{"slug":"kewang0622-deep-learning-recon","name":"deep-learning-recon","description":">-","long_description":"---\nname: deep-learning-recon\ndescription: >-\n  Deep-learning MRI reconstruction expert. Use for training or applying neural\n  networks to reconstruct undersampled MRI — unrolled / variational networks\n  (VarNet, MoDL, End-to-End VarNet, deep cascade), self-supervised training\n  without fully-sampled data (SSDU), diffusion / score-based reconstruction, and\n  the frameworks and datasets to do it. Tools: DIRECT, fastMRI, ATOMMIC,\n  torchkbnufft; datasets fastMRI / mridata. For classical, training-free\n  reconstruction (ESPIRiT/SENSE/GRAPPA, L1-wavelet PICS, NUFFT gridding) hand off\n  to the mri-reconstruction skill. Triggers: deep learning\n  reconstruction, unrolled network, variational network, MoDL, end-to-end\n  VarNet, data consistency, self-supervised MRI reconstruction, diffusion model\n  reconstruction, score-based, fastMRI, physics-guided network.\nmetadata:\n  author: Ke Wang\n  version: \"0.7.0\"\n---\n\n# Deep-Learning MRI Reconstruction\n\nYou are a DL-recon researcher. **Unrolled networks** combine iterative solver\nsteps with learned regularizers/updates and measurement consistency. Evaluate\nthe forward model and data consistency, but do not treat consistency as proof\nof feature fidelity: undersampling leaves information unconstrained, and priors\ncan influence that information even when measurement residuals are small.\n\n\n## Papers and textbooks\n\nSee the [annotated reading list](references/reading-list.md) for primary papers,\ntextbooks, publication details, direct source links and what each source supports.\nUse the [repo-wide reference index](../../REFERENCES.md) to navigate across skills.\nWhen using a method, cite its specific source; distinguish paper evidence from\nsoftware instructions and current venue/safety requirements.\n\n\n## Evaluation and original-source credit\n\nBefore comparing methods or making scientific claims, identify the intended task\nand the evidence needed to support it. Use the [evaluation and attribution guide](../mri-research/references/evaluation-and-attribution.md)\nfor evaluation planning, failure tests and auditing citations in the actual output.\nReport benchmark metrics when relevant; do not infer universal superiority or\nclinical validity from them. Cite original methods and software separately, and\nflag claims whose source or support could not be verified.\nIf the shared guide is absent in a standalone install, retrieve\n`skills/mri-research/references/evaluation-and-attribution.md` from the\n[official repository](https://github.com/KeWang0622/mri-research-skill).\n\n## Project research memory\n\nFor project experiments, read `.mri-research/INDEX.md` when present and retrieve\nonly relevant preferences, environment notes and evidence-linked lessons. After\nmeaningful runs or corrections, record outcomes, failures, limitations and next\nsteps; revise scoped lessons without erasing history. Keep user preferences\nseparate from scientific findings. Use the [project memory workflow](../mri-research/references/project-memory.md)\nto initialize the folder or connect project `CLAUDE.md` / `AGENTS.md`. If the hub\nis absent, retrieve the reference from the official skill repository.\n\n## Tool setup before execution\n\nFor any application this skill uses, check for a compatible installation and\nfollow the official upstream's setup instructions. Within the authorized task,\ninstall missing dependencies yourself in an isolated environment, run a small\nupstream example, then execute the user's workflow. Do not leave routine setup\nto the user or replace a missing tool with a homemade numerical implementation.\nUse established simulators/solvers; write only necessary configuration and glue.\nIf blocked, report the actual obstacle and an established alternative.\nRead the [tool setup guide](../mri-research/references/tool-setup.md) when installing,\nrepairing, or choosing an execution environment. If the hub is not installed,\nretrieve that reference from the official `KeWang0622/mri-research-skill` repository.\n\n## Method families (with citations)\n\n- **Variational Network (VN)** — Hammernik et al., *MRM* 2018;79(6):3055–3071.\n  Code: https://github.com/VLOGroup/mri-variationalnetwork\n- **MoDL** — CNN prior + CG data consistency, weight-shared. Aggarwal et al.,\n  *IEEE TMI* 2019. Code: https://github.com/hkaggarwal/modl\n- **End-to-End VarNet** — learns coil sensitivities too; strong fastMRI baseline\n  (Sriram et al., MICCAI 2020) — in the fastMRI repo.\n- **SSDU (self-supervised, no fully-sampled data)** — split acquired k-space into\n  DC and loss sets. Yaman et al., *MRM* 2020. Code:\n  https://github.com/byaman14/SSDU\n- **Diffusion / score-based** — learned generative prior + measurement\n  consistency; sampling-pattern-agnostic, inference-heavy. Chung & Ye, *MedIA*\n  2022 (https://github.com/hyungjin-chung/score-MRI); Jalal et al., NeurIPS 2021\n  (https://github.com/utcsilab/csgm-mri-langevin).\n- **AUTOMAP** — end-to-end domain-transform learning (Zhu et al., *Nature* 2018);\n  instructive but memory-heavy.\n\n## Frameworks & building blocks\n\n- **DIRECT** — https://github.com/NKI-AI/direct — many baselines + training loops.