Archived

This repository is no longer actively maintained. All new development has been moved to the following repository: https://github.com/MIC-DKFZ/Longitudinal4DMed

All the same features and functionalities are available there. Additionally, we implemented a continuous time version of Temporal Flow Matching.

Temporal Flow Matching

This repository is the official implementation of Temporal Flow Matching (TFM), a spatio-temporal and generative framework for longitudinal medical imaging.

The method is presented in the paper: Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging.

Features

• Flow Matching for sequence-to-image forecasting.
• Discrete variant (grid-based, e.g. regular follow-up times).
• Supports 3D+T or 4D sequences (e.g. MRI volumes).
• Simple, dependency-light PyTorch code.
• Supports longitudinal and spatio-temporal medical imaging datasets.

Status

This repository is still WIP. Regular updates will follow soon!

Installation

Clone this repository and install the required packages:

git clone https://github.com/MIC-DKFZ/Temporal-Flow-Matching.git
cd tfm
pip install -e .

Schematic

Contact

For further information, or if you want to reach out to us, visit our webpage.

Citation

If you find this work useful for your research, please consider citing:

@misc{disch2025temporalflowmatchinglearning,
      title={Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging}, 
      author={Nico Albert Disch and Yannick Kirchhoff and Robin Peretzke and Maximilian Rokuss and Saikat Roy and Constantin Ulrich and David Zimmerer and Klaus Maier-Hein},
      year={2025},
      eprint={2508.21580},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.21580}, 
}