jiayangshi/lodochallenge_pixel_diffusion

Pixel Diffusion UNet – LoDoChallenge (DM4CT)

This repository contains the pretrained pixel-space diffusion UNet presented in the paper DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction (ICLR 2026).

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

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🔬 Model Overview

This model learns a prior over CT reconstruction images using a denoising diffusion probabilistic model (DDPM). It operates directly in pixel space (not latent space).

• Architecture: 2D UNet (Diffusers UNet2DModel)
• Input resolution: 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)

This model is intended to be combined with data-consistency correction for CT reconstruction tasks as detailed in the DM4CT benchmark.

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📊 Dataset: Low Dose Grand Challenge

Source: AAPM Low Dose CT Grand Challenge

Preprocessing steps:

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

The model learns an unconditional image prior over medical CT slices.

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🧠 Training Details

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

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🚀 Usage

You can use this model with the diffusers library:

from diffusers import DDPMPipeline

# Load the pretrained pipeline
pipeline = DDPMPipeline.from_pretrained("jiayangshi/lodochallenge_pixel_diffusion")

# Generate a sample (unconditional CT slice prior)
image = pipeline().images[0]
image.save("generated_ct_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}
}