Deep learning approaches for tomographic image reconstruction have become very effective and have been demonstrated to be competitive in the field. Comparing these approaches is a challenging task as they rely to a great extent on the data and setup used for training. With the Low-Dose Parallel Beam (LoDoPaB)-CT dataset, we provide a comprehensive, open-access database of computed tomography images and simulated low photon count measurements. It is suitable for training and comparing deep learning methods as well as classical reconstruction approaches. The dataset contains over 40000 scan slices from around 800 patients selected from the LIDC/IDRI database. The data selection and simulation setup are described in detail, and the generating script is publicly accessible. In addition, we provide a Python library for simplified access to the dataset and an online reconstruction challenge. Furthermore, the dataset can also be used for transfer learning as well as sparse and limited-angle reconstruction scenarios.

层析成像图像重建的深度学习方法已经变得非常有效，并且已经证明在该领域具有竞争力。比较这些方法是一项具有挑战性的任务，因为它们在很大程度上取决于用于训练的数据和设置。借助低剂量平行光束（LoDoPaB）-CT数据集，我们提供了计算机断层扫描图像和模拟的低光子计数测量的全面，开放式数据库。它适合于训练和比较深度学习方法以及经典的重建方法。该数据集包含从LIDC / IDRI数据库中选择的大约800位患者的40000多个扫描切片。详细介绍了数据选择和仿真设置，并且生成脚本可公开访问。此外，我们提供了一个Python库，用于简化对数据集的访问和在线重建的挑战。此外，该数据集还可用于转移学习以及稀疏和有限角度的重建方案。