Dataset Open Access

SophiaBeads Dataset Project

Coban, S. B.; McDonald, S. A.

Dublin Core Export

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Coban, S. B.</dc:creator>
  <dc:creator>McDonald, S. A.</dc:creator>
  <dc:description>SophiaBeads Dataset are acquired specifically for testing and comparing reconstruction methods for x-ray computed tomography. The sample is a plastic tube with a diameter of 25mm, filled with uniform Soda-Lime Glass (SiO2-Na2O) beads of diameters 2.5mm (with standard deviation 0.1mm). The sample has been scanned in the same conditions for each dataset, where the number of projections is halved after each batch scan (starting from 2048, down to 64 projections). This allows us to understand the effects of fewer projections, and develop algorithms that deliver quality results when the information content is low (i.e. faster scans or lower dose). 


These dataset are also suitable for developing and/or testing

	segmentation methods,
	image or data correction techniques (e.g. centre of rotation),
	forward and back project implementations, and
	benchmarking user’s own code/method.


For the reconstruction and quantification examples, please see (will update with link to paper!)

To use these datasets for own research, please follow the technical report available via (, and the GitHub repository ( If publishing work with these datasets, the reader must cite the dataset and the codes separately. 


The dataset are taken using the Nikon Custom Bay machine (using a cone-beam geometry), located in the Manchester Xray Imaging Facility (, by Sophia B. Coban and Samuel A. McDonald. 


Contact details for authors: 

Sophia B. Coban: 


Samuel A. McDonald: 



SophiaBeads Dataset Project Codes (DOI):</dc:description>
  <dc:subject>computed tomography</dc:subject>
  <dc:subject>cone beam</dc:subject>
  <dc:subject>reconstruction algorithms</dc:subject>
  <dc:subject>data correction</dc:subject>
  <dc:subject>large dataset</dc:subject>
  <dc:title>SophiaBeads Dataset Project</dc:title>
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