direct
importedsoftware/direct
Deep learning framework for MRI reconstruction
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- Apache-2.0(osi)
- Status
- active
- Maturity
- deployed
- Organization
- NKI-AI
- Country
- unknown
- Homepage
- docs.aiforoncology.nl/direct
- Repository
- github.com/NKI-AI/direct
- Documentation
- unknown
- Tags
- deep-learning · fastmri-challenge · inverse-problems · medical-imaging · mri-reconstruction · pytorch
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- sigmanetfastmri-challenge · mri-reconstruction
Sigmanet: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction,
- fastmri-reproducible-benchmarkfastmri-challenge · mri-reconstruction
Try several methods for MRI reconstruction on the fastmri dataset. Home to the XPDNet, runner-up of the 2020 fastMRI challenge.
- meddlrinverse-problems · medical-imaging
A flexible ML framework built to simplify medical image reconstruction and analysis experimentation.
- cs-mri-ganinverse-problems
Structure preserving Compressive Sensing MRI Reconstruction using Generative Adversarial Networks (CVPRW 2020)
- FlexSIMinverse-problems
A flexible SIM reconstruction method capable to handle difficult data prone to reconstruction artifacts.
- irim_fastMRIinverse-problems
i-RIM applied to the fastMRI challenge data.
- api.github.com/repos/NKI-AI/directretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2026-08-05, 309 stars, license reported as Apache-2.0. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/28.json→ .entries["direct"]
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