---
license: mit
language:
- en
tags:
- 3d
- medical
- image-synthesis
- image-generation
- wavelet-transform
arxiv: 2402.19043
---

# WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis
This is the officical model repository of the paper "[**WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis**](https://pfriedri.github.io/wdm-3d-io)" by Paul Friedrich, Julia Wolleb, Florentin Bieder, Alicia Durrer and Philippe C. Cattin.

**WDM** is a wavelet-based medical image synthesis framework that can generate high-resolution medical images like CT or MR scans.
For more information on our method, we refer to our [**project page**](https://pfriedri.github.io/wdm-3d-io) or the [**paper**](https://arxiv.org/abs/2402.19043).

## Origial GitHub repository
If you want to use the pre-trained models provided in this repository, download the model weights and follow the instructions in the official [GitHub repository](https://github.com/pfriedri/wdm-3d).

## Pre-trained models
### BraTS 2023 (T1-weighted brain MR image generation)
- [Download model](brats_unet_128_1200k.pt) Resolution: 128 x 128 x 128, Backbone: U-Net, Trained: 1.2M iterations

### LIDC-IDRI (Lung CT image generation)
- [Download model](lidc-idri_128_unet_1200k.pt) Resolution: 128 x 128 x 128, Backbone: U-Net, Trained: 1.2M iterations

## Hardware requirements
To sample images from the provided models, you require a GPU with at least:
- 3 GB VRAM - for 128 x 128 x 128 (model uses ~2.55 GB)
- 8 GB VRAM - for 256 x 256 x 256 (model uses ~7.27 GB)

The models were trained on a system with an an AMD Epyc 7742 CPU and a NVIDIA A100 (40GB) GPU.

## Citation
If you find this work useful, please cite:
```bibtex
@article{friedrich2024wdm,
         title={WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis},
         author={Paul Friedrich and Julia Wolleb and Florentin Bieder and Alicia Durrer and Philippe C. Cattin},
         year={2024},
         journal={arXiv preprint arXiv:2402.19043}}
```