{"slug":"deep-learning-recon","title":"deep-learning-recon","summary":"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-b","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-28T15:11:48.324331Z","repo":{"url":"https://github.com/KeWang0622/mri-research-skill","stars":26,"forks":0,"license":"MIT","updatedAt":"2026-09-28T08:57:24Z"},"bodyHtml":"
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name: deep-learning-recon\ndescription: >-\nDeep-learning MRI reconstruction expert. Use for training or applying neural\nnetworks to reconstruct undersampled MRI — unrolled / variational networks\n(VarNet, MoDL, End-to-End VarNet, deep cascade), self-supervised training\nwithout fully-sampled data (SSDU), diffusion / score-based reconstruction, and\nthe frameworks and datasets to do it. Tools: DIRECT, fastMRI, ATOMMIC,\ntorchkbnufft; datasets fastMRI / mridata. For classical, training-free\nreconstruction (ESPIRiT/SENSE/GRAPPA, L1-wavelet PICS, NUFFT gridding) hand off\nto the mri-reconstruction skill. Triggers: deep learning\nreconstruction, unrolled network, variational network, MoDL, end-to-end\nVarNet, data consistency, self-supervised MRI reconstruction, diffusion model\nreconstruction, score-based, fastMRI, physics-guided network.\nmetadata:\nauthor: Ke Wang\nversion: \"0.7.0\"

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Deep-Learning MRI Reconstruction

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You are a DL-recon researcher. The dominant, robust paradigm is the unrolled\nnetwork: unroll N iterations of an iterative solver, learn the\nregularizer/updates end-to-end, and keep the measured data-consistency step.\nAlways anchor to data consistency — it's what guards against hallucinated\nstructure.

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Papers and textbooks

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See the annotated reading list for primary papers,\ntextbooks, publication details, direct source links and what each source supports.\nUse the repo-wide reference index to navigate across skills.\nWhen using a method, cite its specific source; distinguish paper evidence from\nsoftware instructions and current venue/safety requirements.

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Project research memory

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For 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\nto initialize the folder or connect project CLAUDE.md / AGENTS.md. If the hub\nis absent, retrieve the reference from the official skill repository.

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Tool setup before execution

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For 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 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.

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Method families (with citations)

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Frameworks & building blocks

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Data

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fastMRI (knee/brain/prostate/breast) is the benchmark; requires a signed\ndata-use agreement (https://fastmri.med.nyu.edu). Fully-open alternative for\nprototyping: mridata.org.

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Training & evaluation

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Hand-offs

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Deeper reference:\nhttps://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/recon-methods.md

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