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license: apache-2.0
library_name: direct
tags:
  - mri
  - reconstruction
  - modulated-convolution
  - fastmri
  - conditional
pipeline_tag: image-to-image

DIRECT — Modulated Convolution for Conditional MRI Reconstruction

Pretrained vSHARP models with modulated convolutions (and baselines) that condition reconstruction on acceleration (R) and ACS fraction at inference time.

Paper (MIDL 2026, PMLR 315): Conditional Learned Reconstruction for Medical Imaging
OpenReview: PDF
Project configs: projects/modulated_convolution

Datasets

Anatomy	Dataset	Link
Knee	fastMRI knee (multi-coil)	fastmri.med.nyu.edu
Prostate	fastMRI prostate (multi-coil)	fastmri.med.nyu.edu

Models

knee/<name>.yaml + knee/<name>.pt
prostate/<name>.yaml + prostate/<name>.pt

Knee

Name	Description
`vsharp_triang`	Non-modulated vSHARP baseline (triangular (R) sampling)
`vsharp_modconv_features_triang`	ModConv features, triangular
`vsharp_modconv_features_triang_32_16`	ModConv features, MLP 32→16
`vsharp_modconv_features_triang_32_8`	ModConv features, MLP 32→8
`vsharp_modconv_features_triang_32_16_mod_inp`	ModConv at input

Prostate

Name	Description
`vsharp_triang`	Non-modulated baseline
`vsharp_modconv_features_triang`	ModConv features
`vsharp_modconv_features_triang_32_16`	ModConv features, MLP 32→16
`vsharp_modconv_features_triang_32_8`	ModConv features, MLP 32→8

Training protocol

Training samples acceleration in a triangular / range schedule (typically (R \in [4, 16])) with paired ACS center_fractions (e.g. [0.08, 0.02]) under FastMRIEquispaced. Models are conditioned on the realized (R) / ACS so a single checkpoint covers the training range.

Released inference YAMLs pin exactly one (R) and one ACS (default validation 4×: accelerations: [4], center_fractions: [0.08]). Keep lists of length 1 when changing rate:

Target (R)	`accelerations`	`center_fractions`
4×	`[4]`	`[0.08]`
8×	`[8]`	`[0.04]`
16×	`[16]`	`[0.02]`

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-modulated-convolution --local-dir ./modconv

direct predict ./predictions \
  --cfg ./modconv/knee/vsharp_modconv_features_triang_32_8.yaml \
  --checkpoint ./modconv/knee/vsharp_modconv_features_triang_32_8.pt \
  --data-root /path/to/fastmri/knee/multicoil_val \
  --num-gpus 1

The first argument 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

@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

@inproceedings{moriakov2026modconv,
  title={Conditional Learned Reconstruction for Medical Imaging},
  author={Moriakov, Nikita and Yiasemis, George and Sonke, Jan-Jakob and Teuwen, Jonas},
  booktitle={Medical Imaging with Deep Learning},
  year={2026},
  url={https://proceedings.mlr.press/v315/moriakov26a.html}
}