---
license: mit
library_name: diffusers
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  
**DM4CT: Benchmarking Diffusion Models for CT Reconstruction (ICLR 2026)** benchmark.

🔗 Paper: https://openreview.net/forum?id=YE5scJekg5  
🔗 Arxiv: https://arxiv.org/abs/2602.18589  
🔗 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.

---

## 📊 Dataset: LoDoInd

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 image prior over CT slices.

---

## 🧠 Training Details

- Optimizer: AdamW
- Learning rate: 1e-4
- Batch size: (insert your batch size)
- Training steps: (insert number of steps)
- Hardware: NVIDIA A100 GPU

Training script:
https://github.com/DM4CT/DM4CT/blob/main/train_pixel.py

---

## 🚀 Usage

```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained("jiayangshi/lodoind_pixel_diffusion")
)

model.eval()