Computed tomography (CT) is a widely used non-invasive diagnostic method in various fields, and recent advances in deep learning have led to significant progress in CT image reconstruction. However, the lack of large-scale, open-access datasets has hindered the comparison of different types of learned methods. To address this gap, we use the 2DeteCT dataset, a real-world experimental computed tomography dataset, for benchmarking machine learning based CT image reconstruction algorithms. We categorize these methods into post-processing networks, learned/unrolled iterative methods, learned regularizer methods, and plug-and-play methods, and provide a pipeline for easy implementation and evaluation. Using key performance metrics, including SSIM and PSNR, our benchmarking results showcase the effectiveness of various algorithms on tasks such as full data reconstruction, limited-angle reconstruction, sparse-angle reconstruction, low-dose reconstruction, and beam-hardening corrected reconstruction. With this benchmarking study, we provide an evaluation of a range of algorithms representative for different categories of learned reconstruction methods on a recently published dataset of real-world experimental CT measurements. The reproducible setup of methods and CT image reconstruction tasks in an open-source toolbox enables straightforward addition and comparison of new methods later on. The toolbox also provides the option to load the 2DeteCT dataset differently for extensions to other problems and different CT reconstruction tasks.

翻译：计算机断层扫描（CT）是一种广泛应用于各领域的非侵入式诊断方法，而深度学习的最新进展显著推动了CT图像重建技术的发展。然而，由于缺乏大规模、开放获取的数据集，不同类型学习方法的比较一直受到限制。为填补这一空白，我们采用真实世界实验CT数据集2DeteCT，对基于机器学习的CT图像重建算法进行基准测试。我们将这些方法分为后处理网络、学习型/展开式迭代方法、学习型正则化方法以及即插即用方法，并提供便于实现与评估的流程框架。通过SSIM和PSNR等关键性能指标，我们的基准测试结果展示了各类算法在全数据重建、有限角度重建、稀疏角度重建、低剂量重建以及射束硬化校正重建等任务上的有效性。本基准研究基于近期发布的真实世界实验CT测量数据集，对代表不同学习重建方法类别的多种算法进行了系统性评估。通过开源工具箱中可复现的方法配置与CT图像重建任务设置，未来能够便捷地集成并比较新方法。该工具箱还支持以不同方式加载2DeteCT数据集，便于扩展至其他问题及不同的CT重建任务。