Projects/Diffusion/Contrasting Tractography Measures

UNC

Overview of UNC Tractography Methods

The UNC tractography measure methodology produces fiber bundles using a standard streamline tractography algorithm FiberTracking. The fiber bundles are attributed with tensor data at each point along the bundle. The user uses clustering and manual editing tools in FiberViewer to identify the fiber bundle and remove outliers. The user identifies an origin on the fiber bundle and computes statistics of the bundle as a function of arc-length along the bundle.

• Inputs
  • DWI or DTI
  • ROI for seed regions for tractography
• Outputs
  • Fiber bundle tracts viewable in FiberViewer or Slicer3
  • Summary statistics of fiber bundle as function of arc length (text file)

Iowa

The University of Iowa tractography program GTRACT is based on a modified streamlines algorithm. The advantage of this algorithm is that it helps to resolve fibers in regions where there are crossing or fanning fibers. We have also instrumented a fast marching based algorithm into GTRACT that we are currently evaluating. These algorithms require a seed region as input and will generate a VTK file representing the fiber tracts. Data associated with anisotropy, curvature or cost of the tract can be included as point data associated with the fiber tract. Tools are also available for computing the distance between fiber tracts.

• Inputs
  • DWI or DTI data (DICOM or other ITK supported format)
  • Parameters defining the diffusion directions
  • Binary image representing the regions of interest for seeds

• Outputs
  • Tensor image
  • ADC and anisotropy images as defined by the user
  • Fiber tracts in VTK format

GATech

Information to be entered here

BWH

Stochastic Tractography

Stochastic Tractography is a Bayesian approach to estimating nerve fiber tracts from DWMRI (Diffusion Weighted Magnetic Imaging) images. The Bayesian framework provides a measure of confidence regarding the estimated tracts. This measure of confidence allows the algorithm to generate tracts which pass through regions with uncertain fiber directions, revealing more details about structural connectivity than non-Bayesian tractography algorithm.

• Inputs
  • DWI and associated parameters (b-values, gradient directions)
  • Posterior White Matter probability map

• Outputs
  • Brain Connectivity Map
  • Tract-Averaged FA Distribution
  • Tract Length Distribution

MGH

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