View source for Algorithm:UNC:DTI Tract Statistics Workflow

# Load diffusion weighted imaging with seven scans in Basser gradient scheme or using .nrrd file with gradient metadata
# Estimate tensors and compute derived tensors measures
# Save FA image to identify ROIs
# Load FA image in InsightSNAP
# Draw source and target ROI for tracking
# Load ROIs in FiberTracking and compute fiber tracts
# Save resulting fiber tracts
# Load resulting fiber tracts in FiberViewer
# Add image for background overlay (optional)
# Cluster fiber tracts to clean results
## Length filtering
## Center of gravity based Hierarchical Agglomerative Clustering (HAC): Useful for removing outliers
## Mean distance based Hierarchical Agglomerative Clustering (HAC): Useful for removing outliers
## Hausdorff distance based Hierarchical Agglomerative Clustering (HAC): Useful for seperating sections of bundles with small deviations at one end
## Normalized cut clustering based on mean distance: Research clustering method
# Manual tract editing
## Cutting fibers with plane
## Resampling fibers
# Tract based statistics
## Averaged derived properties as function of arc-length
## Average tensor as function of arc-length