Text-to-CT Generation via 3D Latent Diffusion Model

This repository hosts the official project page for our work:

Text-to-CT Generation via 3D Latent Diffusion Model with Contrastive Vision-Language Pretraining
Molino, D., Caruso, C. M., Ruffini, F., Soda, P., Guarrasi, V. (2025)

🧠 Model Overview

Our approach combines:

• A 3D CLIP-style encoder for vision-language alignment between CT volumes and radiology reports.
• A volumetric VAE for latent compression of 3D CT data.
• A latent diffusion model with cross-attention conditioning for controllable text-to-CT generation.

This design enables direct synthesis of anatomically consistent, semantically faithful, and high-resolution CT volumes from textual descriptions.

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📦 Synthetic Dataset

We release 1,000 synthetic chest CT scans generated with our model for the VLM3D Challenge.
➡️ Available on Hugging Face: Synthetic Text-to-CT Dataset

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📜 Paper

• Preprint: arXiv:2506.00633

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🚧 Code Release

The full training and inference code will be made available soon.
Stay tuned for updates! ✨

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📬 Contact

For questions or collaborations, please reach out to:
Daniele Molino – daniele.molino@unicampus.it

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