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Cross-modality image synthesis from TOF-MRA to CTA

This repository contains the code for the paper. We explore diffusion-based image-to-image translation models to generate synthetic CTA images from TOF-MRA input.

Figure 1

Installation

We use Python 3.9 for the development. The code is written in JAX and Flax.

git clone https://github.com/alexander-koch/xmodality.git
cd xmodality
pip install -r requirements.txt

You can run the Diffusion Transformer locally on your Mac via Apple MLX. For MLX support, you need an M series chip (Apple silicon) and macOS >= 13.5.

pip install mlx

Download the weights

Pre-trained weights will be made available soon.

Converting some TOF-MRAs

You can either use inference.py or resampled_inference.py. Resampling additionally resamples to be in 256x256 range during generation. Use it like this:

python3 resampled_inference.py --input <path/to/tof> --output <path/to/cta> --load weights/uvit.pkl --arch uvit --bfloat16

or run the model on full resolution like this:

python3 inference.py --input <path/to/tof> --output <path/to/cta> --load weights/uvit.pkl --arch uvit --bfloat16

For Mac users, you can run the Diffusion Transformer model locally like this:

python3 mlx_inference.py --input <path/to/tof> --output <path/to/cta> --load <path/to/weights> --num_sample_steps <num_sample_steps>

All scripts print more detailed information on possible settings and parameters by using the --help flag, i.e.

python3 resampled_inference.py --help

Training and Evaluation

See Training for more information on how to train your model. See Evaluation for more information on how to evaluate a model.

Speed

Inference speed on a 512x512x160 TOF-MRA image using a batch size of 64, float32 precision, DDPM sampling, using the resampled inference script

Device Time (4 steps) Time (32 steps) Time (100 steps)
NVIDIA A40 ~15s ~50s ~2min
Apple M3 Pro ~40s ~6min ~17min

Organization

  • figures/ - Files to reproduce and generate data for the figures of the paper
  • extra/ - Files to reproduce and generate data for the appendix
  • sh/ - Scripts to run the experiments on the HPC
  • docs/ - Files for documentation
  • scripts/ - Additional scripts
  • compute_fd.py - Computes the Frechet Distance on ViT
  • external_validation.py - Computes 3D metrics on internal and external dataset

For anything evaluation related, you will need to download the ViT-B/16 weights.

Contributing

You found a mistake or have an improvement? Happy to hear, send us your pull requests or file an issue!

Citation

If you use this project, cite us as follows:

@misc{koch2024xmodality,
      title={Cross-modality image synthesis from {TOF-MRA} to {CTA} using diffusion-based models}, 
      author={Alexander Koch and Orhun Utku Aydin and Adam Hilbert and Jana Rieger and Satoru Tanioka and Fujimaro Ishida and Dietmar Frey},
      year={2024},
      eprint={2409.10089},
      archivePrefix={arXiv},
      primaryClass={eess.IV},
      url={https://arxiv.org/abs/2409.10089}, 
}