README.md

metadata

library_name: diffusers
license: mit
pipeline_tag: image-to-image
tags:
  - computed-tomography
  - ct-reconstruction
  - diffusion-model
  - inverse-problems
  - dm4ct
  - sparse-view-ct

Pixel Diffusion UNet – LoDoInd (DM4CT)

This repository contains the pretrained pixel-space diffusion UNet used in the benchmark study DM4CT: Benchmarking Diffusion Models for CT Reconstruction (ICLR 2026).

• Paper: DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
• ArXiv: https://arxiv.org/abs/2602.18589
• Project Page: https://dm4ct.github.io/DM4CT/
• Codebase: https://github.com/DM4CT/DM4CT

---

🔬 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: Industry CT dataset (LoDoInd)
• Intensity normalization: Rescaled to (-1, 1)

This model is intended to be combined with data-consistency correction for CT reconstruction tasks.

---

📊 Dataset: LoDoInd

Source: LoDoInd on Zenodo

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 CT slices.

---

🧠 Training Details

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

---

🚀 Usage

from diffusers import DDPMPipeline

# Load the pipeline
pipeline = DDPMPipeline.from_pretrained("jiayangshi/lodoind_pixel_diffusion")
pipeline.to("cuda")

# Generate a CT slice prior
image = pipeline().images[0]
image.save("generated_ct_slice.png")

---

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}
}