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Multimodal Visualization for neurosurgical planning

Multimodal Visualization for neurosurgical planning. CMPS 261 May 17 th 2010 Uliana Popov. DATA. Input – MRI sequences Example. GOALS. Where is tumor? Boundaries WM trackts – DTI Combine them all together. How does it look?. FLAIR T2. Overlay ?.

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Multimodal Visualization for neurosurgical planning

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  1. Multimodal Visualization for neurosurgical planning CMPS 261 May 17th 2010 Uliana Popov

  2. DATA • Input – MRI sequences • Example

  3. GOALS • Where is tumor? • Boundaries • WM trackts – DTI • Combine them all together

  4. How does it look? FLAIR T2

  5. Overlay ? Problem All sequences have different size How to resize? Interpolate, Add, Reduce...

  6. Image Registration Process of transforming the different sets of data into one coordinate system. LONI (Laboratory of Neuro Imaging, UCLA) • AIR (Automated Image Registration – tool for automated registration of 3D and 2D images within and across subjects and across imaging modalities.

  7. Symmetry If a ~= b what is ~ ? then opacity = 0 else do nothing In this way we should get only the asymmetric regions, like tumor.

  8. Results

  9. Results (cont)

  10. In process • Registration • Look at the gradients - boundaries • Compare the suspected regions and vote • which side? Flip a coin...

  11. DTI Done: Take 6 different directions (gradient directions) Calculate products of the gradients Build a matrix M, mxn Calculate pseudo inverse M' (install lapack!) Each row of M' – dual basis element (dbe) Diffusion tensor = sum over all dbe TBD: How to choose 6 out of 30 Calculate RA (relative anisotropy) and FA (fractional anisotropy) Visualize (tracking lines in high order tensor fields – HOT lines)

  12. DTI

  13. 30 directions Q-ball – resolves intravoxel fiber crossing using q-space diffusion imaging. All m diffusion measurements are used. Randomly Dot product

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