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
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.
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