2008 Winter Project Week Tractography Meeting Notes
Contents
Notes from Presentations and Discussions
Data quality, NAMIC DTI pipeline
- Issues?
- Create a synthetic tensor data set (Gordon's helix, crossing fiber situation). Good for debugging for comparing methods.
- EPI distortion correction, ITK?
- White matter mask? Generous?
- Freesurfer white matter mask? Map structural segm to DTI
- EM segmenter in Slicer white matter mask?
ROI definitions and impact on different methods
- Force all methods to use the same ROIs?
- Classes of ROIs?
- For streamline methods larger ROIs than the tract itself good
- For shortest path methods, connections stop at ROI
Methods:
- Streamline, Slicer2 (Sonia)
- Streamline, whole brain Slicer2 (Sonia)
- Streamline, Fiberviewer (Casey)
- Streamline, Gtract (Vince)
- Shortest path (Fast Marching), volumetric (Vince)
- Stochastic, volumetric Slicer3 (Tri)
- Shortest path, volumetric (John)
- Shortest path, volumetric (Tom)
Review and define metrics to compare results, which methods to compare?
Current metrics: tensor invariants (FA, trace, mode, norm), volume, length.
What is a good fiber tract?
Easier question: How similar are two methods?
Parameter settings within a method may change the results comparable to the difference between methods.
- 12 different tracts
- Tracts voxelized -> volumetric result from tracts
- Missing parts of a tract, how should be penalized?
- Statistics along the tracts, should we sum along the tract to get one measure?
- Distributions across the tract
- Atlas (Casey)
- Bundle thickness: mix of radius of tract and uncertainty
- How to define volume from connectivity map (shortest path, or stochastic)
Scientific Output
- All groups should report volumes in unit ml
Notes
From Sonia: Could do tractography for all tracks with both ROI and whole brain seeding.
From Casey's presentation: Could do tractography for all tracks. Made suggestion to compute more complex statistics of fiber track, e.g. FA measure in spatial distribution. His Atlas presentation brought up idea of using one set of ROI specifications for an entire study in atlas space and map them back to the individual.
From John's presentation: His method required that he had to shrink the provided ROIs for uncinate fasciulus and corpus callosum to make his method work. Then could generate fiber volumes for each tract. His tracts all STOP at ROIs (end points). If given ROIs farther apart could generate longer tracts. Needed a white matter mask- created his own with Tri by thresholding FA (>0.15). Discussion regarding whether the ROIs based on Susumu Mori's criteria would actually be optimal or even work for every method.
From Tri's presentation: Able to generate all tracks with given ROIs. Needed white matter mask (see above). 30 minutes/tract on 63GB computer. Seed with source, filter with sink. Had to cut uncinate fasciulus. For later statistical analysis, considered only non-zero connectivity value voxels. Reminds us that bundle thickness is related to both uncertainty and physical radius Internal capsule, why so much blue? What would be his suggestions for how best to quantify outcome?
From Vince Magnotta's presentation:GTRACT fine for forceps major and minor, internal capusule and Fast Marching algorithm better for fornix, hippocampus-cingulum and cingulum. For the Fast Marching algorithm, modified the algorithm to use cost image generations based on tensor and anisotropy, gradient descent. His algorithm would perform "better" for core of cingulum bundle, needs a smaller, more focal ROI. For uncinate, couldn't get it to bend around, would need intermediate ROIs.
Tom Fletcher: Ugly R fornix. Needed to shrink ROIs for some tracts.
Sonia's preliminary cross-algorithm study: "We know when the track is bad (or ugly) but we don't know when it is good." CF Westin
- rank order of qualitative accuracy (strongly underscores need to clarify the clinical goal of the tractography results)
Missing is arcuate fasiculus
From Carlo:
- Commended us for making effort against this tough problem and for the decision to control pre-processing steps
- What is the impact of adjusting parameters within an algorithm on the outcome metrics? May need to explore this in addition to the cross algorithm assessments.
- What is the effect of the tissue properties that surround the track on results within and across algorithm.
- Differences due to the topology, how to visualize? Binary masks in/out of track volume.
Action Plan:
Plan upload for each subject the DTI registered white matter mask in nrrd format (Anastasia, Sylvain, bug-reporter as needed). Agree on units for volume measures. Suggest cubic centimeters (ml) vs mm3.
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