Multimodal Visualization for neurosurgical planning CMPS 261 May 17 th 2010 Uliana Popov.
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Transcript of Multimodal Visualization for neurosurgical planning CMPS 261 May 17 th 2010 Uliana Popov.
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Multimodal Visualization for neurosurgical planning
CMPS 261
May 17th 2010
Uliana Popov
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DATA
Input – MRI sequences Example
File Name Sequence Dimension Data type and Byte Ordering
case2_CT.raw CT 414,535,577,1 Int16 / little endian
case2_DTI.raw DTI 128,128,72,62 Unsigned int / little endian
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GOALS
Where is tumor? Boundaries WM trackts – DTI Combine them all together
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How does it look?
FLAIR T2
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Overlay ?
Problem
All sequences have different size
How to resize? Interpolate, Add, Reduce...
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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.
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Symmetry
If a ~= b what is ~ ?
then opacity = 0
else
do nothing
In this way we should get only the asymmetric regions, like tumor.
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Results
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Results (cont)
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In process
Registration Look at the gradients - boundaries Compare the suspected regions and vote which side? Flip a coin...
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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)
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DTI
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30 directions
Q-ball – resolves intravoxel fiber crossing using q-space diffusion imaging. All m diffusion measurements are used.
Randomly
Dot product