CIL is an open-source mainly Python framework for tomographic imaging for cone and parallel beam geometries. It comes with tools for loading, preprocessing, reconstructing and visualising tomographic data.
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 licence.
Complete API reference, user guides, and tutorials for CIL.
The documentation is updated regularly and built nightly from the latest development code.
Please cite CIL if you use it in your research.
We provide three main papers describing CIL's framework, multichannel capabilities, and algorithmic developments, along with BibTeX and RIS export options.
View publications that have used CIL in their research.
Journal papers, conference proceedings, and PhD theses organized by year.
Run CIL examples without any local installation using Binder.
Access a large collection of Jupyter Notebooks covering basic usage to advanced reconstructions with iterative methods.