Helsinki Tomography Challenge 2022 open tomographic dataset (HTC 2022)

This dataset is primarily designed for the Helsinki Tomography Challenge 2022 (HTC 2022), but it can be used for generic algorithm research and development in 2D CT reconstruction.

The dataset contains 2D tomographic measurements, i.e., sinograms and the affiliated metadata containing measurement geometry and other specifications. The sinograms have already been pre-processed with background and flat-field corrections, and compensated for a slightly misaligned center of rotation in the cone-beam computed tomography scanner. The log-transforms from intensity measurements to attenuation data have also been already computed. The data has been stored as MATLAB structs and saved in .mat file format.

The purpose of HTC 2022 is to develop algorithms for limited angle tomography. The challenge data consists of tomographic measurements of a set of plastic phantoms with a diameter of 7 cm and with holes of differing shapes cut into them.

The currently available dataset contains five training phantoms with full angular data. These are designed to facilitate algorithm development and benchmarking for the challenge itself. Four of the training phantoms contain holes. These are labeled ta, tb, tc, and td. A fifth training phantom is a solid disc with no holes. We encourage subsampling these datasets to create limited data sinograms and comparing the reconstruction results to the ground truth obtainable from the full-data sinograms. Note that the phantoms are not all identically centered.

The actual challenge data will be arranged into seven different difficulty levels, labeled 1-7, with each level containing three different phantoms, labeled A-C. As the difficulty level increases, the number of holes increases and their shapes become increasingly complex. Furthermore, the view angle is reduced as the difficulty level increases, starting with a 90 degree field of view at level 1, and reducing by 10 degrees at each increasing level of difficulty. The view-angles in the challenge data will not all begin from 0 degrees.

As the orientation of CT reconstructions can depend on the tools used, we have included example reconstructions for each of the phantoms to demonstrate how the reconstructions obtained from the sinograms and the specified geometry should be oriented. The reconstructions have been computed using the filtered back-projection algorithm provided by the ASTRA Toolbox.

We have also included segmentation examples of the reconstructions to demonstrate the desired format for the final competition entries. The segmentation images for obtained by the following steps:
1) Set all negative pixel values in the reconstruction to zero.
2) Determine a threshold level using Otsu's method.
3) Globally threshold the image using the threshold level.
4) Perform a morphological closing on the image using a disc with a radius of 3 pixels.

The competitors do not need to follow the above procedure, and are encouraged to explore various segmentation techniques for the limited angle reconstructions.


Also included in this dataset is a MATLAB example script for how to work with the CT data.

For getting started, we recommend the following MATLAB toolboxes:

HelTomo - Helsinki Tomography Toolbox
https://github.com/Diagonalizable/HelTomo/

The ASTRA Toolbox
https://www.astra-toolbox.com/

Spot – A Linear-Operator Toolbox
https://www.cs.ubc.ca/labs/scl/spot/

Note that using the above toolboxes for the Challenge is by no means compulsory: the metadata for each dataset contains a full specification of the measurement geometry, and the competitors are free to use any and all computational tools they want to in computing the reconstructions and segmentations.

The full data for all the test phantoms will be released after the Helsinki Tomography Challenge 2022 has ended.

All measurements were conducted at the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki.