DOI: 10.5281/zenodo.8041800
title: Helsinki Tomography Challenge 2022 (HTC2022) open tomographic dataset

description: This dataset was primarily designed for the Helsinki Tomography Challenge 2022 (HTC2022), 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 HTC2022 was to develop algorithms for limited angle tomography. The challenge data consists of tomographic measurements of two sets of plastic phantoms with a diameter of 7 cm and with holes of differing shapes cut into them. The first set is the teaching data, containing five training phantoms. The second set consists of 21 test phantoms used in the challenge to test algorithm performance. The test phantom data was released after the competition period ended.
The training phantoms were 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 teaching data includes the following files for each phantom:

The sinogram and all associated metadata (.MAT).
A pre-computed FBP reconstruction of the phantom (.MAT and .PNG).
A segmentation of the FBP reconstruction created with the procedure described below (.MAT and .PNG).
Also included in the teaching dataset is a MATLAB example script for how to work with the CT data.
The challenge test data is 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.
The test data includes the following files for each phantom:

The full sinogram and all associated metadata (.MAT).
The limited angle sinogram and all associated metadata, used to test the algorithms submitted to the challenge (.MAT).
A pre-computed FBP reconstruction of the phantom using the full data (.MAT and .PNG).
A pre-computed FBP reconstruction of the phantom using the limited angle data. These are of poor quality, and serve mainly as a demonstration of how FBP fails with limited angle data (.MAT and .PNG).
A segmentation of the FBP reconstruction using the full data, created with the procedure described below. This was used as the ground truth reference in the challenge (.MAT and .PNG).
A segmentation of the FBP reconstruction using the limited angle data, created with the procedure described below. These are of poor quality, and serve mainly as a demonstration of how FBP fails with limited angle data (.MAT and .PNG).
A photograph of the phantom, rotated and resized to match the ground truth segmentation (.PNG).
Also included in the test dataset is a collage in .PNG format, showing all the ground truth segmentation images and the photographs of the phantoms together.
As the orientation of CT reconstructions can depend on the tools used, we have included the 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 (FBP) 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 were not obliged to follow the above procedure, and were encouraged to explore various segmentation techniques for the limited angle reconstructions.
For getting started with the data, we recommend the following MATLAB toolboxes:
HelTomo - Helsinki Tomography Toolboxhttps://github.com/Diagonalizable/HelTomo/The ASTRA Toolboxhttps://www.astra-toolbox.com/Spot – A Linear-Operator Toolboxhttps://www.cs.ubc.ca/labs/scl/spot/Using the above toolboxes for the Challenge was by no means compulsory: the metadata for each dataset contains a full specification of the measurement geometry, and the competitors were free to use any and all computational tools they want to in computing the reconstructions and segmentations.
All measurements were conducted at the Industrial Mathematics Computed Tomography Laboratory at the University of Helsinki.