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

Abstract

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 optimisation framework for prototyping reconstruction methods including sparsity and total variation regularisation, as well as tools for loading, preprocessing and visualising 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.

22 pages, 11 figures

References in corpus (2)

• Core Imaging Library -- Part II: Multichannel reconstruction for dynamic and spectral tomography
• Crystalline phase discriminating neutron tomography using advanced reconstruction methods

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