DOI: 10.5281/zenodo.6985407
title: Cone-Beam Computed Tomography Dataset of a Pine Cone

description: <strong>Summary</strong> This dataset is a collection of X-ray projection images of a pine cone imaged in a cone-beam computed tomography (CBCT) scanner. The dataset also includes a metadata file, specifying the scan geometry and other important scan parameters, and a photograph of the sample. <strong>Description</strong> <em>Sample Information</em> The sample is an open cone of a Baltic pine (<em>Pinus sylvestris</em>), approximately 3 cm in diameter. For the scanning process sticky tack was used to attach the sample to a plastic tube placed into the rotation stage. <em>Scanner</em> The measurements were acquired using a cone-beam computed tomography scanner designed and constructed in-house in the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki. The scanner consists of a molybdenum target X-ray tube (Oxford Instruments XTF5011), a motorized rotation stage (Thorlabs CR1-Z7), and a 12-bit, 2240x2368 pixel, energy-integrating flat panel detector (Hamatsu Photonics C7942CA-22). <em>Scan Settings</em> 721 X-ray projections were acquired using an angle increment of 0.5 degrees. The X-ray source voltage and tube current were set at 40 kV and 1 mA, respectively. The exposure time of the flat panel detector was set to 1000 ms. <em>Data Post-Processing</em> Two correction images were acquired before scanning the sample. A dark current image was created by averaging 100 images taken with the X-ray source off. A flat-field image was created by averaging 100 images taken with the X-ray source switched on with no sample placed in the scanner. After the scan, dark current and flat-field corrections were applied to each projection image using the Hamamatsu HiPic imaging software version 9.3. <em>Data Format</em> The X-ray projections are stored in .tif format. The metadata is contained in .txt file with formatting that is both human-readable and machine-readable. <em>Notes</em> Due to a slightly misaligned center of rotation in the scanner, the CT reconstructions can appear blurry. It was empirically observed that this problem can be compensated for quite well by shifting each projection left by 5 pixels, using circular boundary conditions, before performing any other operations on the projections. <strong>Research Group</strong> This dataset was produced by the Inverse Problems research group at the Department of Mathematics and Statistics at the University of Helsinki, Finland: https://www2.helsinki.fi/en/researchgroups/inverse-problems. <strong>Additional Links</strong> This dataset was originally created as part of a tutorial on working with measured X-ray data in computed tomography. A video tutorial on the measurement process can be found on the Inverse Problems Channel on YouTube at https://www.youtube.com/watch?v=CWUomAmUDys. To get started with the data, we recommend looking at the HelTomo toolbox, specifically created for working with CBCT data collected in the Industrial Mathematics Computed Tomography Laboratory, and available at https://github.com/Diagonalizable/HelTomo <strong>Contact Details</strong> For more information or guidance in using these datasets, please contact alexander.meaney [at] helsinki.fi.