{"slug":"kewang0622-mri-research","name":"mri-research","description":">-","long_description":"---\nname: mri-research\ndescription: >-\n  The generalist navigator and curated reference hub for MRI research — use it\n  for orientation, cross-domain questions, and the canonical paper / course /\n  dataset / toolbox across the whole MRI pipeline: MR physics and k-space,\n  acquisition, reconstruction, image analysis, quantitative MRI and\n  spectroscopy, hardware, data formats, and publishing. This hub also OWNS\n  image-level analysis, which no sibling skill covers: fMRI and GLM analysis,\n  BIDS, DICOM/NIfTI conversion, FreeSurfer, segmentation, registration,\n  fMRIPrep, relaxometry and QSM mapping. Reach for it when a question spans\n  several MRI sub-areas or it isn't clear which specialist applies; it defers to\n  the focused sibling skills when one is squarely in-lane. Triggers: MRI /\n  magnetic-resonance research questions, \"where do I find…\", \"which MRI tool /\n  paper / dataset for…\", k-space orientation, BIDS, NIfTI, DICOM, fMRI,\n  FreeSurfer, registration, segmentation, QSM. It points to external repos,\n  papers, and datasets rather than bundling them.\nmetadata:\n  author: Ke Wang\n  version: \"0.7.0\"\n---\n\n# MRI Research Hub\n\n## What this is (and is not)\n\nA fluent, well-oriented guide to the whole MRI research landscape — from spins\nto statistics. It exists to make the MRI community's collective knowledge\naccessible to any researcher through their AI agent. Its job is **navigation and\njudgment**, not storage:\n\n- **It IS** a curated, verified map of the MRI ecosystem — the physics and\n  courses, the acquisition and pulse-sequence tools, the reconstruction methods\n  and toolboxes, the data formats and datasets, the analysis/processing\n  pipelines, quantitative MRI and spectroscopy, the hardware community, and how\n  to find literature — plus practical \"which tool for which task\" guidance.\n- **It is NOT** a copy of any dataset, textbook, or codebase. MRI datasets run\n  from hundreds of GB to multiple TB and are governed by data-use agreements;\n  textbooks are copyrighted. So this **points** to where things live and\n  teaches how to use them.\n\nAct like a knowledgeable lab-mate: someone who can say \"for that, read Uecker's\nESPIRiT paper and use `bart ecalib`,\" \"that raw file is Siemens twix — convert\nwith `siemens_to_ismrmrd`,\" or \"preprocess that with fMRIPrep, then analyze in\nnilearn.\"\n\n## The expert team (sibling skills)\n\nThis hub is the generalist. The repo also ships focused expert agents — install\nany with `npx skills add KeWang0622/mri-research-skill --skill <name>`:\n\n- **mri-research-workflow** — end-to-end research assistant: idea → experiments →\n  paper (CVPR/MICCAI/MRM); orchestrates the experts below and helps write it.\n- **mri-reconstruction** — actionable BART/SigPy reconstruction (\"reconstruct\n  this k-space\" — it runs the pipeline).\n- **diffusion-mri** — DTI/DKI/NODDI, preprocessing (topup/eddy), tractography.\n- **pulse-sequence-design** — Pulseq/PyPulseq + Siemens/GE/Philips sequence dev.\n- **deep-learning-recon** — unrolled / self-supervised / diffusion recon, fastMRI.\n- **mri-hardware** — low-field, open-source consoles, coils, MR safety.\n\nUse this hub for orientation and cross-domain questions; hand off to an expert\nwhen the task is squarely in its lane.\n\n## Ground rules\n\n1. **Links can rot.** Every link here was verified when written, but repos move\n   and course pages change. When a link is load-bearing for the user's next\n   action, confirm it resolves (a quick fetch or `gh repo view`) before\n   presenting it as a step.\n2. **Respect dataset licenses.** Many datasets (fastMRI, HCP, UK Biobank, ADNI,\n   OASIS, BraTS) require registration or a data-use agreement. Never help\n   circumvent an access gate; point to the official application. OpenNeuro and\n   IXI are examples of fully-open sources.\n3. **Do not reproduce copyrighted text.** Summarize and cite; don't paste\n   textbook chapters or paywalled paper bodies.\n4. **Image reading is orientation, not diagnosis.** The reading primer helps\n   you follow research talk about contrast; it is not clinical or diagnostic\n   advice. Refer real-scan interpretation to a radiologist.\n5. **Prefer primary sources.** Cite the paper; use awesome-lists as living\n   indexes to discover what's new.\n\n## Core mental model (the MRI pipeline)\n\nKeep this spine in mind so you can place any MRI question:\n\n1. **Physics & contrast** — spins, RF excitation, T1/T2/T2\\* relaxation, proton\n   density; a *sequence* weights these to create contrast.\n2. **Spatial encoding & k-space** — gradients encode position; the scanner\n   samples **k-space** (the Fourier transform of the image) along a\n   *trajectory* (Cartesian/radial/spiral/EPI). Center = contrast/SNR, edges =\n   detail.\n3. **Acquisition** — the *pulse sequence* (RF + gradient events) sets the\n   contrast and trajectory; runs on *hardware* (magnet, gradients, RF coils,\n   console).\n4. **Raw data** — stored in a vendor raw format (Siemens twix, GE P-file,\n   Philips raw) or the vendor-neutral **ISMRMRD**.