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Tytuł pozycji:

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

Tytuł:
Core Imaging Library - Part I: a versatile Python framework for tomographic imaging.
Autorzy:
Jørgensen JS; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.; Department of Mathematics, The University of Manchester, Manchester, UK.
Ametova E; Laboratory for Applications of Synchrotron Radiation, Karlsruhe Institute of Technology, Karlsruhe, Germany.; Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK.
Burca G; ISIS Neutron and Muon Source, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK.; Department of Mathematics, The University of Manchester, Manchester, UK.
Fardell G; Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK.
Papoutsellis E; Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK.; Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK.
Pasca E; Scientific Computing Department, STFC, UKRI, Rutherford Appleton Laboratory, Didcot, UK.
Thielemans K; Institute of Nuclear Medicine and Centre for Medical Image Computing, University College London, London, UK.
Turner M; Research IT Services, The University of Manchester, Manchester, UK.
Warr R; Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK.
Lionheart WRB; Department of Mathematics, The University of Manchester, Manchester, UK.
Withers PJ; Henry Royce Institute, Department of Materials, The University of Manchester, Manchester, UK.
Źródło:
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences [Philos Trans A Math Phys Eng Sci] 2021 Aug 23; Vol. 379 (2204), pp. 20200192. Date of Electronic Publication: 2021 Jul 05.
Typ publikacji:
Journal Article
Język:
English
Imprint Name(s):
Original Publication: London : The Royal Society, c1996-
MeSH Terms:
Software*
Radiographic Image Interpretation, Computer-Assisted/*statistics & numerical data
Tomography, X-Ray Computed/*statistics & numerical data
Algorithms ; Data Interpretation, Statistical ; Databases, Factual/statistics & numerical data ; Humans ; Image Interpretation, Computer-Assisted/statistics & numerical data ; Imaging, Three-Dimensional/statistics & numerical data ; Neutrons ; Positron-Emission Tomography/statistics & numerical data ; Synchrotrons ; Tomography/statistics & numerical data
References:
IEEE Trans Image Process. 2009 Nov;18(11):2419-34. (PMID: 19635705)
Philos Trans A Math Phys Eng Sci. 2021 Aug 23;379(2204):20200192. (PMID: 34218673)
J Appl Crystallogr. 2015 Jan 30;48(Pt 1):301-305. (PMID: 26089752)
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J Synchrotron Radiat. 2014 Sep;21(Pt 5):1188-93. (PMID: 25178011)
Philos Trans A Math Phys Eng Sci. 2021 Aug 23;379(2204):20200193. (PMID: 34218671)
J Theor Biol. 1972 Jul;36(1):105-17. (PMID: 5070894)
Opt Express. 2016 Oct 31;24(22):25129-25147. (PMID: 27828452)
J Synchrotron Radiat. 2014 Nov;21(Pt 6):1224-30. (PMID: 25343788)
Med Phys. 2018 Oct;45(10):e886-e907. (PMID: 30098050)
IEEE Trans Med Imaging. 1994;13(4):601-9. (PMID: 18218538)
Philos Trans A Math Phys Eng Sci. 2021 Aug 23;379(2204):20200208. (PMID: 34218674)
Opt Express. 2012 Jan 16;20(2):794-806. (PMID: 22274425)
Phys Med Biol. 2012 May 21;57(10):3065-91. (PMID: 22538474)
Philos Trans A Math Phys Eng Sci. 2015 Jun 13;373(2043):. (PMID: 25939626)
Phys Med Biol. 2018 Sep 10;63(18):185005. (PMID: 30113313)
Contributed Indexing:
Keywords: X-ray CT; computed tomography; convex optimization; image reconstruction; software
Entry Date(s):
Date Created: 20210705 Date Completed: 20210930 Latest Revision: 20220204
Update Code:
20240105
PubMed Central ID:
PMC8255949
DOI:
10.1098/rsta.2020.0192
PMID:
34218673
Czasopismo naukowe
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'.

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