Slice-by-Slice X-ray Tomography dataset of Dog Toy

This submission contains a dataset used in the paper

"Ajinkya Kadu, Felix Lucka, and K. Joost Batenburg. "Single-shot Tomography of Discrete Dynamic Objects." arXiv preprint arXiv:2311.05269 (2023)."

The data collection has been acquired using a highly flexible, programmable and custom-built X-ray CT scanner, the FleX-ray scanner, developed by TESCAN-XRE NV, located in the FleX-ray Lab at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands. It consists of a cone-beam microfocus X-ray point source (limited to 90 kV and 90 W) that projects polychromatic X-rays onto a 14-bit CMOS (complementary metal-oxide semiconductor) flat panel detector with CsI(Tl) scintillator (Dexella 1512NDT). To create a 2D dataset, a fan-beam geometry was mimicked by only reading out the central row of the detector, which results in 956 detector pixel with an effective length of 149.6 μm each. Between source and detector there is a rotation stage, upon which the sample was mounted. The sample that we imaged was a dog toy in a shape of a bone made of a rubber. The X-ray tube voltage was 90kV and a copper filter was used to block the low-energy part of the spectrum to limit beam-hardening artifacts. The source-to-detector distance was 487.9 mm, while the source-to-origin of the sample was 374.5 mm in a fan-beam geometry. We acquired 673 z-slices with 0.25 mm distance between slices. Further information about the technical details of X-ray CT can be found in the above paper and in 

Maximilian B. Kiss, Sophia B. Coban, K. Joost Batenburg, Tristan van Leeuwen, and Felix Lucka “2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning", Sci Data 10, 576 (2023) or  arXiv:2306.05907 (2023)

The upload consists of two files, namely:

1. GrayBone90kV4Filter.zip: contains the raw measurement data.
2. GrayBone90kV4FilterPreprocessed.mat: contains preprocessed data to be used in the MATLAB script provided to do pseudo-dynamic tomography. It also contains reference reconstruction obtained via Filtered Back Projection (FBP) algorithm. 

In the Github repository https://github.com/ajinkyakadu/DynamicXRayCT, we provide the scripts to read and process the raw data. The Github repository also contains all the scripts to reconstruct the dynamic solution using advanced algorithms. Furthermore, the raw data formats are described in great details in Kiss et al 2023 paper referenced above.