\n5. **Reconstruction** — turn k-space into images. Undersampling speeds scans but\n   aliases; recon undoes it with parallel imaging, compressed sensing, low-rank,\n   or learned/diffusion priors. Formally: measured `y = A x + noise`, with\n   `A = (sampling) ∘ (Fourier/NUFFT) ∘ (coil sensitivities)`; solve\n   `argmin_x ||A x − y||² + λ R(x)` — each method is a choice of `A`, `R`, and\n   optimizer.\n6. **Images → analysis** — converted to DICOM/NIfTI, organized (BIDS), then\n   registered, segmented, and analyzed (structural, functional, diffusion).\n7. **Quantification** — parameter maps (relaxometry, QSM, perfusion, MT), MR\n   fingerprinting, and spectroscopy (metabolite concentrations).\n8. **Interpretation & applications** — contrast reading, neuro/cardiac/body/MSK\n   applications (research orientation, not diagnosis).\n\n## How to route a question\n\nOpen the reference file matching the need (each is self-contained; open only\nwhat you need):\n\n| If the user is asking about… | Open |\n|---|---|\n| MR physics, k-space intuition, contrast, where to *learn* (courses, handbooks, free books) | [`references/foundations.md`](references/foundations.md) |\n| Designing/programming pulse sequences and k-space trajectories, RF pulse design, simulation | [`references/sequences-and-trajectories.md`](references/sequences-and-trajectories.md) |\n| MRI hardware: low-field, open-source consoles, coils, gradients, safety | [`references/hardware.md`](references/hardware.md) |\n| Which reconstruction method/paper applies + the landmark reading list (parallel imaging → CS → low-rank → DL → diffusion → fingerprinting) | [`references/recon-methods.md`](references/recon-methods.md) |\n| Which reconstruction *software* to use and how (BART, SigPy, MIRT.jl, MRIReco.jl, torchkbnufft, DIRECT, Gadgetron) | [`references/tools.md`](references/tools.md) |\n| Raw & image data formats (ISMRMRD, twix/P-file/Philips, DICOM, NIfTI, BIDS) and where to get data | [`references/data-and-formats.md`](references/data-and-formats.md) |\n| Image analysis & processing: structural, fMRI, diffusion MRI, segmentation, registration, pipelines | [`references/analysis-processing.md`](references/analysis-processing.md) |\n| Quantitative MRI (relaxometry, QSM, perfusion/ASL, MT) and MR spectroscopy | [`references/quantitative-and-spectroscopy.md`](references/quantitative-and-spectroscopy.md) |\n| Programmatic access to papers/data — APIs, keys, and MCP servers | [`references/literature-access.md`](references/literature-access.md) |\n| Writing up & submitting — MR journals, LaTeX templates, reporting standards, abstracts, preprints | [`references/publishing.md`](references/publishing.md) |\n| How MR image contrast reads (T1/T2/FLAIR/DWI) — background orientation only | [`references/radiology-primer.md`](references/radiology-primer.md) |\n| **Actually running a reconstruction** on real k-space (BART/SigPy, `.cfl`, twix, ISMRMRD) | hand off to the **mri-reconstruction** skill — this hub explains, that skill executes |\n\n**What this hub owns outright:** image-level analysis has no sibling expert, so\nfMRI/GLM, BIDS organization, DICOM↔NIfTI conversion, FreeSurfer, segmentation,\nregistration, fMRIPrep, and relaxometry/QSM mapping are *this* skill's\nresponsibility — answer them here via\n[`references/analysis-processing.md`](references/analysis-processing.md) and\n[`references/quantitative-and-spectroscopy.md`](references/quantitative-and-spectroscopy.md)\nrather than looking for a specialist that doesn't exist. (Diffusion MRI is the\nexception: `diffusion-mri` owns it.)\n\nCross-cutting requests pull from several files — e.g., \"reproduce this spiral CS\npaper on real scanner data\" → `recon-methods` (method) + `tools` (BART/SigPy) +\n`data-and-formats` (read the raw file) + `sequences-and-trajectories` (spiral).\n\n## Living indexes (when this is stale)\n\nMRI research moves fast. When you need something newer or a topic not covered\nhere, these community-maintained lists are the best next hop:\n\n- Awesome MRI Reconstruction — https://github.com/Joyies/Awesome-MRI-Reconstruction\n- Awesome DL-based CS-MRI — https://github.com/mosaf/Awesome-DL-based-CS-MRI\n- Awesome MRI (broad) — https://github.com/dangom/awesome-mri\n- ISMRM (the field's professional society & annual meeting) — https://www.ismrm.org\n\nFor finding papers programmatically, use the APIs/MCP servers in\n`references/literature-access.md`.\n","tagline":">-","category":"research","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":"github candidate review","sourceDetail":"KeWang0622/mri-research-skill","creatorName":"Ke Wang","creatorUrl":"https://github.com/KeWang0622","sourceUrl":"https://github.com/KeWang0622/mri-research-skill/tree/main/skills/mri-research","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/kewang0622-mri-research#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":21,"forks":0,"verified_installs":0,"successful_runs":0,"total_outcomes":0,"rating":0,"review_count":0,"quality_score":27.4},"quality":{"score":55,"tier":"promising","label":"Promising","summary":"Useful candidate, but compare it with alternatives before adopting.","signals":[{"label":"GitHub stars","value":"21","tone":"neutral"},{"label":"Freshness","value":"13d 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":63,"base_score":71,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. 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