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Analysis and processing of Diffusion Weighted MRI

Analysis and processing of Diffusion Weighted MRI. Remco Duits Anna Vilanova Luc Florack. Tom Dela Haije Rutger Fick. Supervised by : Collaboration : with. Overview of presentation. Short introduction to DW-MRI Enhancement of DW-MRI data Fiber tracking. Diffusion of water.

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Analysis and processing of Diffusion Weighted MRI

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  1. Analysis and processing of DiffusionWeighted MRI Remco Duits Anna Vilanova Luc Florack Tom Dela Haije Rutger Fick Supervisedby: Collaboration: with

  2. Overview of presentation Short introduction to DW-MRI Enhancement of DW-MRI data Fiber tracking

  3. Diffusion of water Diffusion is dependentonorientation

  4. Visualization 4

  5. Goal

  6. Overview Water Diffusion Modelling Fiber PDF Tensor(s) Raw Data Water PDF Fiber Tracking Low signal for high diffusion Other models Clinical Information

  7. ConstrainedSphericalDeconvolution Constrained SphericalDeconvolution Original data (single fiber) SphericalDeconvolution

  8. Enhancement of PDFs • PDFs contain information on the direction of water diffusion (water PDF) or fiber distribution (fiber PDF) • Many models canbeconverted to a PDF - Oftennoisy and incoherent

  9. Rotatingcoordinate system z y x diffusion diffusion

  10. Evolutions in new frame Contour Enhancement Contour Enhancement

  11. Evolutions in new frame Contour completion Contour Completion

  12. Results

  13. Resultsonsimplefibertracking Fibertrackingon CSD Fibertracking on enhanced CSD Phantom dataset from the ISBI reconstructionchallenge (2013)

  14. Fiber Tracking • Problem: findanatomical fibers basedon DW-MRI scan • Variants • Findbrain fiber betweentwo areas • Find all fibers that pass throughanarea • Mathematicalproblem? • Multiple options

  15. Local fiber tracking Streamlinetracing: • Compute main direction of diffusion (AKA: reduce to vectorfield: ) • Integratealongvectorfieldfromgivenseedpoint

  16. Advantages/Disadvantages • Advantages • Computationallycheap • Easy to implement • Disadvantages • Erroraccumulation • Sensitive to noise

  17. Localmethod: example

  18. Global fibertracking • curve Curvature • Corresponding energy functional Solvedfor C(x)=1 Externalcost (data) Geodesicenergy • Find for given end points/directions

  19. Horizontal curves

  20. Lifting the optimal curve problem to The energy functional to minimize subject to the constraints along the curve:

  21. Solutionssub-RiemanniangeodesicsGhosh&Dela Haije&Duits

  22. Optimal control problem

  23. Benefits and disadvantages • Advantages • Robust to noise • No erroraccumulation • Disadvantages • Computationallyexpensive • Needs more boundaryconditions • Cansacrificelocalerrorforglobaloptimization

  24. Global method:example

  25. New idea: combine local and global • Not global energy minimizers, but limit search to smaller search areas and combine solutions • Addadditionalconstraints to limit search space • Limit curvature to bebelowthreshold • Do extra constraintschangeoptimal curve problem?

  26. Intuition

  27. Search area Simulateconvection Geodesics to endpoints

  28. Theoreticalbenefits • Advantages • Robust to noise • Computationalintermediate • Balance between local and global error • Limits to localorglobalmethodfor search areasmallorlarge • Disadvantages • Extra parameters thatneed to betuned

  29. How to find optimum curve? • Minimizermaynotexist • Minimizermaynotbeunique • Different options • UseDijkstra to findcheapestpathalongtree-graph (restrictsenergyfunction) • Try discrete subset of curves • Getanapproximateminimizer and iterativelyrefineit

  30. Past and Plans Article published in NM-TMA (feb ‘13) Enhancement Article published in JIMV Refineideas and publishproof-of-concept to MICCAI conference (June) Expandforjournalarticle Visit Berlin to workonnewnon-linearenhancementtechnique (August)

  31. Anyquestions, ideasorsuggestions?

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