Convert clinical DICOM data to NIfTI volumes with JSON sidecars.
npx skills add https://github.com/CUHK-AIM-Group/NeuroClaw --skill dcm2nii
Please help me install this Agent Skill. Skill: dcm2nii Source: https://github.com/CUHK-AIM-Group/NeuroClaw/tree/main/skills/dcm2nii Command: npx skills add https://github.com/CUHK-AIM-Group/NeuroClaw --skill dcm2nii
Converting clinical DICOM data into analysis-ready NIfTI volumes with preserved metadata is labor-intensive and error-prone; this skill wraps dcm2niix to automate the process and generate JSON sidecars.
Run the dcm2niix-based conversion on the input DICOM directory to generate NIfTI volumes and JSON sidecars in the specified output directory.
High-intent search queries and answers about installing and using this skill.
To convert clinical DICOM to NIfTI with preserved metadata, this Skill wraps dcm2niix to automate the process and generate JSON sidecars. It preserves orientation, voxel spacing, and slice timing while outputting .nii/.nii.gz and .json files.
Yes, you can batch process DICOM to NIfTI conversion across multiple subjects and series. This Skill supports batch processing and includes anonymization features to handle large neuroimaging datasets efficiently.
Yes, the DICOM to NIfTI conversion outputs are suitable for FSL, FreeSurfer, SPM, ANTs, nnU-Net, and BIDS-ready pipelines. The generated NIfTI volumes and JSON sidecars integrate directly into these downstream neuroimaging workflows.
The DICOM to NIfTI conversion supports MRI, CT, and PET datasets. It applies high-fidelity conversion logic to these modalities, ensuring analysis-ready NIfTI volumes with accurately preserved metadata.
You need a wrapper around dcm2niix to convert DICOM files using this Skill. The Skill automates the dcm2niix conversion process, requiring only an input DICOM directory and a specified output directory for the NIfTI volumes and JSON sidecars.
CUHK-AIM-GroupConvert NIfTI volumes to single-frame DICOM series with MR/SVR tagging.
awslabsStandardize structural MRI and CT volumes through reorientation, bias correction, skull stripping, registration, and intensity normalization.
tomlupoDefine dataset directory structures and documentation for reproducibility.
jackspaceRead, modify, and anonymize DICOM files via pydicom Dataset objects and metadata.
