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--- |
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language: |
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- en |
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license: apache-2.0 |
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size_categories: |
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- 1K<n<10K |
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pretty_name: CT-RATE_Synthetic |
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tags: |
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- medical |
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task_categories: |
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- text-to-3d |
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--- |
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# Dataset Card for Synthetic Text-to-CT Scans - VLM3D Challenge |
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## Dataset Details |
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### Dataset Description |
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This dataset contains **1,000 synthetic 3D chest CT scans** generated using the model introduced in |
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[*Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining*](https://arxiv.org/abs/2506.00633) (Molino et al., 2025). |
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The model was trained on the **CT-RATE dataset**, the largest publicly available collection of paired CT volumes and radiology reports. |
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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. |
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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. |
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- **Curated by:** ArCo Lab – Università Campus Bio-Medico di Roma & Umeå University |
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- **Language(s):** Conditioning report are in English |
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- **License:** Apache 2.0 |
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### Dataset Sources |
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- **Repository:** [GitHub Repository](https://github.com/cosbidev/Text2CT) |
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- **Paper:** [arXiv:2506.00633](https://arxiv.org/abs/2506.00633) |
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- **Challenge:** [VLM3D Challenge](https://vlm3dchallenge.com) |
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## Uses |
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### Direct Use |
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- Benchmarking text-to-CT generative models. |
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- Data augmentation for classification, detection, or segmentation tasks. |
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- Research in multimodal vision-language learning for 3D medical imaging. |
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- Educational purposes and simulation in medical training. |
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### Out-of-Scope Use |
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- Direct diagnostic or clinical use. |
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- Deployment in healthcare without proper validation and regulatory approval. |
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- Any attempt to re-identify patients (note: scans are fully synthetic). |
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## Dataset Structure |
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- **Format:** Volumetric CT scans stored in NIfTI (`.nii.gz`) format. |
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- **Resolution:** Resampled to 0.75 × 0.75 × 3.0 mm voxel spacing, cropped/padded to 512 × 512 × 128. |
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- **Intensity:** Normalized in Hounsfield Units (clipped to [−1000, +1000]). |
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- **Content:** Synthetic chest CT scans across 18 pathological conditions (e.g., nodules, opacities, effusion, emphysema). |
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## Dataset Creation |
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### Curation Rationale |
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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. |
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### Source Data |
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- Trained on **CT-RATE** (Hamamci et al., 2024), a large-scale dataset of chest CTs paired with radiology reports. |
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### Annotations |
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No manual annotations included; diagnostic semantics are embedded via the conditioning text prompts used during generation. |
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### Personal and Sensitive Information |
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- The dataset contains **no real patient data**. |
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- All scans are **synthetic** and generated by a model trained on anonymized public datasets. |
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## Bias, Risks, and Limitations |
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- Synthetic data may not fully capture rare pathologies or distributional nuances of real-world scans. |
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- While useful for augmentation and benchmarking, these scans are **not clinically validated**. |
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- There is a potential risk if synthetic data are used without acknowledging their limitations in medical research. |
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### Recommendations |
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Users should: |
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- Combine synthetic with real-world data for downstream tasks. |
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- Avoid over-relying on synthetic volumes for clinical translation. |
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- Report the provenance of synthetic data when used in publications. |
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## Citation |
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If you use this dataset, please cite the following work: |
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**BibTeX:** |
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```bibtex |
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@article{molino2025textct, |
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title={Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining}, |
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author={Molino, Daniele and Caruso, Camillo Maria and Ruffini, Filippo and Soda, Paolo and Guarrasi, Valerio}, |
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journal={arXiv preprint arXiv:2506.00633}, |
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year={2025} |
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} |
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``` |
