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Dataset . 2017
License: CC BY
Data sources: Datacite
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Dataset . 2017
License: CC BY
Data sources: ZENODO
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Sparsebeads Dataset

Authors: Jørgensen, Jakob S; Coban, Sophia; Lionheart, William; Mcdonald, Samuel; Withers, Philip; Applied Mathematics; Materials Engineering;

Sparsebeads Dataset

Abstract

The presented data set, inspired by the SophiaBeads Dataset Project for X-ray Computed Tomography, is collected for studies involving sparsity-regularised reconstruction. The aim is to provide tomographic data for various samples where the sparsity in the image varies. This dataset is made available as part of the publication "SparseBeads Data: Benchmarking Sparsity-Regularized Computed Tomography", Jakob S Jørgensen et al, 2017. Meas. Sci. Technol. 28 124005. Direct link: https://doi.org/10.1088/1361-6501/aa8c29. This manuscript is published as part of Special Feature on Advanced X-ray Tomography (open access). We refer the users to this publication for an extensive detail in the experimental planning and data acquisition. Each zipped data folder includes The meta data for data acquisition and geometry parameters of the scan (.xtekct and .ctprofile.xml). A sinogram of the central slice (CentreSlice > Sinograms > .tif) along with meta data for the 2D slice (.xtek2dct and .ct2dprofile.xml), List of projection angles (.ang) and a 2D FDK reconstruction using the CTPro reconstruction suite (RECON2D > .vol) with volume visualisation parameters (.vgi), added as a reference. We also include an extra script for those that wish to use the SophiaBeads Dataset Project Codes, which essentially replaces the main script provided, sophiaBeads.m (visit https://zenodo.org/record/16539). Please note that sparseBeads.m script will have to be placed in the same folder as the project codes. The latest version of this script can be found here: https://github.com/jakobsj/SparseBeads_code For more information, please contact jakj [at] dtu.dk jakob.jorgensen [at] manchester.ac.uk

JSJ was supported by the project ``High-Definition Tomography'' funded by Advanced Grant No. 291405 from the European Research Council. JSJ is grateful to the Schools of Mathematics and Materials, University of Manchester for hosting him during the work. SBC was supported by the School of Mathematics, University of Manchester, EPSRC CCPi (EP/J010456/1), and BP through the BP International Centre for Advanced Materials (BP-ICAM). WRBL acknowledges support from a Royal Society Wolfson Research Merit Award. SAM is grateful for funding through ZEISS. Authors acknowledge use of the Henry Moseley X-ray Imaging Facility at the University of Manchester, funded from the EPSRC under EP/F007906/1, EP/F028431/1, EP/I02249X/1 and EP/M022498/1 as well as Advanced Grant No. 695638 ``Correlative Tomography'' from the European Research Council.

{"references": ["J\u00f8rgensen, J. S., Coban, S. B., Lionheart W. R. B., McDonald S. A., Withers P. J. (2017). SparseBeads Data: Benchmarking Sparsity-Regularized Computed Tomography. Meas. Sci. Technol. 28 124005. https://doi.org/10.1088/1361-6501/aa8c29", "Coban, S. B. (2015). SophiaBeads Dataset Project Codes [Data set]. Zenodo. http://doi.org/10.5281/zenodo.16539", "Coban, S. B., & McDonald, S. A. (2015). SophiaBeads Dataset Project [Data set]. Zenodo. http://doi.org/10.5281/zenodo.16474"]}

Country
United Kingdom
Keywords

sparsity, computed tomography, sparse image reconstruction

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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