Core Imaging Library - Part I: a versatile Python framework for tomographic imaging

• Published in: Philosophical transactions of the Royal Society of London. Series A: Mathematical, physical, and engineering sciences Vol. 379; no. 2204; p. 20200192
• Main Authors: Jørgensen, J. S., Ametova, E., Burca, G., Fardell, G., Papoutsellis, E., Pasca, E., Thielemans, K., Turner, M., Warr, R., Lionheart, W. R. B., Withers, P. J.
• Format: Journal Article
• Language: English
• Published: England The Royal Society Publishing 23.08.2021
• Subjects: Algorithms; Data Interpretation, Statistical; Databases, Factual - statistics & numerical data; Humans; Image Interpretation, Computer-Assisted - statistics & numerical data; Imaging, Three-Dimensional - statistics & numerical data; Neutrons; Positron-Emission Tomography - statistics & numerical data; Radiographic Image Interpretation, Computer-Assisted - statistics & numerical data; Software; Synchrotrons; Tomography - statistics & numerical data; Tomography, X-Ray Computed - statistics & numerical data; computed tomography; convex optimization; X-ray CT; image reconstruction; software
• ISSN: 1364-503X; 1471-2962; 1471-2962
• DOI: 10.1098/rsta.2020.0192

We present the Core Imaging Library (CIL), an open-source Python framework for tomographic imaging with particular emphasis on reconstruction of challenging datasets. Conventional filtered back-projection reconstruction tends to be insufficient for highly noisy, incomplete, non-standard or multi-channel data arising for example in dynamic, spectral and in situ tomography. CIL provides an extensive modular optimization framework for prototyping reconstruction methods including sparsity and total variation regularization, as well as tools for loading, preprocessing and visualizing tomographic data. The capabilities of CIL are demonstrated on a synchrotron example dataset and three challenging cases spanning golden-ratio neutron tomography, cone-beam X-ray laminography and positron emission tomography. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 2’.