direct-calgary-campinas

DIRECT — Calgary-Campinas model zoo

Pretrained multi-coil MRI reconstruction baselines for the Calgary-Campinas MR reconstruction challenge, repackaged as <name>_<Rx>.yaml + <name>_<Rx>.pt pairs for DIRECT.

Originally hosted at files.aiforoncology.nl/direct-project. Metrics and context: DIRECT model zoo.

Dataset

Models

Filenames encode the fixed challenge acceleration (5x or 10x):

rim_5x.yaml / rim_5x.pt
rim_10x.yaml / rim_10x.pt
varnet_5x.yaml / …

Architectures include RIM, VarNet, LPDNet, XPDNet, KIKI-Net, U-Net, RecurrentVarNet, ConjGradNet, IterDualNet, JointICNet, and others. YAML configs match projects/calgary_campinas/ in the DIRECT repository.

Training / inference masking

Each checkpoint was trained for a single challenge rate (5× or 10×) with the CalgaryCampinas mask. Inference YAMLs already pin that one acceleration. To run the other rate, use the matching *_5x / *_10x file pair.

Install DIRECT

git clone https://github.com/NKI-AI/direct.git
cd direct
conda create --name direct python=3.12
conda activate direct
pip install meson-python meson ninja
pip install --no-build-isolation -e ".[dev]"

Usage

hf download NKI-AI/direct-calgary-campinas --local-dir ./calgary

direct predict ./predictions \
  --cfg ./calgary/rim_5x.yaml \
  --checkpoint ./calgary/rim_5x.pt \
  --data-root /path/to/calgary_campinas \
  --num-gpus 1

The first argument is the prediction output directory.

License

Creative Commons Attribution-ShareAlike 3.0 (aligned with the DIRECT model zoo release).

Citation

If you use these models or DIRECT, please cite:

@article{DIRECTTOOLKIT,
  title={DIRECT: Deep Image REConstruction Toolkit},
  author={Yiasemis, George and Moriakov, Nikita and Karkalousos, Dimitrios and Caan, Matthan and Teuwen, Jonas},
  journal={Journal of Open Source Software},
  volume={7},
  number={73},
  pages={4278},
  year={2022},
  doi={10.21105/joss.04278},
  url={https://doi.org/10.21105/joss.04278}
}