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README.md

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-license: apache-2.0
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+license: apache-2.0
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+
+# 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*](https://arxiv.org/abs/2506.00633) (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](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
+@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}
+}