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This article presents the algorithms developed by the Core Imaging Library(CIL) developer team for the Helsinki Tomography Challenge 2022. The challengefocused on reconstructing 2D phantom shapes from limited-angle computedtomography (CT) data. The CIL team designed and implemented five reconstructionmethods using CIL (https://ccpi.ac.uk/cil/), an open-source Python package fortomographic imaging. The CIL team adopted a model-based reconstructionstrategy, unique to this challenge with all other teams relying ondeep-learning techniques. The CIL algorithms showcased exceptional performance,with one algorithm securing the third place in the competition. Thebest-performing algorithm employed careful CT data pre-processing and anoptimization problem with single-sided directional total variationregularization combined with isotropic total variation and tailored lower andupper bounds. The reconstructions and segmentations achieved high quality fordata with angular ranges down to 50 degrees, and in some cases acceptableperformance even at 40 and 30 degrees. This study highlights the effectivenessof model-based approaches in limited-angle tomography and emphasizes theimportance of proper algorithmic design leveraging on available prior knowledgeto overcome data limitations. Finally, this study highlights the flexibility ofCIL for prototyping and comparison of different optimization methods.

A directional regularization method for the limited-angle Helsinki Tomography Challenge using the Core Imaging Library (CIL)