This is the official PyTorch implementation of the paper WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis by Paul Friedrich, Julia Wolleb, Florentin Bieder, Alicia Durrer and Philippe C. Cattin.

If you find our work useful, please consider to ⭐ star this repository and 📝 cite our paper:

@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}}

Due to the three-dimensional nature of CT- or MR-scans, generative modeling of medical images is a particularly challenging task. Existing approaches mostly apply patch-wise, slice-wise, or cascaded generation techniques to fit the high-dimensional data into the limited GPU memory. However, these approaches may introduce artifacts and potentially restrict the model's applicability for certain downstream tasks. This work presents WDM, a wavelet-based medical image synthesis framework that applies a diffusion model on wavelet decomposed images. The presented approach is a simple yet effective way of scaling diffusion models to high resolutions and can be trained on a single 40 GB GPU. Experimental results on BraTS and LIDC-IDRI unconditional image generation at a resolution of 128 x 128 x 128 show state-of-the-art image fidelity (FID) and sample diversity (MS-SSIM) scores compared to GANs, Diffusion Models, and Latent Diffusion Models. Our proposed method is the only one capable of generating high-quality images at a resolution of 256 x 256 x 256.

We recommend using a conda environment to install the required dependencies. You can create and activate such an environment called wdm by running the following commands:

mamba env create -f environment.yml
mamba activate wdm

For training a new model or sampling from an already trained one, you can simply adapt and use the script run.sh. All relevant hyperparameters for reproducing our results are automatically set when using the correct MODEL in the general settings. For executing the script, simply use the following command:

bash run.sh

Supported settings (set in run.sh file):

MODE: 'training', 'sampling'

MODEL: 'ours_unet_128', 'ours_unet_256', 'ours_wnet_128', 'ours_wnet_256'

DATASET: 'brats', 'lidc-idri'

We released pretrained models on HuggingFace.

Currently available models:

• BraTS 128: BraTS, 128 x 128 x 128, U-Net backbone, 1.2M Iterations
• LIDC-IDRI 128: LIDC-IDRI, 128 x 128 x 128, U-Net backbone, 1.2M Iterations

To ensure good reproducibility, we trained and evaluated our network on two publicly available datasets:

• BRATS 2023: Adult Glioma, a dataset containing routine clinically-acquired, multi-site multiparametric magnetic resonance imaging (MRI) scans of brain tumor patients. We just used the T1-weighted images for training. The data is available here.

• LIDC-IDRI, a dataset containing multi-site, thoracic computed tomography (CT) scans of lung cancer patients. The data is available here.

The provided code works for the following data structure (you might need to adapt the DATA_DIR variable in run.sh):

data
└───BRATS
    └───BraTS-GLI-00000-000
        └───BraTS-GLI-00000-000-seg.nii.gz
        └───BraTS-GLI-00000-000-t1c.nii.gz
        └───BraTS-GLI-00000-000-t1n.nii.gz
        └───BraTS-GLI-00000-000-t2f.nii.gz
        └───BraTS-GLI-00000-000-t2w.nii.gz  
    └───BraTS-GLI-00001-000
    └───BraTS-GLI-00002-000
    ...

└───LIDC-IDRI
    └───LIDC-IDRI-0001
      └───preprocessed.nii.gz
    └───LIDC-IDRI-0002
    └───LIDC-IDRI-0003
    ...

We provide a script for preprocessing LIDC-IDRI. Simply run the following command with the correct path to the downloaded DICOM files DICOM_PATH and the directory you want to store the processed nifti files NIFTI_PATH:

python utils/preproc_lidc-idri.py --dicom_dir DICOM_PATH --nifti_dir NIFTI_PATH

• HA-GAN: For implementing the paper Hierarchical Amortized GAN for 3D High Resolution Medical Image Synthesis, we use the publicly available implementation. We follow the implementation details presented in the original paper (Section E). The authors recommend cutting all zero slices from the volumes before training. To allow a fair comparison with other methods, we have omitted this step.
• 3D-LDM: For implementing the paper Denoising Diffusion Probabilistic Models for 3D Medical Image Generation, we use the publicly available implementation. We follow the implementation details presented in the Supplementary Material of the original paper (Supplementary Table 1).
• 2.5D-LDM: For implementing the paper Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis, we adopted the method to work for image generation. We trained a VQ-VAE (downsampling factor 4, latent dimension 32) using an implementation from MONAI Generative and a diffusion model implementation from OpenAI. For implementing the pseudo 3D layers, we use a script provided by the authors. To allow for image generation, we sample all slices at once - meaning that the models batch size and the dimension of the 1D convolution is equal to the number of slices in the volume to be generated.
• 3D DDPM: For implementing a memory efficient baseline model, we use the 3D DDPM presented in the paper Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing, and used the publicly available implementation. We use additive skip connections and train the model with the same hyperparameters as our models.

All experiments were performed on a system with an AMD Epyc 7742 CPU and a NVIDIA A100 (40GB) GPU.

We plan to add further functionality to our framework:

• Add compatibility for more datasets like MRNet, ADNI, or fastMRI
• Release pre-trained models
• Extend the framework for 3D image inpainting
• Extend the framework for 3D image-to-image translation

Our code is based on / inspired by the following repositories:

• https://github.com/openai/guided-diffusion (published under MIT License)
• https://github.com/FlorentinBieder/PatchDDM-3D (published under MIT License)
• https://github.com/VinAIResearch/WaveDiff (published under GNU General Public License v3.0)
• https://github.com/LiQiufu/WaveCNet (published under CC BY-NC-SA 4.0 License)

For computing FID scores we use a pretrained model (resnet_50_23dataset.pth) from:

• https://github.com/Tencent/MedicalNet (published uner MIT License)

Thanks for making these projects open-source.