mats: 2-tuple of real, imaginary sparse matrices to use for
sparse matrix KB interpolation.
norm: Whether to apply normalization with the FFT operation. Options
are ``"ortho"`` or ``None``.
Returns:
``data`` transformed to an image.
"""
data = kb_spmat_interp_adjoint(
data=data, interp_mats=interp_mats, grid_size=grid_size
)
return ifft_and_scale(
image=data,
scaling_coef=scaling_coef,
im_size=im_size,
grid_size=grid_size,
norm=norm,
)
[docs]def kb_table_nufft(
image: Tensor,
scaling_coef: Tensor,
im_size: Tensor,
grid_size: Tensor,
omega: Tensor,
tables: List[Tensor],
n_shift: Tensor,
numpoints: Tensor,
table_oversamp: Tensor,
offsets: Tensor,
norm: Optional[str] = None,
) -> Tensor:
"""Kaiser-Bessel NUFFT with table interpolation.
See :py:class:`~torchkbnufft.KbNufft` for an overall description of
the forward NUFFT.
Args:
image: Image to be NUFFT'd to scattered data.
scaling_coef: Image-domain coefficients to pre-compensate for
interpolation errors.
im_size: Size of image with length being the number of dimensions.
grid_size: Size of grid to use for interpolation, typically 1.25 to 2
times ``im_size``.
omega: k-space trajectory (in radians/voxel).
tables: Interpolation tables (one table for each dimension).
n_shift: Size for fftshift, usually ``im_size // 2``.
numpoints: Number of neighbors to use for interpolation.
table_oversamp: Table oversampling factor.
offsets: A list of offsets, looping over all possible combinations of
`numpoints`.
norm: Whether to apply normalization with the FFT operation.
Options are ``"ortho"`` or ``None``.
Returns:
``image`` calculated at scattered Fourier locations.
"""
image = fft_and_scale(
image=image,
scaling_coef=scaling_coef,
im_size=im_size,
grid_size=grid_size,
norm=norm,
)
return kb_table_interp(
image=image,
omega=omega,
tables=tables,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
offsets=offsets,
)
[docs]def kb_table_nufft_adjoint(
data: Tensor,
scaling_coef: Tensor,
im_size: Tensor,
grid_size: Tensor,
omega: Tensor,
tables: List[Tensor],
n_shift: Tensor,
numpoints: Tensor,
table_oversamp: Tensor,
offsets: Tensor,
norm: Optional[str] = None,
) -> Tensor:
"""Kaiser-Bessel NUFFT adjoint with table interpolation.
See :py:class:`~torchkbnufft.KbNufftAdjoint` for an overall description of
the adjoint NUFFT.
Args:
data: Scattered data to be iNUFFT'd to an image.
scaling_coef: Image-domain coefficients to compensate for interpolation
errors.
im_size: Size of image with length being the number of dimensions.
grid_size: Size of grid to use for interpolation, typically 1.25 to 2
times ``im_size``.
omega: k-space trajectory (in radians/voxel).
tables: Interpolation tables (one table for each dimension).
n_shift: Size for fftshift, usually ``im_size // 2``.
numpoints: Number of neighbors to use for interpolation.
table_oversamp: Table oversampling factor.
offsets: A list of offsets, looping over all possible combinations of
`numpoints`.
norm: Whether to apply normalization with the FFT operation.
Options are ``"ortho"`` or ``None``.
Returns:
``data`` transformed to an image.
"""
data = kb_table_interp_adjoint(
data=data,
omega=omega,
tables=tables,
n_shift=n_shift,
numpoints=numpoints,
table_oversamp=table_oversamp,
offsets=offsets,
grid_size=grid_size,
)
return ifft_and_scale(
image=data,
scaling_coef=scaling_coef,
im_size=im_size,
grid_size=grid_size,
norm=norm,
)