Supplementing record containing the LoDoPaB-CT challenge submissions compared in the article "Quantitative comparison of deep learning-based image reconstruction methods for low-dose and sparse-angle CT applications". Below are references for the included methods. cinn: A. Denker et al., 2020, Conditional Normalizing Flows for Low-Dose Computed Tomography Image Reconstruction diptv: D. Otero Baguer et al., 2020, Computed tomography reconstruction using deep image prior and learned reconstruction methods (DIVαℓ implementation) fbp: Filtered back-projection (ODL implementation) fbpistaunet: T. Liu et al., 2020, Interpreting U-Nets via Task-Driven Multiscale Dictionary Learning (Implementation by T. Liu) fbpmsdnet: D. Pelt et al., 2017, A mixed-scale dense convolutional neural network for image analysis (Implementation based on msd_pytorch by A. Hendriksen) fbpunet: K. H. Jin et al., 2017, Deep Convolutional Neural Network for Inverse Problems in Imaging (DIVαℓ implementation) fbpunetpp: Z. Zhou et al., 2018, UNet++: A Nested U-Net Architecture for Medical Image Segmentation (Implementation and network weights by A. Hadjifaradji) ictnet: D. Bauer et al., 2021, iCTU-Net (submitted, based on iCT-Net) learnedpd: J. Adler et al., 2018, Learned Primal-Dual Reconstruction (DIVαℓ implementation) tv: Total Variation Regularization (DIVαℓ implementation)