deep-learning-recon skillA
deep-learning-recon is agent-read markdown (skill) from kewang0622/mri-research-skill: Deep-learning MRI reconstruction expert. Use for training or applying neural networks to reconstruct undersampled MRI — unrolled / variational networks (VarNet, MoDL, End-to-End VarNet, deep cascade), self-supervised training without fully-sampled data (SSDU), diffusion / score-based reconstruction, and the frameworks and datasets to do it. Tools: DIRECT, fastMRI, ATOMMIC, torchkbnufft; datasets fastMRI / mridata. For classical, training-free reconstruction (ESPIRiT/SENSE/GRAPPA, L1-wavelet PICS.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Deep-Learning MRI Reconstruction You are a DL-recon researcher. The dominant, robust paradigm is the **unrolled network**: unroll N iterations of an iterative solver, learn the regularizer/updates end-to-end, and keep the measured **data-consistency** step. Always anchor to data consistency — it's what guards against hallucinated structure. ## Project research memory For project experiments, read `.mri-research/INDEX.md` when present and retrieve only relevant preferences, environment notes and evidence-linked lessons. After meaningful runs or corrections, record outcomes, failures, limitations and next steps; revise scoped lessons without erasing history. Keep user preferences separate from scientific findings. Use the [project memory workflow](../mri-research/references/project-memory.md) to initialize the folder or connect project `CLAUDE.md` / `AGENTS.md`. If the hub is absent, retrieve the reference from the official skill repository. ## Tool setup before execution For any application this skill uses, check for a compatible installation and follow the official upstream's setup instructions. Within the authorized task, …
Read the whole file at its exact version.
How to install
mdr add kewang0622/mri-research-skill/deep-learning-recon@v0.7.0mdr add kewang0622/mri-research-skill/deep-learning-recon@sha256:4bc725ec8e560745Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_l3p6e5ph7xvl53ec)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| v0.7.0 latest | 2026-09-24 | 90d0c56 | 6,095 B | A | view · diff |
| v0.7.0 | 2026-09-24 | f90c25f | 5,482 B | A | view · diff |
| v0.7.0 | 2026-09-22 | c1cd332 | 4,652 B | A | view · diff |
| v0.1.0 | 2026-09-20 | a8f0a76 | 3,389 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (6095 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
kewang0622/mri-research-skill · 22 stars · license MIT · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_l3p6e5ph7xvl53ec GET https://markdownregistry.com/api/v1/resolve?ref=kewang0622/mri-research-skill/deep-learning-recon GET https://markdownregistry.com/api/v1/blob/4bc725ec8e560745ed4df0cd652076f69733ad4dd09332f410762eafbd6d411b
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