mlsanityData to 7 Oct 2026

A Cone-Beam X-Ray CT Data Collection designed for Machine Learning

arXiv 1905.04787 ↗First posted 12 May 2019eess.IV cs.LG cs.NAcited by 21

Not on the map. Why?

Abstract (arXiv)

Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 6; 2024: 6; 2025: 2; 2026: 4.

21 papers.

Sort:

By area:

  1. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  2. 2022Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Datacited by 17
  3. 2023Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstructioncited by 15
  4. 2023Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstructioncited by 12
  5. 20232DeteCT -- A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learningcited by 9
  6. 2024Implicit Neural Representations for Robust Joint Sparse-View CT Reconstructioncited by 9
  7. 2024Deep Learning Computed Tomography based on the Defrise and Clack Algorithmcited by 6
  8. 2022Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equationscited by 3
  9. 2024AC-IND: Sparse CT reconstruction based on attenuation coefficient estimation and implicit neural distributioncited by 3
  10. 2024Swap-Net: A Memory-Efficient 2.5D Network for Sparse-View 3D Cone Beam CT Reconstructioncited by 3
  11. 2024Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT Reconstructioncited by 3
  12. 2025TomoSelfDEQ: Self-Supervised Deep Equilibrium Learning for Sparse-Angle CT Reconstructioncited by 3
9 more
  1. 2023RED-PSM: Regularization by Denoising of Factorized Low Rank Models for Dynamic Imagingcited by 2
  2. 2025RSR-NF: Neural Field Regularization by Static Restoration Priors for Dynamic Imagingcited by 2
  3. 2024Self-Supervised Denoiser Frameworkcited by 1
  4. 2026Efficient Unrolled Networks for Large-Scale 3D Inverse Problemscited by 1
  5. 2026A Stability Benchmark of Generative Regularizers for Inverse Problemscited by 1
  6. 2022Stable Optimization for Large Vision Model Based Deep Image Prior in Cone-Beam CT Reconstructioncited by 0
  7. 2023HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learningcited by 0
  8. 2026Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstructioncited by 0
  9. 2026Enabling self-supervised learned primal dual with Noise2Inversecited by 0