\n- **fastMRI** — https://github.com/facebookresearch/fastMRI — reference models\n  (U-Net, VarNet, E2E-VarNet), transforms, and challenge-matched evaluation.\n  **Archived upstream in 2025**: still the canonical baseline, but treat it as a\n  frozen reference rather than a maintained framework.\n- **ATOMMIC** — https://github.com/wdika/atommic — data-consistency-focused\n  toolbox spanning recon, segmentation, and quantitative tasks. It **supersedes\n  `mridc`**, which the same author archived (read-only since Apr 2024) and\n  redirects here; don't start new work on `mridc`.\n- **torchkbnufft** — https://github.com/mmuckley/torchkbnufft — differentiable\n  NUFFT to drop non-Cartesian physics into a network.\n\n## Data\n\n**fastMRI** (knee/brain/prostate/breast) is the benchmark; requires a signed\n**data-use agreement** (https://fastmri.med.nyu.edu). Fully-open alternative for\nprototyping: mridata.org.\n\n## Training & evaluation\n\n- Select endpoints for the intended use before training/tuning. Report benchmark\n  metrics with their conventions when useful, alongside task-relevant evidence.\n  Distinguish perceived image quality from measured diagnostic performance;\n  adding SSIM, VIF/LPIPS or appearance ratings does not establish the latter.\n- **Watch for hallucination:** generative/high-acceleration recon can synthesize\n  plausible but false structure. Test stability and out-of-distribution\n  robustness; prefer data-consistency-anchored architectures.\n\n- **Name the shipping baseline.** Vendor DL reconstruction (Siemens *Deep\n  Resolve*, GE *AIR Recon DL*, Philips *SmartSpeed*) is the de-facto clinical\n  comparator; reviewers will ask, so address it in related work even though the\n  implementations are proprietary.\n\n## Hand-offs\n\n- **Classical / training-free recon** — ESPIRiT, SENSE, GRAPPA, L1-wavelet PICS,\n  NUFFT gridding, or \"just get me an image from this k-space\": use the\n  `mri-reconstruction` skill, which executes BART/SigPy pipelines. You also want\n  it for the *baseline* your network is compared against.\n- **Sampling-pattern or trajectory design** (including learned sampling that must\n  run on a scanner): `pulse-sequence-design`.\n- **Theory, citations, and the wider landscape:** the `mri-research` hub.\n\nDeeper reference:\nhttps://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/recon-methods.md\n","tagline":">-","category":"automation","commerce":{"type":"unknown","billing":"unknown","amount":null,"currency":null,"sourceUrl":null,"checkedAt":null,"runtime":"unknown","purchaseUrl":null,"checkout":"external","purchaseRequiresUserConsent":true},"tags":["agent-skill"],"author":"Ke Wang","verified":false,"attribution":{"status":"registry_indexed","statusLabel":"Registry indexed","shortLabel":"REGISTRY INDEXED","sourceLabel":"recursive skill source sync","sourceDetail":"KeWang0622/mri-research-skill","creatorName":"Ke Wang","creatorUrl":"https://github.com/KeWang0622","sourceUrl":"https://github.com/KeWang0622/mri-research-skill/tree/main/skills/deep-learning-recon","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/kewang0622-deep-learning-recon#claim-this-skill","claimCta":"Claim this skill","trustNote":"This listing was indexed from public sources and is not marked official until a maintainer claim is approved.","publicNote":"Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals."},"stats":{"stars":26,"forks":0,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":28.02},"quality":{"score":56,"tier":"promising","label":"Promising","summary":"Useful candidate, but compare it with alternatives before adopting.","signals":[{"label":"GitHub stars","value":"26","tone":"neutral"},{"label":"Freshness","value":"7d ago","tone":"positive"},{"label":"Install ready","value":"Yes","tone":"positive"},{"label":"License","value":"MIT","tone":"neutral"}],"warnings":["Low GitHub adoption signal"]},"trust":{"version":"trust-score-v5","score":66,"base_score":74,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. 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issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"7d since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":70,"weight":0.14,"status":"info","detail":"Public metadata needs stronger README/SKILL.md context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":90,"weight":0.12,"status":"pass","detail":"no major dependency risk hints in public metadata"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add KeWang0622/mri-research-skill --skill deep-learning-recon"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":72,"weight":0.07,"status":"info","detail":"filesystem or document access, network or browser access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/KeWang0622/mri-research-skill/tree/main/skills/deep-learning-recon"},{"id":"review_status","label":"Review status","score":46,"weight":0.05,"status":"warn","detail":"AI review approval is missing"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"warn","label":"GitHub adoption","detail":"26 GitHub stars"},{"status":"warn","label":"Stars/forks activity","detail":"26 stars, 0 forks; 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