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