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

2. DATA Input – MRI sequences Example File NameSequenceDimensionData type and Byte Ordering case2_CT.rawCT414,535,577,1Int16 / little endian case2_DTI.rawDTI128,128,72,62Unsigned int / little endian

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