torch-fourier-slice 0.5.2

Fourier slice extraction/insertion in PyTorch.

torch-fourier-slice

Fourier slice extraction/insertion from 2D images and 3D volumes in PyTorch.

Overview

This package provides a simple API for back projection (reconstruction) and forward projection of 3D volumes using Fourier slice insertion and extraction. This can be done for

• single volumes with project_3d_to_2d() and backproject_2d_to_3d()
• and multichannel volumes with project_3d_to_2d_multichannel() and backproject_2d_to_3d_multichannel()

There are also some lower order layers in the package that run directly on Fourier transforms of volumes/images which can be relevant if the fourier transform can be precalculated:

• extract_central_slices_rfft_3d(), extract_central_slices_rfft_3d_multichannel()
• insert_central_slices_rfft_3d(), insert_central_slices_rfft_3d_multichannel()

The package also provides a use case for extracting common lines from 2D images with project_2d_to_1d which can be useful for tilt-axis angle optimization in cryo-ET.

Installation

pip install torch-fourier-slice

Usage

Single volume

import torch
from scipy.stats import special_ortho_group
from torch_fourier_slice import project_3d_to_2d, backproject_2d_to_3d

# start with a volume
volume = torch.rand((30, 30, 30))

# and some random rotations
rotation_matrices = torch.tensor(special_ortho_group.rvs(dim=3, size=10))
# shape is (10, 3, 3)

# forward project the volume, provides 10 projection images
projections = project_3d_to_2d(volume, rotation_matrices)
# shape is (10, 30, 30)

# we can backproject the 10 images to get the original volume back
reconstruction = backproject_2d_to_3d(projections, rotation_matrices)
# shape is (30, 30, 30)

# we can have an arbitrary number of leading dimensions for the rotations
rotation_matrices = torch.rand(3, 10, 3, 3)
projections = project_3d_to_2d(volume, rotation_matrices)
# shape is (3, 10, 30, 30)

# but for reconstruction it needs to match up with the projections
reconstruction = backproject_2d_to_3d(
    projections,  # (3, 10, 30, 30) 
    rotation_matrices  # (3, 10, 3, 3)
)
# shape is (30, 30, 30

Multichannel volumes

import torch
from scipy.stats import special_ortho_group
from torch_fourier_slice import project_3d_to_2d_multichannel, backproject_2d_to_3d_multichannel

# now we start with a multichannel 3d volume
volume = torch.rand((5, 30, 30, 30))

# and some random rotations
rotation_matrices = torch.tensor(special_ortho_group.rvs(dim=3, size=10))
# shape is (10, 3, 3)

# forward project the volume, provides 10 projection images with 5 channels each
projections = project_3d_to_2d_multichannel(volume, rotation_matrices)
# shape is (10, 5, 30, 30)

# we can backproject the 10 multichannel images to get the original multichannel volume back
reconstruction = backproject_2d_to_3d_multichannel(projections, rotation_matrices)
# shape is (5, 30, 30, 30)

# we can have an arbitrary number of trailing dimensions as well for multichannel data
rotation_matrices = torch.rand(3, 10, 3, 3)
projections = project_3d_to_2d_multichannel(volume, rotation_matrices)
# shape is (3, 10, 5, 30, 30)

# but for reconstruction it needs to match up with the projections
reconstruction = backproject_2d_to_3d_multichannel(
    projections,  # (3, 10, 5, 30, 30) 
    rotation_matrices  # (3, 10, 3, 3)
)
# shape is (5, 30, 30, 30)

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.