  Deep Learning approaches for solving Inverse Problems in imaging have become
very effective and are demonstrated to be quite competitive in the field.
Comparing these approaches is a challenging task since they highly rely on the
data and the setup that is used for training. We provide a public dataset of
computed tomography images and simulated low-dose measurements suitable for
training this kind of methods. With the LoDoPaB-CT Dataset we aim to create a
benchmark that allows for a fair comparison. It contains over 40,000 scan
slices from around 800 patients selected from the LIDC/IDRI Database. In this
paper we describe how we processed the original slices and how we simulated the
measurements. We also include first baseline results.
