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Anna Vilanova

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Anna Vilanova

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Anna Vilanova

  1. 1. Planning of brain surgery with MRI-based fiber tractography Uncertainty Visualization Anna Vilanova Computer Graphics & Visualization https://graphics.tudelft.nl/anna-vilanova/
  2. 2. MR measurements Measure Diffusion Weighted signal 𝑆𝑖 in a given direction 𝐷𝑖 is often called 𝐴𝐷𝐶𝑖 (Apparent Diffusion Coefficient ) Stejskal-Tanner relationship attenuation signal 𝑆𝑖 and 𝐷𝑖 𝑆𝑖 = 𝑆0 𝑒−𝑏𝐷 𝑖 where 𝑆0 not diffusion weighted 𝑏 protocol parameter (diffusion time, etc.)
  3. 3. ( , )   HARDI Voxel Diffusion Tensor Imaging Voxel Background: Diffusion-Weighted MRI
  4. 4. xx xy xz yx yy yz zx zy zz D D D D D D D D D            D Tensor Field Second-order tensors
  5. 5. xxD xyD xzD yxD yyD yzD zxD zyD zzD xx xy xz yx yy yz zx zy zz D D D D D D D D D            D Tensor Field
  6. 6. Slide 7 Glyphs Tensor Field Lines Tractography Texture Scalar Fields Tensor Field Visualization Segmentation
  7. 7. Tumor Dissection Tumor Tumor
  8. 8. Visualization PipelineUncertainty Sources
  9. 9. Visualization Pipeline 2nd Order Tensor Spherical Harmonics 30 2( , ) ( , )e T R iS t p t d S    0 q dq r | r r ( , )S tq 0S Model Accuracy Model Parameters ... Noise Numerical Error Acquisition Artifacts ...
  10. 10. Visualization Pipeline Fiber Tracking Moberts et al. IEEE Vis 2005 Parameters Resampling Filtering ...
  11. 11. Visualization Pipeline Otten et al. EuroVis 2010 Illumination Parameters ...
  12. 12. Uncertainty Sources
  13. 13. How does streamline fiber tracking work? • Start in seed-point • Compute tensor’s main eigenvector • Take small step in direction of main eigenvector • Repeat until threshold hit Seed-point 1 ( ) ( ( )) ( )p s e p s ds p s s  path with parameter
  14. 14. Sensitivity to input parameters • Integration step size, stopping thresholds, etc. • Often re-used between datasets • Stability is rarely evaluated • Small variations can result in large visual differences • Visualize the parameter sensitivity R. Brecheisen et al. IEEE TVCG 2009
  15. 15. Stopping thresholds in streamline tracing 16/ Anisotropy Angle
  16. 16. Zero-threshold fiber tracking
  17. 17. Computing quantitative tract features Anisotropy Angle Average Fiber Length Feature MapAngle Anisotropy Anisotropy Angle
  18. 18. Threshold selection
  19. 19. 20
  20. 20. 21
  21. 21. Uncertainty Sources
  22. 22. Uncertainty Effect Single White Matter fiber Variations due to Uncertainty Distance Margin
  23. 23. Capturing noise and model uncertainty • Propagation of errors (Anderson 2001, Poonawalla 2004) • Requires noise model • Propagation through nonlinear transformations • Repeated scans • Highly impractical except for inanimate objects • Repetition bootstrap (Pajevic and Basser 2003) • Limited number of gradient directions • Wild bootstrap (Flachaire 2005, Whitcher 2005, Jones 2008) • Requires only single dataset • Tries to approximate repetition bootstrap
  24. 24. How does wild bootstrap work? Repeat this 100+ times / Residual
  25. 25. Wild bootstrap – multiple seed points Showing all streamlines is not very insightful Original tracking result 100 tracking variations Pyramidal (Motor) Tract Saggital view Which streamlines are more certain than others?
  26. 26. Uncertainty Visualization 0 10 20 30 40 50 60 70 Variations due to Uncertainty
  27. 27. Illustrative confidence intervals • Subdivide the fiber collection into discrete regions with given confidence interval • We need a confidence measure describing likelihood of each fiber • Wild bootstrap: streamline distance R. Brecheisen et al. The Visual Computer 2012
  28. 28. Computing streamline distance For each seed point, compute pairwise distances between streamlines Choose a ´mean´ fiber: • Fiber from original tensor volume • Fiber with minimum sum of distances / Seed points ‘Mean’ fiber
  29. 29. Confidence histogram widget / • Defining and changing intervals • Adjusting visual styles • Shows histogram of confidence values in fiber set
  30. 30. Illustrative confidence intervals • Try to classify the streamlines into ‘confidence’ categories or intervals • Use silhouette/contour representation • Reduces clutter • Shows distinct % regions 10% 95% 50%
  31. 31. Rendering confidence intervals / Build sorted table with fiber ID’s and associated confidence values
  32. 32. Example visualizations Informal user evalation: combination of reduced opacity and light-to-dark colors 07-10 Increasing silhouette dilation Decreasing opacity Warm to cool colors Light to dark colors
  33. 33. Uncertainty lens Showing uncertainty everywhere is likely to be confusing Uncertainty lens
  34. 34. Slide 36
  35. 35. Slide 37
  36. 36. Conclusions • Uncertainty has large influence in tractography • Parameter sensitivity visual analysis and visualization of confidence interval are starting points Slide 38
  37. 37. Conclusions • Uncertainty has large influence in tractography • Parameter sensitivity visual analysis and visualization of confidence interval are starting points • How to take all uncertainties into account? • What are the most important uncertainties? • How to visualize the uncertainties effectively for surgery? • How much influence has the uncertainty in the decision making? Slide 40
  38. 38. Thank you • Ralph Brecheisen • Vesna Prckovska • Paulo Rodrigues • Neda Sepasian • Tim Peeters More information: https://graphics.tudelft.nl/anna-vilanova/ Geert Jan Rutten (Elisabeth-Tweesteden Hospital) Carola van Pul (Maxima Medical Center Eindhoven) Pim Pullens and Rainer Goebel (BrainVoyager Maastricht) Martijn Froeling (Eindhoven Univ. of Technology) Pieter Kubben, MD (Maastricht Univ. Hospital)

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