CIL provides optimised standard methods such as Filtered Back Projection and FDK and an extensive modular optimisation framework for prototyping reconstruction methods including sparsity and total variation regularisation, useful when conventional filtered backprojection reconstruction do not lead to satisfactory results, as in highly noisy, incomplete, non-standard or multichannel data arising for example in dynamic, spectral and in situ tomography.
CIL is open-source software released under the Apache v2.0 license
Examples
We have a repository with a large collection of Jupyter Notebooks which cover a wide range of topics, from basic usage to advanced reconstructions with iterative methods.
Some examples without any local installation are provided in Binder. Please click the launch binder icon below to try them immediately, in your browser. 
Installing CIL
CIL source code is available on GitHub and binary distribution is with anaconda packages. This makes installation simple, so once you have miniconda you can install CIL in a new environment with one line. Please refer to the CIL GitHub Installation for complete information.
Documentation
CIL has a live documentation which gets updated regularly and built nightly. We suggest to download and read the open access articles below, which provide very detailed information about CIL structure and usage. Code to reproduce the results in the papers is also available on GitHub.
Jørgensen JS et al. 2021 Core Imaging Library Part I: a versatile python framework for tomographic imaging. Phil. Trans. R. Soc. A 20200192.
The code to reproduce the results of the paper can be found at https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-I
Papoutsellis E et al. 2021 Core Imaging Library – Part II: multichannel reconstruction for dynamic and spectral
tomography. Phil. Trans. R. Soc. A 20200193.
The code to reproduce the results of the paper can be found at at https://github.com/TomographicImaging/Paper-2021-RSTA-CIL-Part-II
![IMAT neutron tomography dataset. Top row: (left) top-view schematic of high-purity elemental metal rod sample; (centre) top-view photograph; (right) single raw projection image showing rods of different absorption. Middle row: (left) preprocessed slice sinogram; (right) horizontal line profile of FBP, PDHG TV and GD TV reconstruction along line shown on image below. Bottom row: (left) slice reconstructions, FBP; (centre) TV reconstruction with PDHG; (right) STV reconstruction with GD. Colour range [−0.002, 0.012].](https://ccpi.ac.uk/wp-content/webpc-passthru.php?src=https://ccpi.ac.uk/wp-content/uploads/2022/10/RSTA_cover.png&nocache=1 "RSTA_cover")
Contacts
Join the CIL community on Discord, or the CCPi developers mailing list, to keep updated with latest news and to ask and receive help.