curadon aims to be a foundational library designed to unite model-based and deep learning communities in the X-ray CT field. As a versatile building block for a broad range of X-ray CT applications, curadon offers efficient projection operators, a minimalistic interface, easy-to-use Python bindings, and support for diverse geometries. Demonstrating both efficiency compared to standard tools and flexibility with real-world data, curadon caters to classical analytic and model-based iterative approaches, as well as deep learning applications.
Currently, curadon provides forward and backward projection operators for X-ray attenuation CT. It can handle arbitrary fan and cone-beam flat-planen setups. It further provides differentiable PyTorch functions, helpful for deep learning based applications.
As of now, the 2D forward and backward projection operators perform on a similar levels as the implementations by TorchRadon, lacking behind a bit for smaller problems, but outperforming (slightly) for larger problems.
You can see the average operations per second over 50 runs (with 3 warmup runs) in the following table:
| Benchmark (mean op / s) | curadon | astra (± vs curadon) | torch-radon (± vs curadon) |
|---|---|---|---|
| forward 2d 64 | 12864.38 | 1162.71 (-91.0%) | 14274.11 (+11.0%) |
| forward 2d 128 | 7541.54 | 1029.40 (-86.4%) | 8579.41 (+13.8%) |
| forward 2d 256 | 2922.37 | 722.96 (-75.3%) | 3429.46 (+17.4%) |
| forward 2d 512 | 869.38 | 363.63 (-58.2%) | 1037.06 (+19.3%) |
| forward 2d 1024 | 241.41 | 121.98 (-49.5%) | 240.24 ( -0.5%) |
| forward 2d 2048 | 66.89 | 32.89 (-50.8%) | 47.05 (-29.7%) |
| backward 2d 64 | 12969.40 | 911.69 (-93.0%) | 15643.38 (+20.6%) |
| backward 2d 128 | 8666.99 | 682.84 (-92.1%) | 9538.58 (+10.1%) |
| backward 2d 256 | 3683.74 | 274.31 (-92.6%) | 3640.26 ( -1.2%) |
| backward 2d 512 | 1083.92 | 81.28 (-92.5%) | 1075.92 ( -0.7%) |
| backward 2d 1024 | 263.92 | 21.79 (-91.7%) | 266.48 ( +1.0%) |
| backward 2d 2048 | 66.68 | 5.50 (-91.8%) | 62.46 ( -6.3%) |
The size of the image is given in the description, the detector is of size
benchmark folder.
I got much valuable feedback at the CT Meeting 2024 conference. Where I was fortunate enough to showcase my work. With this feedback in mind, as of August 2024, my next steps will focus on building further improving the 2D projection. The features I'll work on next (in no particular order):
- Loose restrictions on trajectory
- Construct geometry from ASTRA-like vector notation
- Support batch operations
- Support curved detector
- Matched operators and builtin cosine weights (similar to FDK weights)
Then I will play catch up with the 3D projectors, and make them more similar to the 2D projectors, including support for projection matrices.
In the mean time, I will work on examples that showcase the different features of curadon. If you have any thoughts or feature requests, please open an issue. I'm happy to discuss it!
Check the example folder to see how the library works. There you will find an example to reconstruct real and synthetic X-ray CT data using the filtered backprojection.
To get started with curadon, follow these steps:
From the project root, run
pip install -v ./pythonYou can omit the -v flag, for a more concise output.
If you plan on contributing or developing, install the package in editable mode with the rebuild option. For that you'll need to install developer dependencies:
pip install -v ./python[dev]Then you can build with editable mode:
pip install --no-build-isolation -Ceditable.rebuild=true -ve ./pythonThis makes development quite comfortable.
Install the required dependencies with:
pip install ./python[benchmark]Additionally, ensure that both the ASTRA-toolbox and TorchRadon are installed in your environment.
Then you can run from the root directory:
python benchmark/bench.py benchmark --repeat 100 --warmup 5 --rmin 8 --rmax 13Any help is appreciated. If you have any questions or comments feel free to open an issue. We are also welcoming code contributions in the form of pull requests. Consider opening an issue first, especially if you plan on implementing a larger feature.