A collection of common tomographic reconstruction algorithms implemented using the tomosipo package.
The following algorithms are implemented:
- FBP
- FDK
- SIRT
- EM: Maximum likelihood expectation-maximization algorithm
- tv_min: Total-variation regularized least squares with the Champbolle Pock method
- nag_ls: l2-regularized least squares with the Nesterov accelerated gradient descent(nag) method
Free software: GNU General Public License v3
It takes a few steps to setup Tomosipo algorithms on your machine. We recommend installing Anaconda package manager for Python 3.
Install with:
conda create -n tomosipo tomosipo pytorch==2.0.1 pytorch-cuda=11.7 tqdm -c pytorch -c nvidia -c astra-toolbox/label/dev -c aahendriksen -c defaults
conda activate tomosipo
pip install git+https://github.com/ahendriksen/ts_algorithms.git
import torch
import tomosipo as ts
from ts_algorithms import fbp, sirt, tv_min2d, fdk, nag_ls
# Setup up volume and parallel projection geometry
vg = ts.volume(shape=(1, 256, 256))
pg = ts.parallel(angles=384, shape=(1, 384))
A = ts.operator(vg, pg)
# Create hollow cube phantom
x = torch.zeros(A.domain_shape)
x[:, 10:-10, 10:-10] = 1.0
x[:, 20:-20, 20:-20] = 0.0
# Forward project
y = A(x)
# reconstructions made with different algorithms
rec_fbp = fbp(A, y)
rec_sirt = sirt(A, y, num_iterations=100)
rec_tv_min = tv_min2d(A, y, 0.0001, num_iterations=100)
rec_nag_ls = nag_ls(A, y, num_iterations=100)- Allard Hendriksen - Initial work
- Dirk Schut - FDK and nag_ls implementations, current maintainer
See also the list of contributors who participated in this project.
Contributions are always welcome. Please submit pull requests against the master branch.
If you have any issues, questions, or remarks, then please open an issue on GitHub.
This project is licensed under the GNU General Public License v3 - see the LICENSE.md file for details.