ArticleScientific data2021
LoDoPaB-CT, a benchmark dataset for low-dose computed tomography reconstruction.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Visual theranostic oral nanozymes for IBD from principles to preclinical design.Bioactive materials · 2026Review
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- FMDNet: Spatial-frequency feature routing for low-dose CT denoising.Journal of applied clinical medical physics · 2026Article
- WE-UNet: A Wavelet-enhanced U-Net framework for radiation dose reduction in chest radiography.Journal of applied clinical medical physics · 2026Article
- A cone-beam photon-counting CT dataset for spectral image reconstruction and deep learning.Scientific data · 2025Article
- Learned denoising with simulated and experimental low-dose CT data.Scientific reports · 2025Article
- Lack of children in public medical imaging data points to growing age bias in biomedical AI.Research square · 2025Article
- No Reproducibility, No Progress: Rethinking CT Benchmarking.Journal of imaging · 2025Article
- Lack of children in public medical imaging data points to growing age bias in biomedical AI.medRxiv : the preprint server for health sciences · 2025Article
- Deep learning-based segmentation of ultra-low-dose CT images using an optimized nnU-Net model.La Radiologia medica · 2025Article
- Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.Nature communications · 2025Article
- Conditioning generative latent optimization for sparse-view computed tomography image reconstruction.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Exploring connections of spectral analysis and transfer learning in medical imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Federated Metadata-Constrained iRadonMAP Framework with Mutual Learning for All-in-One Computed Tomography Imaging.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Low-dose CT reconstruction using dataset-free learning.PloS one · 2024Article
- Deep Learning Body Region Classification of MRI and CT Examinations.Journal of digital imaging · 2023Article
- Explainable AI via learning to optimize.Scientific reports · 2023Article
- Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives.Medical image analysis · 2023Review
- An AI-Based Colonic Polyp Classifier for Colorectal Cancer Screening Using Low-Dose Abdominal CT.Sensors (Basel, Switzerland) · 2022Article
- Unsupervised knowledge-transfer for learned image reconstruction.Inverse problems · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Deep learning approaches for tomographic image reconstruction have become very effective and have been demonstrated to be competitive in the field. Comparing these approaches is a challenging task as they rely to a great extent on the data and setup used for training. With the Low-Dose Parallel Beam (LoDoPaB)-CT dataset, we provide a comprehensive, open-access database of computed tomography images and simulated low photon count measurements. It is suitable for training and comparing deep learning methods as well as classical reconstruction approaches. The dataset contains over 40000 scan slices from around 800 patients selected from the LIDC/IDRI database. The data selection and simulation setup are described in detail, and the generating script is publicly accessible. In addition, we provide a Python library for simplified access to the dataset and an online reconstruction challenge. Furthermore, the dataset can also be used for transfer learning as well as sparse and limited-angle reconstruction scenarios.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.