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

TL;DR

The Core Imaging Library (CIL) is an open-source Python framework designed for advanced tomographic image reconstruction, especially effective on challenging datasets with noise, incompleteness, or complexity, demonstrated across various imaging modalities.

Contribution

CIL introduces a versatile, modular Python framework that enhances tomographic reconstruction capabilities for difficult datasets, incorporating optimization, regularization, and visualization tools.

Findings

Successfully reconstructed challenging datasets including neutron tomography, laminography, and PET.

Demonstrated improved reconstruction quality over traditional methods in complex scenarios.

Showcased flexibility and extensibility of CIL for diverse tomographic imaging applications.

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.