Multimodal Visualization for neurosurgical planning

CMPS 261

May 17th 2010

Uliana Popov

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

GOALS

Where is tumor? Boundaries WM trackts – DTI Combine them all together

How does it look?

FLAIR T2

Overlay ?

Problem

All sequences have different size

How to resize? Interpolate, Add, Reduce...

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.

Symmetry

If a ~= b what is ~ ?

then opacity = 0

else

do nothing

In this way we should get only the asymmetric regions, like tumor.

Results

Results (cont)

In process

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

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)

DTI

30 directions

Q-ball – resolves intravoxel fiber crossing using q-space diffusion imaging. All m diffusion measurements are used.

Randomly

Dot product