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

TL;DR

This paper introduces a model-based directional regularization approach for limited-angle CT reconstruction, demonstrating high-quality results with the open-source CIL library and outperforming deep-learning methods in the Helsinki Tomography Challenge.

Contribution

The paper presents a novel directional regularization method for limited-angle tomography, showcasing the effectiveness of model-based algorithms over deep learning in this context.

Findings

Achieved high-quality reconstructions at angles as low as 30-50 degrees.

One algorithm secured third place in the Helsinki Tomography Challenge.

Demonstrated the flexibility of CIL for prototyping various optimization methods.

Abstract

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.