Visualization of Anatomic Covariance Tensor Fields Gordon L by 4HA1WqpQ

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									Exploring Connectivity of the Brain’s
White Matter with Dynamic Queries
  Anthony Sherbondy, David Akers, Rachel Mackenzie,
         Robert Dougherty, and Brian Wandell

 IEEE Transactions on Visualization and Computer Graphics,
                V11, No 4, July/August 2005


                  Presented by:
            Eugene (Austin) Stoudenmire
                   14 Feb 2007
               Problem
• New technology emerged
  –Diffusion Tensor Imaging (DTI)
  –White matter connections, i.e. fiber
   tracts, can now be measured
• Need to take advantage of it
• Requires better visualization
             We Care
• Better visualization would
  –Assist research
  –Interactive
              Approach
• Combine types of data
  –Anatomical – White – DTI
  –Functional – Gray – fMRI
    • Functional Magnetic Resonance Imaging
• Precompute
• Query Interface
  –Pictoral
  –Labeled
  –Ranges
                   DTI
•   Diffusion Tensor Imaging
•   New Technology
•   Measures white matter pathways
•   Estimates water molecule diffusion
    –Water diffuses lengthwise along axons
    –Diffusion direction  nerve fiber
     orientation
       One Method of DTI
         Visualization
• MR Tractography
• Traces principle direction of diffusion
• Connects points into fiber tracts
• Fiber tracts = pathways
• Anatomical connections between
  endpoints of the pathways are implied
• Therefore, implied white matter structure
          These Pathways
•   Not individual nerves
•   Not Bundles
•   But something
•   Abstract, white matter route
    “possibilities”
               fMRI
• Functional Magnetic Res Imaging
• Correlate activity
• Suggests gray matter connections
        The Combination
• Take the MR Tractography data
• Precompute paths, statistical properties
• Interactive manipulation
  – Regions of interest – Box / Ellipsoid
  – Path properties – Length / Curvature
• Combine with fMRI
  – Search for anatomical paths that might
    connect functionally-defined regions
• Saves time over existing approaches
Query Interface
Query Interface – Partial Blowup
Query Interface – Partial Blowup
Query Interface – Partial Blowup
Query Interface – Partial Blowup
Acqusition
 DTI & fMRI
            Subject
• Neurologically Normal
• Male
• Human
• 35
              DTI
• Eight 3-minute whole brain scans
 –Averaged
 –38 axial slices
 –2 x 2 x 3 mm voxels
• 8-minute high res anat images
 –1 x 1 x 1 mm voxel
• Coregistered
• DTI resampled to 2 mm
             fMRI
• 21-30 obliquely oriented slices
• 2 x 2 x 3 mm voxel
• Registered with anatomy
• Mapped to cortical surface mesh
Precomputation
 Fractional Anisotropy (FA)
• Diffusion orientation ratio
    0 = spherical = gray matter
    0.5 = linear or planar ellipsoid
    1 = very linear
• Uses
  –Algorithm termination criteria
  –Queries
  –Navigational aid
             Approaches
• Typical
 –Interactively trace pathways
• Authors’
 –Precompute pathways
 –Over entire white matter
 –Then let software “prune”
       Cortical Surface
• Classified white matter
• Semi-manually – neuroscientist
• Marching-Cubes -> t-mesh
• Smoothed
• Kept both
• 230,000 vertices
       Precomputation
• Statistical properties
• Length
• Avg FA
• Avg Curvature
• Tractography Algorithm
Implementation
        Path Rendering
• Lines vs streamtubes (for speed)
• Pathways – luminance offset
• Groups of pathways – hue
  –User defined hue
  –Virtual staining
• Queries modified – stains remain
      Hardware/Software
• Visualization C++
• ToolKit (VTK)
• RAPID
  –Fast VOI / Path Intersection Comp
  –80K-120K paths/sec (w/SGI RE)
  –Allowed 3-8
• 510MB for 26K paths @ 20KB/path
• 160MB for cortical meshes
Sequential Dynamic
     Queries
All 13,000 Pathways
Length > 4 cm
Through VOI 1
Through VOI 1 AND (2 or 3)
Volumes of Interest
  Surface-constrained
VOI on Cortical Surface
Same VOI, Smoothed Surface
Validation of Known
     Pathways
Occipital Lobe
Occipital to Right Frontal Lobe
Occipital to Left Frontal Lobe
Occipital to R & L, w/Context
Forming Hypotheses
Known and Unknown Paths
Algorithm Comparison
 STT – Streamlines Tracking Techniques
                  Vs
       TEND – Tensor Deflection
STT (blue) vs TEND (yellow)
 Exploration of
 Connections
Between Functional Areas
fMRI Areas Colormapped
VOI Placement
Surface Removed  Paths Visible
VOI Adjusted  Different Paths
            Evaluation
• Types of functions
  –Validation of known pathways
  –Hypothesis generation
• Time to explore – 10 minutes for
  significant exploration
• Speed – Interactive rates
• Interface – Interactive queries
Alternative Methods
     Alternative Methods
• Diffusion tensor visualization
  White Matter Algorithms
• Streamlines Tracking Techniques
• Fiber Assg thru Cont Tracking
• Tensor-deflection
              Filters
• Length
• Average linear anisotropy
• Regions of interest
           Conclusion
• Multiple data types (DTI & fMRI)
• New visualization interface
• Interactive queries
• Hypothesis generation & testing
          Next Steps
• Real work
• Multiple subjects
• Normal to abnormal
• Acquisition technology
• Path tracing algorithms
           Question
• Is there any reason for tools
  such as this to be validated?
           Question

• If validated this early on,
  wouldn’t every change pretty
  much negate the validation?
                 Question
• Should there be some kind of benchmark to use
  to measure these applications against?

								
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