dcm2nii
Use this skill whenever the user wants to convert DICOM files or folders to NIfTI format (.nii or .nii.gz), extract neuroimaging volumes from clinical DICOM series (MRI, CT, PET, etc.), prepare raw DICOM data for research processing pipelines, anonymize while converting, or batch-convert multiple series/studies. Triggers include: 'DICOM to NIfTI', 'dcm to nii', 'convert dicom to nii.gz', 'dcm2niix', 'extract nii from dicom', 'batch dicom to nifti', 'prepare dicom for freesurfer/fsl/spm', 'anonymized nifti conversion', or any request to transform clinical DICOM data into analysis-ready NIfTI fo
npx skills add BioTender-max/awesome-bio-agent-skills --skill dcm2nii --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# DICOM to NIfTI conversion ## Overview DICOM is the universal clinical imaging format containing rich metadata, patient information, acquisition parameters, and often multi-slice series. NIfTI (.nii/.nii.gz) is the de-facto standard in neuroimaging research — compact, orientation-aware, and directly supported by FSL, FreeSurfer, SPM, AFNI, ANTs, etc. This skill wraps `dcm2niix` (latest stable release as of 2026), the most widely used and actively maintained DICOM→NIfTI converter in neuroimaging. It produces high-fidelity 3D/4D NIfTI volumes + comprehensive JSON sidecar files containing DICOM tags (BIDS-compatible when using `-b y`). ## Benchmark-Facing Default Mainline For benchmark-style DICOM conversion tasks, default to the narrow canonical answer instead of a broad converter survey: - Preferred default command shape: `dcm2niix -z y -b y -o <output_dir> <dicom_dir>` - For batch conversion, the default answer should be a simple loop over subject/session or series directories. - Metadata preservation means emitting paired `.nii.gz` and `.json` outputs; present this as the primary validation target. - Prefer `dcm2niix` over legacy `dcm2nii` unless the user explicitly asks for the
- Overview
- Benchmark-Facing Default Mainline
- Quick Reference
- Installation
- Via pre-built binary (recommended for NeuroClaw)
- Via conda (clean & reproducible)
- Via pip (python wrapper – if needed for scripting)
- Docker (isolated environment)
- Usage Examples
- Basic conversion (most common)
- fMRI / 4D conversion with BIDS-style naming
- Aggressive anonymization + compression
- Convert entire study folder (auto-detect series)
- NeuroClaw recommended wrapper (for agent consistency)
Linux / macOS (use latest release) wget https://github.com/rordenlab/dcm2niix/releases/latest/download/dcm2niix_lnx.zip unzip dcm2niix_lnx.zip chmod +x dcm2niix mv dcm2niix /usr/local/bin/ # or add to PATH dcm2niix --version conda install -c conda-forge dcm2niix pip install pydicom # optional helper then call subprocess.run(["dcm2niix", ...]) docker pull rordenlab/dcm2niix:latest
What does the dcm2nii skill do?
Use this skill whenever the user wants to convert DICOM files or folders to NIfTI format (.nii or .nii.gz), extract neuroimaging volumes from clinical DICOM series (MRI, CT, PET, etc.), prepare raw DICOM data for research processing pipelines, anonymize while converting, or batch-convert multiple series/studies. Triggers include: 'DICOM to NIfTI', 'dcm to nii', 'convert dicom to nii.gz', 'dcm2niix', 'extract nii from dicom', 'batch dicom to nifti', 'prepare dicom for freesurfer/fsl/spm', 'anonymized nifti conversion', or any request to transform clinical DICOM data into analysis-ready NIfTI fo
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill dcm2nii --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.