# 2DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning

arXiv: https://arxiv.org/abs/2306.05907 · HTML: https://mlsanity.com/paper/2306.05907/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 33 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning](https://mlsanity.com/paper/1904.02816/) (2019)
- [A Cone-Beam X-Ray CT Data Collection designed for Machine Learning](https://mlsanity.com/paper/1905.04787/) (2019)

## Built on by (most cited first, 9 of 9)

- [OSNet & MNetO: Two Types of General Reconstruction Architectures for Linear Computed Tomography in Multi-Scenarios](https://mlsanity.com/paper/2309.11858/) (2023)
- [Learned reconstruction methods for inverse problems: sample error estimates](https://mlsanity.com/paper/2312.14078/) (2023)
- [Learned denoising with simulated and experimental low-dose CT data](https://mlsanity.com/paper/2408.08115/) (2024)
- [Self-Supervised Denoiser Framework](https://mlsanity.com/paper/2411.19593/) (2024)
- [Unsupervised Deep Learning for Inverse Problems in Computed Tomography](https://mlsanity.com/paper/2508.05321/) (2025)
- [Improving the Generalisation of Learned Reconstruction Frameworks](https://mlsanity.com/paper/2511.12730/) (2025)
- [Latent Space Inference via Paired Autoencoders](https://mlsanity.com/paper/2601.11397/) (2026)
- [A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems](https://mlsanity.com/paper/2606.30230/) (2026)
- [Design Choices in Splitting-Based Self-Supervised Sparse-View CT Reconstruction](https://mlsanity.com/paper/2607.10898/) (2026)

## Abstract (arXiv)

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.