Thesis dMRI Pipeline#
A modular Python framework for preprocessing and tractography analysis of diffusion MRI (dMRI) data, integrating Nipype workflows with FSL (ProbTrackX2) and ANTs.
Neuroimaging Workflow Framework
Config-driven dMRI processing for reproducible research delivery
Build tractography, segmentation, and QC pipelines on top of Nipype, FSL, and ANTs with typed configuration, structured CLI output, and modular workflow composition.
Workflow-first
Self-registering workflows for HCP tractography, SynthSeg, combined meta-workflows, and QC.
Config-driven
Hierarchical YAML configuration with Pydantic validation and patient-specific overrides.
Research-friendly
Structured CLI output, progress reporting, reproducible execution, and explicit workflow graphs.
Start here
Install and run your first workflow
Set up the environment, inspect available workflows, and launch a first patient run.
Configure
Understand the config hierarchy
Combine defaults, hardware settings, protocol config, patient overrides, and CLI flags.
Operate
Choose the right workflow path
Use HCP, SynthSeg, meta-workflows, or QC depending on the processing stage you need.
Extend
Add your own workflow
Learn the architecture and the pipeline pattern, then register a new workflow of your own.
User Guide
API Reference