---
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
[*From Alignment to Synthesis: Contrastive Volumetric Grounding for Text-to-CT Generation*](https://arxiv.org/abs/2506.00633) (Molino et al., BMVC 2026).

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.

> **Note.** These scans were generated with the earlier version of the model ([arXiv v2](https://arxiv.org/abs/2506.00633v2)), the one submitted to the VLM3D Challenge.

- **Curated by:** ArCo Lab – Università Campus Bio-Medico di Roma & Umeå University
- **Language(s):** Conditioning reports are in English
- **License:** Apache 2.0

### Dataset Sources

- **Repository:** [GitHub Repository](https://github.com/cosbidev/Text2CT)
- **Paper:** [arXiv:2506.00633](https://arxiv.org/abs/2506.00633)
- **Challenge:** [VLM3D Challenge](https://vlm3dchallenge.com)

## 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:**

```bibtex@misc{molino2026alignment,
      title={From Alignment to Synthesis Contrastive Volumetric Grounding for Text-to-CT Generation}, 
      author={Daniele Molino and Camillo Maria Caruso and Filippo Ruffini and Paolo Soda and Valerio Guarrasi},
      year={2026},
      eprint={2506.00633},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.00633}, 
}
```