The Core Imaging Library (CIL) is an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered backprojection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multichannel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimisation framework for prototyping reconstruction methods including sparsity and total variation regularisation, as well as tools for loading, preprocessing and visualising tomographic data.

The documentation for CIL can be accessed here.

Binary installation of CIL can be achieved with conda.

We recommend using either miniconda or miniforge, which are both minimal installers for conda. We also recommend a conda version of at least 23.10 for quicker installation.

We maintain an environment file with the required packages to run the CIL demos which you can use to create a new environment. This will have specific and tested versions of all dependencies that are outlined in the table below:

conda env create -f https://tomographicimaging.github.io/scripts/env/cil_demos.yml

Or for a CPU-only environment which will work for a limited number of CIL demos

conda env create -f https://tomographicimaging.github.io/scripts/env/cil_demos_cpu.yml

If you prefer to give the environment your own name, include -n <env_name> in the above command. To activate the environment run

conda activate <env_name>

where <env_name> is cil_demos, cil_demos_cpu or whatever name you specified above.

If you prefer to install CIL with minimal dependencies into an existing environment you can use:

conda install -c conda-forge -c ccpi cil=25.0.0

A number of additional dependencies are required for specific functionality in CIL, these should be added to your environment as necessary. See the dependency table below for details.

See our documentation for details of dependency versions we support.

Finally, CIL can be run via a Jupyter Notebook enabled Docker container:

docker run --rm --gpus all -p 8888:8888 -it ghcr.io/tomographicimaging/cil:latest

We typically run training courses at least twice a year - check https://ccpi.ac.uk/training/ for our upcoming events!

Jupyter Notebooks usage examples without any local installation are provided in Binder. Please click the launch binder icon above. For more information, go to CIL-Demos and https://mybinder.org.

• PyCon DE & PyData Berlin 2022, Apr 2022: Abstract, Video, Material
• Training School for the Synergistic Image Reconstruction Framework (SIRF) and Core Imaging Library (CIL), Jun 2021: Videos, Material
• Synergistic Reconstruction Symposium, Nov 2019: Slides, Videos, Material

For instructions on how to build CIL from source code, please see our Developers' Guide

If you use CIL in your research, please include citations to both the software on Zenodo, and a CIL paper:

E. Pasca, J. S. Jørgensen, E. Papoutsellis, E. Ametova, G. Fardell, K. Thielemans, L. Murgatroyd, M. Duff and H. Robarts (2023)
Core Imaging Library (CIL)
Zenodo [software archive]
DOI: https://doi.org/10.5281/zenodo.4746198

In most cases, the first CIL paper will be the appropriate choice:

J. S. Jørgensen, E. Ametova, G. Burca, G. Fardell, E. Papoutsellis, E. Pasca, K. Thielemans, M. Turner, R. Warr, W. R. B. Lionheart and P. J. Withers (2021)
Core Imaging Library - Part I: a versatile Python framework for tomographic imaging.
Phil. Trans. R. Soc. A. 379: 20200192.
DOI: https://doi.org/10.1098/rsta.2020.0192
Code: https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-I

However, if your work is more closely related to topics covered in our second CIL paper then please additionally or alternatively reference the second paper:

E. Papoutsellis, E. Ametova, C. Delplancke, G. Fardell, J. S. Jørgensen, E. Pasca, M. Turner, R. Warr, W. R. B. Lionheart and P. J. Withers (2021)
Core Imaging Library - Part II: multichannel reconstruction for dynamic and spectral tomography.
Phil. Trans. R. Soc. A. 379: 20200193.
DOI: https://doi.org/10.1098/rsta.2020.0193)
Code: https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-II