Exploring Connectivity of the Brain’s White Matter with Dynamic Queries
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Transcript of Exploring Connectivity of the Brain’s White Matter with Dynamic Queries
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Exploring Connectivity of the Brain’s White Matter with Dynamic Queries
Presented by:Eugene (Austin) Stoudenmire
14 Feb 2007
Anthony Sherbondy, David Akers, Rachel Mackenzie, Robert Dougherty, and Brian Wandell
IEEE Transactions on Visualization and Computer Graphics, V11, No 4, July/August 2005
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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
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We Care• Better visualization would
–Assist research–Interactive
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Approach• Combine types of data
–Anatomical – White – DTI–Functional – Gray – fMRI
• Functional Magnetic Resonance Imaging
• Precompute• Query Interface
–Pictoral–Labeled–Ranges
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DTI
• Diffusion Tensor Imaging• New Technology• Measures white matter pathways• Estimates water molecule diffusion
–Water diffuses lengthwise along axons–Diffusion direction nerve fiber
orientation
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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
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These Pathways
• Not individual nerves
• Not Bundles
• But something
• Abstract, white matter route “possibilities”
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fMRI
• Functional Magnetic Res Imaging
• Correlate activity
• Suggests gray matter connections
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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
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Query Interface
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Query Interface – Partial Blowup
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Query Interface – Partial Blowup
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Query Interface – Partial Blowup
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Query Interface – Partial Blowup
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Acqusition
DTI & fMRI
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Subject
• Neurologically Normal• Male• Human• 35
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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
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fMRI
• 21-30 obliquely oriented slices• 2 x 2 x 3 mm voxel• Registered with anatomy• Mapped to cortical surface mesh
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Precomputation
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Fractional Anisotropy (FA)• Diffusion orientation ratio
0 = spherical = gray matter0.5 = linear or planar ellipsoid1 = very linear
• Uses–Algorithm termination criteria–Queries–Navigational aid
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Approaches• Typical
–Interactively trace pathways
• Authors’–Precompute pathways–Over entire white matter–Then let software “prune”
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Cortical Surface• Classified white matter • Semi-manually – neuroscientist• Marching-Cubes -> t-mesh• Smoothed• Kept both• 230,000 vertices
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Precomputation• Statistical properties• Length• Avg FA• Avg Curvature• Tractography Algorithm
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Implementation
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Path Rendering• Lines vs streamtubes (for speed)• Pathways – luminance offset• Groups of pathways – hue
–User defined hue–Virtual staining
• Queries modified – stains remain
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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
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Sequential Dynamic Queries
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All 13,000 Pathways
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Length > 4 cm
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Through VOI 1
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Through VOI 1 AND (2 or 3)
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Volumes of Interest
Surface-constrained
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VOI on Cortical Surface
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Same VOI, Smoothed Surface
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Validation of Known Pathways
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Occipital Lobe
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Occipital to Right Frontal Lobe
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Occipital to Left Frontal Lobe
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Occipital to R & L, w/Context
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Forming Hypotheses
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Known and Unknown Paths
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Algorithm Comparison
STT – Streamlines Tracking TechniquesVs
TEND – Tensor Deflection
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STT (blue) vs TEND (yellow)
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Exploration of Connections
Between Functional Areas
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fMRI Areas Colormapped
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VOI Placement
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Surface Removed Paths Visible
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VOI Adjusted Different Paths
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Evaluation• Types of functions
–Validation of known pathways
–Hypothesis generation
• Time to explore – 10 minutes for significant exploration
• Speed – Interactive rates
• Interface – Interactive queries
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Alternative Methods
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Alternative Methods
• Diffusion tensor visualization
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White Matter Algorithms
• Streamlines Tracking Techniques
• Fiber Assg thru Cont Tracking
• Tensor-deflection
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Filters
• Length
• Average linear anisotropy
• Regions of interest
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Conclusion
• Multiple data types (DTI & fMRI)• New visualization interface• Interactive queries• Hypothesis generation & testing
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Next Steps
• Real work• Multiple subjects• Normal to abnormal• Acquisition technology• Path tracing algorithms
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Question
• Is there any reason for tools such as this to be validated?
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Question
• If validated this early on, wouldn’t every change pretty much negate the validation?
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Question
• Should there be some kind of benchmark to use to measure these applications against?