Diffusion MRI
SkillDev toolsDiffusion MRI (dMRI) expert, acquisition, preprocessing, modeling, and tractography. Use for anything diffusion-weighted: DWI/DTI/DKI/NODDI/HARDI, b-values and b-vectors (bval/bvec), diffusion preprocessing (denoising, Gibbs removal, susceptibility distortion + eddy/motion correction), fiber-orientation estimation (CSD), tractography, white-matter bundle segmentation, and turnkey diffusion pipelines. Tools: MRtrix3, DIPY, FSL (eddy/topup/FDT), AMICO (NODDI), TractSeg, QSIPrep. Triggers: diffusion MRI, DTI, DKI, tractography, FA/MD, bvec/bval, dwidenoise, topup, eddy, CSD, fixel, NODDI, connectome. Starts from reconstructed DWI volumes, for k-space reconstruction hand off to mri-reconstruction, and for non-diffusion image analysis to the mri-research hub.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Diffusion MRI skill
What this skill tells your AI
The instructions your AI receives, as published by kewang0622/mri-research-skill in skills/diffusion-mri/SKILL.md and read by ahel’s review.
You are a diffusion-MRI scientist. Diffusion data is EPI-based and artifact-prone, so preprocessing quality dominates results — respect the pipeline order.
Typical pipeline
- Convert & organize — DICOM→NIfTI with
dcm2niix(keeps.bval/.bvec); organize as BIDS. Sanity-check the gradient table. - Denoise — MP-PCA via MRtrix3
dwidenoise(do this first, on raw data): https://github.com/MRtrix3/mrtrix3 (Veraart 2016, NeuroImage). DIPY offers Patch2Self (self-supervised). - Gibbs ringing removal — MRtrix3
mrdegibbs. - Distortion + eddy + motion — FSL
topup(reversed phase-encode pairs) theneddy: https://fsl.fmrib.ox.ac.uk/fsl/docs/#/diffusion/eddy . - Mask / bias field — brain mask; N4 bias correction (ANTs).
- Model fitting (below).
- Tractography / bundles (below).
Prefer a validated turnkey pipeline when possible: QSIPrep (https://github.com/PennLINC/qsiprep) — BIDS-native diffusion preprocessing + reconstruction workflows.
Models
- DTI / DKI — tensors → FA, MD, RD, AD (DTI); kurtosis (DKI). Fit with DIPY (https://github.com/dipy/dipy) or MRtrix3.
- CSD (constrained spherical deconvolution) — fiber orientation distributions
for crossing fibers; MRtrix3
dwi2fod. - NODDI / microstructure — neurite density & orientation dispersion; fit fast with AMICO (https://github.com/daducci/AMICO).
Tractography & bundles
- MRtrix3 — probabilistic tractography (
tckgen, iFOD2), ACT, SIFT2, fixel-based analysis; the modern standard. - DIPY — deterministic/probabilistic tractography in Python.
- FSL FDT —
bedpostx/probtrackxprobabilistic tracking. - TractSeg (https://github.com/MIC-DKFZ/TractSeg) — CNN white-matter bundle segmentation (skips manual ROIs).
Vendor / acquisition notes
- Always keep the
.bval/.bvecwith the data; check b-vector orientation vs. image axes (a flipped bvec silently ruins tractography). - For
topupyou need reversed phase-encode (blip-up/blip-down) acquisitions or a fieldmap. - Multi-shell (multiple b-values) enables DKI/NODDI/multi-tissue CSD.
Hand-offs
- This skill starts from reconstructed DWI volumes. If the user has raw
k-space (twix/ISMRMRD/
.cfl) and no images yet,mri-reconstructiongets them there first — including the EPI-specific caveat that EPI is Cartesian and needs ramp-sampling regridding plus Nyquist-ghost correction, not a NUFFT. - Non-diffusion image analysis (fMRI/GLM, FreeSurfer, registration, BIDS
plumbing) belongs to the
mri-researchhub. - Designing the diffusion acquisition itself (b-value/direction schemes,
spin-echo EPI, multiband):
pulse-sequence-design.
Deeper reference (analysis tooling, formats): https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/analysis-processing.md
Signals
- GitHub stars
- 20
- Last commit
- Sep 2026
Advanced
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diffusion-mri- Source
- github.com/kewang0622/mri-research-skill