dwi-skill

To plan diffusion MRI preprocessing workflows for multi-shell datasets, you can define the input types, processing steps, and expected outputs, then delegate execution to specialized tools like qsiprep-tool and fsl-tool.

Extracting FA and MD maps from raw DWI NIfTI files involves planning a diffusion metric extraction workflow that delegates tensor calculations to specialized tools, saving the resulting maps under a structured output layout.

Yes, you can use BIDS datasets to build a tractography-based connectome by planning the connectome construction workflow and delegating the tractography steps to MRtrix3 wrappers and nilearn components.

Yes, this approach supports DICOM inputs for ROI diffusion statistics by applying the workflow across DICOM, NIfTI, and BIDS datasets and delegating ROI feature generation to the appropriate tool skills.

The best way to organize reproducible DWI pipeline outputs is to enforce explicit tool delegation and save all results under the structured dwi_output layout via claw-shell, ensuring reproducibility.

Yes, you need separate tools for preprocessing and connectome construction because this approach orchestrates the workflow by delegating concrete algorithm execution to tools like qsiprep-tool, fsl-tool, and MRtrix3 wrappers.