{"slug":"mri-research","title":"mri-research","summary":"The generalist navigator and curated reference hub for MRI research — use it for orientation, cross-domain questions, and the canonical paper / course / dataset / toolbox across the whole MRI pipeline: MR physics and k-space, acquisition, reconstruction, image analysis, quantitat","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-28T15:11:48.933916Z","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: mri-research\ndescription: >-\nThe generalist navigator and curated reference hub for MRI research — use it\nfor orientation, cross-domain questions, and the canonical paper / course /\ndataset / toolbox across the whole MRI pipeline: MR physics and k-space,\nacquisition, reconstruction, image analysis, quantitative MRI and\nspectroscopy, hardware, data formats, and publishing. This hub also OWNS\nimage-level analysis, which no sibling skill covers: fMRI and GLM analysis,\nBIDS, DICOM/NIfTI conversion, FreeSurfer, segmentation, registration,\nfMRIPrep, relaxometry and QSM mapping. Reach for it when a question spans\nseveral MRI sub-areas or it isn't clear which specialist applies; it defers to\nthe focused sibling skills when one is squarely in-lane. Triggers: MRI /\nmagnetic-resonance research questions, \"where do I find…\", \"which MRI tool /\npaper / dataset for…\", k-space orientation, BIDS, NIfTI, DICOM, fMRI,\nFreeSurfer, registration, segmentation, QSM. It points to external repos,\npapers, and datasets rather than bundling them.\nmetadata:\nauthor: Ke Wang\nversion: \"0.7.0\"

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MRI Research Hub

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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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What this is (and is not)

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A 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:

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Act 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.\"

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The expert team (sibling skills)

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This hub is the generalist. The repo also ships focused expert agents — install\nany with npx skills add KeWang0622/mri-research-skill --skill <name>:

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Use this hub for orientation and cross-domain questions; hand off to an expert\nwhen the task is squarely in its lane.

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Ground rules

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  1. Links can rot. Every link here was verified when written, but repos move\nand course pages change. When a link is load-bearing for the user's next\naction, confirm it resolves (a quick fetch or gh repo view) before\npresenting it as a step.
    2. \n
  2. Respect dataset licenses. Many datasets (fastMRI, HCP, UK Biobank, ADNI,\nOASIS, BraTS) require registration or a data-use agreement. Never help\ncircumvent an access gate; point to the official application. OpenNeuro and\nIXI are examples of fully-open sources.
    3. \n
  3. Do not reproduce copyrighted text. Summarize and cite; don't paste\ntextbook chapters or paywalled paper bodies.
    4. \n
  4. Image reading is orientation, not diagnosis. The reading primer helps\nyou follow research talk about contrast; it is not clinical or diagnostic\nadvice. Refer real-scan interpretation to a radiologist.
    5. \n
  5. Prefer primary sources. Cite the paper; use awesome-lists as living\nindexes to discover what's new.
    6. \n
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Core mental model (the MRI pipeline)

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Keep this spine in mind so you can place any MRI question:

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  1. Physics & contrast — spins, RF excitation, T1/T2/T2* relaxation, proton\ndensity; a sequence weights these to create contrast.
    2. \n
  2. Spatial encoding & k-space — gradients encode position; the scanner\nsamples k-space (the Fourier transform of the image) along a\ntrajectory (Cartesian/radial/spiral/EPI). Center = contrast/SNR, edges =\ndetail.
    3. \n
  3. Acquisition — the pulse sequence (RF + gradient events) sets the\ncontrast and trajectory; runs on hardware (magnet, gradients, RF coils,\nconsole).
    4. \n
  4. Raw data — stored in a vendor raw format (Siemens twix, GE P-file,\nPhilips raw) or the vendor-neutral ISMRMRD.
    5. \n
  5. Reconstruction — turn k-space into images. Undersampling speeds scans but\naliases; recon undoes it with parallel imaging, compressed sensing, low-rank,\nor learned/diffusion priors. Formally: measured y = A x + noise, with\nA = (sampling) ∘ (Fourier/NUFFT) ∘ (coil sensitivities); solve\nargmin_x ||A x − y||² + λ R(x) — each method is a choice of A, R, and\noptimizer.
    6. \n
  6. Images → analysis — converted to DICOM/NIfTI, organized (BIDS), then\nregistered, segmented, and analyzed (structural, functional, diffusion).
    7. \n
  7. Quantification — parameter maps (relaxometry, QSM, perfusion, MT), MR\nfingerprinting, and spectroscopy (metabolite concentrations).
    8. \n
  8. Interpretation & applications — contrast reading, neuro/cardiac/body/MSK\napplications (research orientation, not diagnosis).
    9. \n
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How to route a question

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Open the reference file matching the need (each is self-contained; open only\nwhat you need):

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If the user is asking about…Open
MR physics, k-space intuition, contrast, where to learn (courses, handbooks, free books)references/foundations.md
Designing/programming pulse sequences and k-space trajectories, RF pulse design, simulationreferences/sequences-and-trajectories.md
DWI/DTI, ADC/FA/MD, gradients, diffusion preprocessing and model QCDiffusion skill and its DWI/DTI guide
MRI hardware: low-field, open-source consoles, coils, gradients, safetyreferences/hardware.md
Which reconstruction method/paper applies + the landmark reading list (parallel imaging → CS → low-rank → DL → diffusion → fingerprinting)references/recon-methods.md
Which reconstruction software to use and how (BART, SigPy, MIRT.jl, MRIReco.jl, torchkbnufft, DIRECT, Gadgetron)references/tools.md
Raw & image data formats (ISMRMRD, twix/P-file/Philips, DICOM, NIfTI, BIDS) and where to get datareferences/data-and-formats.md
Image analysis & processing: structural, fMRI, diffusion MRI, segmentation, registration, pipelinesreferences/analysis-processing.md
Quantitative MRI (relaxometry, QSM, perfusion/ASL, MT) and MR spectroscopyreferences/quantitative-and-spectroscopy.md
Programmatic access to papers/data — APIs, keys, and MCP serversreferences/literature-access.md
Writing up & submitting — MR journals, LaTeX templates, reporting standards, abstracts, preprintsreferences/publishing.md
How MR image contrast reads (T1/T2/FLAIR/DWI) — background orientation onlyreferences/radiology-primer.md
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
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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\nreferences/analysis-processing.md and\nreferences/quantitative-and-spectroscopy.md\nrather than looking for a specialist that doesn't exist. (Diffusion MRI is the\nexception: diffusion-mri owns it.)

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Cross-cutting requests pull from several files — e.g., \"reproduce this spiral CS\npaper on real scanner data\" → recon-methods (method) + tools (BART/SigPy) +\ndata-and-formats (read the raw file) + sequences-and-trajectories (spiral).

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Living indexes (when this is stale)

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MRI research moves fast. When you need something newer or a topic not covered\nhere, these community-maintained lists are the best next hop:

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For finding papers programmatically, use the APIs/MCP servers in\nreferences/literature-access.md.

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