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

Jakob Sauer Jørgensen

doi:10.1098/rsta.2020.0192

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

2021-07-05

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Jakob Sauer Jørgensen *

Evangelos Papoutsellis

DOI：10.1098/rsta.2020.0192

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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'.

Keywords:

computed tomography

X-ray CT

convex optimization

software

image reconstruction

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

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