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license: apache-2.0
library_name: direct
tags:
- mri
- reconstruction
- adaptive-sampling
- dynamic-mri
- cmrxrecon
pipeline_tag: image-to-image
---
# DIRECT — End-to-End Adaptive Sampling & Reconstruction
Pretrained models for joint learning of an adaptive \(k\)-space sampler and a reconstruction network on dynamic MRI (MIDL 2026).
- Paper (PMLR): [End-to-End Co-Optimization of Adaptive k-space Sampling and Reconstruction for Dynamic MRI](https://proceedings.mlr.press/v315/yiasemis26a.html)
- OpenReview: [forum](https://openreview.net/forum?id=0yrf87zVf2)
- Related: [arXiv:2403.10346](https://arxiv.org/abs/2403.10346)
- Companion (with registration): [`NKI-AI/direct-e2e-ads-recon-reg`](https://huggingface.co/NKI-AI/direct-e2e-ads-recon-reg)
## Dataset
| | |
|---|---|
| **Data** | [CMRxRecon](https://cmrxrecon.github.io/) multi-coil cardiac cine |
| **Challenge / site** | [cmrxrecon.github.io](https://cmrxrecon.github.io/) |
| **Task** | Adaptive sampling + reconstruction under a fixed acceleration budget |
## Models
Each experiment is a `.yaml` + `.pt` pair (inference YAML active at **4×**; other rates are commented under `masking`):
| Name | Reconstruction | Sampler |
|------|----------------|---------|
| `vsharp_ads_1d` | vSHARP | ADS 1D, unified mask |
| `medl_ads_1d` | MEDL | ADS 1D, unified |
| `vsharp_ads_1d_frame` | vSHARP | ADS 1D, frame-specific |
| `medl_ads_1d_frame` | MEDL | ADS 1D, frame-specific |
| `vsharp_ads_1d_init2` | vSHARP | ADS 1D unified + init |
| `medl_ads_1d_init2` | MEDL | ADS 1D unified + init |
| `vsharp_ads_1d_frame_init2` | vSHARP | ADS 1D frame-specific + init |
| `medl_ads_1d_frame_init2` | MEDL | ADS 1D frame-specific + init |
| `vsharp_ads_2d` | vSHARP | ADS 2D, unified |
| `medl_ads_2d` | MEDL | ADS 2D, unified |
| `vsharp_ads_2d_frame` | vSHARP | ADS 2D, frame-specific |
| `medl_ads_2d_frame` | MEDL | ADS 2D, frame-specific |
Full training configs: [`projects/e2e_ads_recon`](https://github.com/NKI-AI/direct/tree/main/projects/e2e_ads_recon).
## Acceleration rates
Hub YAMLs pin **one** active `accelerations` / `center_fractions` pair (default `val-4x`). To run another trained rate, uncomment the matching block under `masking` (and comment out the active lists). For `*init2*`, also update `target_acceleration`.
## 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]"
```
## Usage
```bash
hf download NKI-AI/direct-e2e-ads-recon --local-dir ./e2e_ads_recon
direct predict ./predictions \
--cfg ./e2e_ads_recon/vsharp_ads_1d.yaml \
--checkpoint ./e2e_ads_recon/vsharp_ads_1d.pt \
--data-root /path/to/cmrxrecon \
--num-gpus 1
```
The first argument is the **prediction output directory**.
## Citation
If you use these models or [DIRECT](https://github.com/NKI-AI/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{yiasemis2026e2eads,
title={End-to-End Co-Optimization of Adaptive k-space Sampling and Reconstruction for Dynamic {MRI}},
author={Yiasemis, George and Moriakov, Nikita and Sonke, Jan-Jakob and Teuwen, Jonas},
booktitle={Medical Imaging with Deep Learning},
year={2026}
}
```
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