Large-Volume Conditioned 3D Latent Diffusion Models for CT Metal Artifact Suppression
Official model weights for the paper:
Large-Volume Conditioned 3D Latent Diffusion Models for CT Metal Artifact Suppression
Deep Generative Models for Medical Imaging (DGM4MICCAI), MICCAI 2026 Workshop (In press).
Authors: Xabier Moreno Casado, Jef Vandemeulebroucke, Jakub Ceranka
Organization: ETRO - Vrije Universiteit Brussel (VUB) & imec
GitHub Code: https://github.com/ETRO-MIT/3D-LDMs-for-CT-MAR
Checkpoints Included
| Filename | Architecture | Description |
|---|---|---|
vqvae_checkpoint.pth | Stage 1 3D VQ-VAE | 4x spatial compression autoencoder |
anatomy_ldm_checkpoint.pth | Model 2.1 | 3D LDM conditioned on artifacted CT |
anatomy_metadata_ldm_checkpoint.pth | Model 2.2 | 3D LDM conditioned on artifacted CT + implant metadata |
Usage
Clone the GitHub repository and download automatically:
git clone https://github.com/ETRO-MIT/3D-LDMs-for-CT-MAR.git
cd 3D-LDMs-for-CT-MAR
pip install -e ".[inference]"
python DownloadWeights.py
Citation
@inproceedings{casado2026largevolume,
title={Large-Volume Conditioned 3D Latent Diffusion Models for CT Metal Artifact Suppression},
author={Moreno Casado, Xabier and Vandemeulebroucke, Jef and Ceranka, Jakub},
booktitle={Deep Generative Models for Medical Imaging (DGM4MICCAI), MICCAI Workshop},
year={2026},
note={In press}
}