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

Jørgensen, J. S.; Ametova, E. 1; Burca, G.; Fardell, G.; Papoutsellis, E.; Pasca, E.; Thielemans, K.; Turner, M.; Warr, R.; Lionheart, W. R. B.; Withers, P. J.
1 Laboratorium für Applikationen der Synchrotronstrahlung (LAS), Karlsruher Institut für Technologie (KIT)


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.

Verlagsausgabe §
DOI: 10.5445/IR/1000136787
Veröffentlicht am 26.08.2021
DOI: 10.1098/rsta.2020.0192
Zitationen: 12
Web of Science
Zitationen: 8
Zitationen: 15
Cover der Publikation
Zugehörige Institution(en) am KIT Laboratorium für Applikationen der Synchrotronstrahlung (LAS)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 08.2021
Sprache Englisch
Identifikator ISSN: 1364-503X, 1471-2962
KITopen-ID: 1000136787
HGF-Programm 56.13.11 (POF IV, LK 01) Building Blocks of Life: Structure and Function
Erschienen in Philosophical transactions of the Royal Society of London / A
Verlag The Royal Society
Band 379
Heft 2204
Seiten Art. Nr.: 20200192
Vorab online veröffentlicht am 05.07.2021
Nachgewiesen in Web of Science
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