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NKI-AI/
direct-cvpr2022-recurrentvarnet

Name: direct-cvpr2022-recurrentvarnet
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DIRECT — CVPR 2022 RecurrentVarNet

Checkpoints from the DIRECT release related to Recurrent Variational Network (Yiasemis et al., CVPR 2022).

Paper: arXiv:2111.09639
Project: projects/cvpr2022_recurrentvarnet

Layout

text

calgary_campinas/<model>.{yaml,pt}
fastmri_axt1/<model>.{yaml,pt}

Directory	Dataset	Link
`calgary_campinas/`	Calgary-Campinas brain (multi-coil) — main experiments & ablations	sites.google.com/view/calgary-campinas-dataset
`fastmri_axt1/`	fastMRI brain AXT1 — paper appendix	fastmri.med.nyu.edu · fastmri.org/dataset

Challenge Poisson-disk masks: challenge site · Hub NKI-AI/direct-mri-masks.

Calgary-Campinas split used in the paper: 47 / 10 / 10 volumes (train / val / test) after discarding outer slices. Subsampling uses the challenge Poisson-disk masks. fastMRI AXT1 experiments use random Cartesian undersampling.

Calgary-Campinas

RecurrentVarNet (shared weights and ablations *_noRSI, *_noSER, *_T11) plus comparison baselines (rim, xpdnet, unet, varnet, …). Each inference YAML pins a single acceleration under inference.dataset.transforms.masking (default 5×; 10× commented) and sets crop_outer_slices: true.

bash

hf download NKI-AI/direct-cvpr2022-recurrentvarnet --local-dir ./cvpr_rvn

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

fastMRI AXT1 brain (paper appendix)

These checkpoints are from the appendix of Recurrent Variational Network (arXiv:2111.09639) (Yiasemis et al., CVPR 2022) — not the main Calgary-Campinas experiments. PDF: arxiv.org/pdf/2111.09639.

Models: recurrentvarnet, recurrentvarnet_t6_h128_nl2, lpd, rim, unet, varnet (recurrentvarnet_t6_h128_nl2 is the appendix ablation: T=6, 128 hidden channels, nl=2).

Inference YAMLs default to 4× random Cartesian undersampling (center_fractions: [0.08]). Commented 8× settings (accelerations: [8], center_fractions: [0.04]) are under inference.dataset.transforms.masking. Keep exactly one active acceleration list.

bash

direct predict ./predictions \
  --cfg ./cvpr_rvn/fastmri_axt1/recurrentvarnet.yaml \
  --checkpoint ./cvpr_rvn/fastmri_axt1/recurrentvarnet.pt \
  --data-root /path/to/fastmri/brain/multicoil_val \
  --num-gpus 1

Install DIRECT

bash

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]"

The first argument to direct predict is the prediction output directory.

Citation

If you use these models or DIRECT, please cite the DIRECT toolkit and the method paper(s) below.

DIRECT

bibtex

@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}
}

Method

bibtex

@inproceedings{yiasemis2022recurrentvarnet,
  title={Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated {MRI} Reconstruction},
  author={Yiasemis, George and Sonke, Jan-Jakob and S{\'a}nchez, Clara I. and Teuwen, Jonas},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2022}
}