Helsinki tomography challenge 2022: Description of the competition and dataset

Inverse problems involve extracting information from indirect measurements. Many of these problems are ill-posed, making the recovery process unstable and sensitive to errors and noise. Specially designed algorithms are essential for robust solutions in such cases. Computed tomography (CT), which aims to determine an object's interior structure from X-ray projections, is a widely used application that requires solving an ill-posed problem. In this work we present the Helsinki Tomography Challenge 2022 (HTC2022), aimed to foster algorithm development in the field of CT reconstruction. HTC2022 focused specifically on limited-angle tomography to produce segmented reconstructions of disc-shaped imaging phantoms with holes of varying complexity and with progressively reduced angular range in seven levels of difficulty. This work also presents the dataset used in the competition, now publicly available. Real data is crucial for testing algorithms against the complexities of real-world scenarios, and this dataset can now be used by the reconstruction algorithm development community. HTC2022 was a global competition, with nine teams from seven countries participating and submitting a total of 22 algorithms. The competition results indicate interesting solutions in limited-angle tomography, with high-quality reconstructions that demonstrated promising directions for future research.