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Is Diffusion Mri free to use?

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Who published Diffusion Mri?

kewang0622 (@kewang0622) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Diffusion Mri by kewang0622 — Agent Skill

Name: Diffusion Mri
Rating: 4.666666666666667 (3 reviews)
Author: kewang0622

Diffusion Mri

Diffusion 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.

kewang0622 Updated Sep 22, 2026

File contents

Diffusion MRI

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) then eddy: 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/probtrackx probabilistic tracking.
TractSeg (https://github.com/MIC-DKFZ/TractSeg) — CNN white-matter bundle segmentation (skips manual ROIs).

Vendor / acquisition notes

Always keep the .bval/.bvec with the data; check b-vector orientation vs. image axes (a flipped bvec silently ruins tractography).
For topup you need reversed phase-encode (blip-up/blip-down) acquisitions or a fieldmap.
Multi-shell (multiple b-values) enables DKI/NODDI/multi-tissue CSD.

Deeper reference (analysis tooling, formats): https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/analysis-processing.md

---name: diffusion-mridescription: Diffusion 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.---# Diffusion MRIYou are a diffusion-MRI scientist. Diffusion data is EPI-based and artifact-prone,so preprocessing quality dominates results — respect the pipeline order.## Typical pipeline1. **Convert & organize** — DICOM→NIfTI with `dcm2niix` (keeps `.bval`/`.bvec`); organize as BIDS. Sanity-check the gradient table.2. **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).3. **Gibbs ringing removal** — MRtrix3 `mrdegibbs`.4. **Distortion + eddy + motion** — FSL **`topup`** (reversed phase-encode pairs) then **`eddy`**: https://fsl.fmrib.ox.ac.uk/fsl/docs/#/diffusion/eddy .5. **Mask / bias field** — brain mask; N4 bias correction (ANTs).6. **Model fitting** (below).7. **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`/`probtrackx` probabilistic tracking.- **TractSeg** (https://github.com/MIC-DKFZ/TractSeg) — CNN white-matter bundle segmentation (skips manual ROIs).## Vendor / acquisition notes- Always keep the **`.bval`/`.bvec`** with the data; check b-vector orientation vs. image axes (a flipped bvec silently ruins tractography).- For `topup` you need **reversed phase-encode** (blip-up/blip-down) acquisitions or a fieldmap.- Multi-shell (multiple b-values) enables DKI/NODDI/multi-tissue CSD.Deeper reference (analysis tooling, formats):https://github.com/KeWang0622/mri-research-skill/blob/main/skills/mri-research/references/analysis-processing.md

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