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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import torch
from monai.config.type_definitions import NdarrayOrTensor
from monai.networks.blocks.fft_utils_t import fftn_centered_t, ifftn_centered_t
from monai.utils.type_conversion import convert_data_type, convert_to_dst_type
def ifftn_centered(ksp: NdarrayOrTensor, spatial_dims: int, is_complex: bool = True) -> NdarrayOrTensor:
"""
Pytorch-based ifft for spatial_dims-dim signals. "centered" means this function automatically takes care
of the required ifft and fft shifts. This function calls monai.networks.blocks.fft_utils_t.ifftn_centered_t.
This is equivalent to do fft in numpy based on numpy.fft.ifftn, numpy.fft.fftshift, and numpy.fft.ifftshift
Args:
ksp: k-space data that can be
1) real-valued: the shape is (C,H,W) for 2D spatial inputs and (C,H,W,D) for 3D, or
2) complex-valued: the shape is (C,H,W,2) for 2D spatial data and (C,H,W,D,2) for 3D. C is the number of channels.
spatial_dims: number of spatial dimensions (e.g., is 2 for an image, and is 3 for a volume)
is_complex: if True, then the last dimension of the input ksp is expected to be 2 (representing real and imaginary channels)
Returns:
"out" which is the output image (inverse fourier of ksp)
Example:
.. code-block:: python
import torch
ksp = torch.ones(1,3,3,2) # the last dim belongs to real/imaginary parts
# output1 and output2 will be identical
output1 = torch.fft.ifftn(torch.view_as_complex(torch.fft.ifftshift(ksp,dim=(-3,-2))), dim=(-2,-1), norm="ortho")
output1 = torch.fft.fftshift( torch.view_as_real(output1), dim=(-3,-2) )
output2 = ifftn_centered(ksp, spatial_dims=2, is_complex=True)
"""
# handle numpy format
ksp_t, *_ = convert_data_type(ksp, torch.Tensor)
# compute ifftn
out_t = ifftn_centered_t(ksp_t, spatial_dims=spatial_dims, is_complex=is_complex)
# handle numpy format
out, *_ = convert_to_dst_type(src=out_t, dst=ksp)
return out
def fftn_centered(im: NdarrayOrTensor, spatial_dims: int, is_complex: bool = True) -> NdarrayOrTensor:
"""
Pytorch-based fft for spatial_dims-dim signals. "centered" means this function automatically takes care
of the required ifft and fft shifts. This function calls monai.networks.blocks.fft_utils_t.fftn_centered_t.
This is equivalent to do ifft in numpy based on numpy.fft.fftn, numpy.fft.fftshift, and numpy.fft.ifftshift
Args:
im: image that can be
1) real-valued: the shape is (C,H,W) for 2D spatial inputs and (C,H,W,D) for 3D, or
2) complex-valued: the shape is (C,H,W,2) for 2D spatial data and (C,H,W,D,2) for 3D. C is the number of channels.
spatial_dims: number of spatial dimensions (e.g., is 2 for an image, and is 3 for a volume)
is_complex: if True, then the last dimension of the input im is expected to be 2 (representing real and imaginary channels)
Returns:
"out" which is the output kspace (fourier of im)
Example:
.. code-block:: python
import torch
im = torch.ones(1,3,3,2) # the last dim belongs to real/imaginary parts
# output1 and output2 will be identical
output1 = torch.fft.fftn(torch.view_as_complex(torch.fft.ifftshift(im,dim=(-3,-2))), dim=(-2,-1), norm="ortho")
output1 = torch.fft.fftshift( torch.view_as_real(output1), dim=(-3,-2) )
output2 = fftn_centered(im, spatial_dims=2, is_complex=True)
"""
# handle numpy format
im_t, *_ = convert_data_type(im, torch.Tensor)
# compute ifftn
out_t = fftn_centered_t(im_t, spatial_dims=spatial_dims, is_complex=is_complex)
# handle numpy format
out, *_ = convert_to_dst_type(src=out_t, dst=im)
return out
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