dmolino/CT-RATE_Generated_Scans

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 (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), 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
• 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:

      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}, 
}