{"slug":"kewang0622-diffusion-mri","name":"diffusion-mri","description":">-","long_description":"---\nname: diffusion-mri\ndescription: >-\n  Diffusion MRI (dMRI) expert — acquisition, preprocessing, modeling, and\n  tractography. Use for anything diffusion-weighted: DWI/DTI/DKI/NODDI/HARDI,\n  b-values and b-vectors (bval/bvec), diffusion preprocessing (denoising, Gibbs\n  removal, susceptibility distortion + eddy/motion correction), fiber-orientation\n  estimation (CSD), tractography, white-matter bundle segmentation, and turnkey\n  diffusion pipelines. Tools: MRtrix3, DIPY, FSL (eddy/topup/FDT), AMICO (NODDI),\n  TractSeg, QSIPrep. Triggers: diffusion MRI, DTI, DKI, tractography, FA/MD,\n  bvec/bval, dwidenoise, topup, eddy, CSD, fixel, NODDI, connectome. Starts from\n  reconstructed DWI volumes — for k-space reconstruction hand off to\n  mri-reconstruction, and for non-diffusion image analysis to the mri-research hub.\nmetadata:\n  author: Ke Wang\n  version: \"0.7.0\"\n---\n\n# Diffusion MRI\n\nYou are a diffusion-MRI scientist. Diffusion data is often EPI-based and artifact-prone,\nso preprocessing quality dominates results — respect the pipeline order.\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## 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## DWI, DTI and acquisition choices\n\nRead [DWI and DTI](references/dwi-dti.md) for the measurement/model distinction,\ngradient and BIDS metadata checks, tensor fitting, QC and interpretation limits.\nDWI is acquired data; DTI is one model of it. EPI is a readout, and DENSE is\ntissue-displacement imaging, not a diffusion-tensor technique.\n\n## Typical pipeline\n\n1. **Convert & organize** — DICOM→NIfTI with `dcm2niix` (keeps `.bval`/`.bvec`);\n   organize as BIDS. Sanity-check the gradient table.\n2. **Denoise** — MP-PCA via MRtrix3 `dwidenoise` (do this first, on raw data):\n   https://github.com/MRtrix3/mrtrix3 (Veraart 2016, *NeuroImage*). DIPY offers\n   Patch2Self (self-supervised).\n3. **Gibbs ringing removal** — MRtrix3 `mrdegibbs`.\n4. **Distortion + eddy + motion** — FSL **`topup`** (reversed phase-encode pairs)\n   then **`eddy`** (retain its rotated b-vectors): https://fsl.fmrib.ox.ac.uk/fsl/docs/#/diffusion/eddy .\n5. **Mask / bias field** — brain mask; N4 bias correction (ANTs).\n6. **Model fitting** (below).\n7. **Tractography / bundles** (below).\n\nPrefer a validated turnkey pipeline when possible: **QSIPrep**\n(https://github.com/PennLINC/qsiprep) — BIDS-native diffusion preprocessing +\nQC. Downstream diffusion modeling and tractography use **QSIRecon**\n(https://qsirecon.readthedocs.io/); this is distinct from raw k-space reconstruction.\n\n## Models\n\n- **DTI / DKI** — tensors → FA, MD, RD, AD (DTI); kurtosis (DKI). Fit with **DIPY**\n  (https://github.com/dipy/dipy) or MRtrix3.\n- **CSD (constrained spherical deconvolution)** — fiber orientation distributions\n  for crossing fibers; MRtrix3 `dwi2fod`.\n- **NODDI / microstructure** — neurite density & orientation dispersion; fit fast\n  with **AMICO** (https://github.com/daducci/AMICO).\n\n## Tractography & bundles\n\n- **MRtrix3** — probabilistic tractography (`tckgen`, iFOD2), ACT, SIFT2,\n  fixel-based analysis; the modern standard.\n- **DIPY** — deterministic/probabilistic tractography in Python.\n- **FSL FDT** — `bedpostx`/`probtrackx` probabilistic tracking.\n- **TractSeg** (https://github.com/MIC-DKFZ/TractSeg) — CNN white-matter bundle\n  segmentation (skips manual ROIs).\n\n## Vendor / acquisition notes\n\n- Always keep the **`.bval`/`.bvec`** with the data; check b-vector orientation\n  vs. image axes (a flipped bvec silently ruins tractography).\n- For `topup` you need **reversed phase-encode** (blip-up/blip-down) acquisitions\n  with suitable metadata. A conventional fieldmap requires a separate\n  fieldmap-based route; it is not a replacement image passed directly to `topup`.\n- Multi-shell (multiple b-values) enables DKI/NODDI/multi-tissue CSD.\n\n## Hand-offs\n\n- This skill starts from **reconstructed DWI volumes**. If the user has raw\n  k-space (twix/ISMRMRD/`.cfl`) and no images yet, `mri-reconstruction` gets them\n  there first — including the EPI-specific caveat that EPI is Cartesian and needs\n  regridding when ramp-sampled plus Nyquist-ghost correction; a\n  NUFFT alone does not address these effects.\n- **Non-diffusion image analysis** (fMRI/GLM, FreeSurfer, registration, BIDS\n  plumbing) belongs to the `mri-research` hub.\n- **Designing the diffusion acquisition** itself (b-value/direction schemes,\n  spin-echo EPI, multiband): `pulse-sequence-design`.\n\nDeeper reference (analysis tooling, formats):\nhttps://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/analysis-processing.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/diffusion-mri","indexedBy":"OpenAgentSkill community index","claimUrl":"https://www.openagentskill.com/skills/kewang0622-diffusion-mri#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":64,"base_score":72,"outcome_confidence":0,"tier":"review","label":"Sandbox only","summary":"Useful candidate with missing or mixed trust signals. 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