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

Jakob Sauer Jørgensen*, E. Ametova, G. Burca, G. Fardell, E. Papoutsellis, E. Pasca, K. Thielemans, M. Turner, R. Warr, W. R. B. Lionheart, P. J. Withers

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

61 Downloads (Pure)

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 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'.

Original languageEnglish
Article number20200192
JournalPhilosophical transactions. Series A, Mathematical, physical, and engineering sciences
Volume379
Issue number2204
Number of pages24
ISSN1364-503X
DOIs
Publication statusPublished - 23 Aug 2021

Keywords

  • Computed tomography
  • Convex optimization
  • Image reconstruction
  • Software
  • X-ray CT

Fingerprint

Dive into the research topics of 'Core Imaging Library - Part I: a versatile Python framework for tomographic imaging'. Together they form a unique fingerprint.

Cite this