Latent Diffusion Model – LoDoChallenge (DM4CT)

This repository contains the pretrained latent-space diffusion model used in the benchmark DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction (ICLR 2026).

• Paper: DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
• Project Page: https://dm4ct.github.io/DM4CT/
• Codebase: https://github.com/DM4CT/DM4CT

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🔬 Model Overview

This model learns a prior over CT reconstruction images in a compressed latent space using a denoising diffusion probabilistic model (DDPM).

Unlike the pixel diffusion model, diffusion is performed in the latent space of a pretrained autoencoder.

• Architecture:
  • VQ-VAE (image encoder/decoder)
  • 2D UNet operating in latent space
• Input resolution (image space): 512 × 512
• Channels: 1 (grayscale CT slice)
• Training objective: ε-prediction (standard DDPM formulation)
• Noise schedule: Linear beta schedule
• Training dataset: Low Dose Grand Challenge (LoDoChallenge)
• Intensity normalization: Rescaled to (-1, 1)

The diffusion model operates purely in latent space and relies on the autoencoder for encoding and decoding. This model is intended to be combined with data-consistency correction for CT reconstruction.

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📊 Dataset: Low Dose Grand Challenge

Source: https://www.aapm.org/grandchallenge/lowdosect/

Preprocessing steps:

• Train/test split
• Rescale reconstructed slices to (-1, 1)
• No geometry information is embedded in the model

The model learns an unconditional latent prior over CT slices.

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🧠 Training Details

• Optimizer: AdamW
• Learning rate: 1e-4
• Hardware: NVIDIA A100 GPU
• Training scripts: train_latent.py

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🚀 Usage

from diffusers import DiffusionPipeline
import torch

pipeline = DiffusionPipeline.from_pretrained(
    "jiayangshi/lodochallenge_latent_diffusion"
)

pipeline.to("cuda")

# Generate an unconditional CT slice prior
image = pipeline(batch_size=1).images[0]
image.save("reconstructed_slice.png")

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Citation

@inproceedings{shi2026dmct,
title={{DM}4{CT}: Benchmarking Diffusion Models for Computed Tomography Reconstruction},
author={Shi, Jiayang and Pelt, Dani{\"e}l M and Batenburg, K Joost},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=YE5scJekg5}
}