# The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods

arXiv: https://arxiv.org/abs/1910.01113 · HTML: https://mlsanity.com/paper/1910.01113/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2019.

## Built on by (most cited first, 12 of 12)

- [Feasibility-based Fixed Point Networks](https://mlsanity.com/paper/2104.14090/) (2021)
- [Explainable AI via Learning to Optimize](https://mlsanity.com/paper/2204.14174/) (2022)
- [Conditional Injective Flows for Bayesian Imaging](https://mlsanity.com/paper/2204.07664/) (2022)
- [Fourier Image Transformer](https://mlsanity.com/paper/2104.02555/) (2021)
- [Truly shift-equivariant convolutional neural networks with adaptive polyphase upsampling](https://mlsanity.com/paper/2105.04040/) (2021)
- [Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences](https://mlsanity.com/paper/2404.07671/) (2024)
- [Unsupervised Knowledge-Transfer for Learned Image Reconstruction](https://mlsanity.com/paper/2107.02572/) (2021)
- [Deep Learning Body Region Classification of MRI and CT examinations](https://mlsanity.com/paper/2104.13826/) (2021)
- [3D U-NetR: Low Dose Computed Tomography Reconstruction via Deep Learning and 3 Dimensional Convolutions](https://mlsanity.com/paper/2105.14130/) (2021)
- [Differentiable Uncalibrated Imaging](https://mlsanity.com/paper/2211.10525/) (2022)
- [Efficient Unrolled Networks for Large-Scale 3D Inverse Problems](https://mlsanity.com/paper/2601.02141/) (2026)
- [TD-Net: A Tri-domain network for sparse-view CT reconstruction](https://mlsanity.com/paper/2311.15369/) (2023)

## Abstract (arXiv)

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.