language:
- en
license: apache-2.0
size_categories:
- 1K<n<10K
pretty_name: CT-RATE_Synthetic
tags:
- medical
task_categories:
- text-to-3d
Dataset Card for Synthetic Text-to-CT Scans - VLM3D Challenge
Dataset Details
Dataset Description
This dataset contains 1,000 synthetic 3D chest CT scans generated using the model introduced in
Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining (Molino et al., 2025).
The model was trained on the CT-RATE dataset, the largest publicly available collection of paired CT volumes and radiology reports.
It leverages a 3D latent diffusion framework combined with contrastive vision-language pretraining (3D-CLIP) to synthesize anatomically coherent and semantically faithful CT scans directly from clinical text prompts.
These 1,000 scans were generated for the VLM3D Challenge - Task 4, serving as a benchmark resource for multimodal evaluation and synthetic data research in medical imaging.
- Curated by: ArCo Lab – Università Campus Bio-Medico di Roma & Umeå University
- Language(s): Conditioning report are in English
- License: Apache 2.0
Dataset Sources
- Repository: GitHub Repository
- Paper: arXiv:2506.00633
- Challenge: VLM3D Challenge
Uses
Direct Use
- Benchmarking text-to-CT generative models.
- Data augmentation for classification, detection, or segmentation tasks.
- Research in multimodal vision-language learning for 3D medical imaging.
- Educational purposes and simulation in medical training.
Out-of-Scope Use
- Direct diagnostic or clinical use.
- Deployment in healthcare without proper validation and regulatory approval.
- Any attempt to re-identify patients (note: scans are fully synthetic).
Dataset Structure
- Format: Volumetric CT scans stored in NIfTI (
.nii.gz) format. - Resolution: Resampled to 0.75 × 0.75 × 3.0 mm voxel spacing, cropped/padded to 512 × 512 × 128.
- Intensity: Normalized in Hounsfield Units (clipped to [−1000, +1000]).
- Content: Synthetic chest CT scans across 18 pathological conditions (e.g., nodules, opacities, effusion, emphysema).
Dataset Creation
Curation Rationale
Created to provide a reproducible benchmark for text-to-CT generation and to supply synthetic volumetric data for research in data augmentation, privacy preservation, and multimodal foundation models.
Source Data
- Trained on CT-RATE (Hamamci et al., 2024), a large-scale dataset of chest CTs paired with radiology reports.
Annotations
No manual annotations included; diagnostic semantics are embedded via the conditioning text prompts used during generation.
Personal and Sensitive Information
- The dataset contains no real patient data.
- All scans are synthetic and generated by a model trained on anonymized public datasets.
Bias, Risks, and Limitations
- Synthetic data may not fully capture rare pathologies or distributional nuances of real-world scans.
- While useful for augmentation and benchmarking, these scans are not clinically validated.
- There is a potential risk if synthetic data are used without acknowledging their limitations in medical research.
Recommendations
Users should:
- Combine synthetic with real-world data for downstream tasks.
- Avoid over-relying on synthetic volumes for clinical translation.
- Report the provenance of synthetic data when used in publications.
Citation
If you use this dataset, please cite the following work:
BibTeX:
@article{molino2025textct,
title={Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining},
author={Molino, Daniele and Caruso, Camillo Maria and Ruffini, Filippo and Soda, Paolo and Guarrasi, Valerio},
journal={arXiv preprint arXiv:2506.00633},
year={2025}
}