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- A complete implementation of the whole process from preprocessing of the diffusion tensor data to the nonrigid alignment of two different data sets has been implemented. The implementation also is a framework to process and store tensor data, so that further work can be based on the developed classes.
- A comparison and assessment of different tensor reorientation strategies.
- Different approaches to extract points in grayscale and tensor data are discussed and implemented.
- A new visualization method of tensor data is presented.

The new visualization method is very useful to inspect small volumes of tensor data. It is the most precise way of displaying tensor data sets as all the information in the data is represented in the ellipsoids. Displaying the tensors as a three dimensional structure is also probably the best way to understand the properties of this data structure. Furthermore, it enables a developer to prove visually the correctness of any algorithm implemented and to check if the results of any transformation of the data has a meaningful interpretation.

Besides optimizing the code for performance, further investigation of general improvements and extensions would be interesting. Some of these ideas will be presented in the next section.

Thus, the extension to three dimensions primarily means finding a method for testing the correctness of all functions.

As it can be assumed that diffusion tensor data has no meaningful
interpretation in regions of gray matter, a first step in incorporating
T2 weighted data would be to mask the tensor data with the segmented
white matter and use the border between gray and white matter, as a
boundary condition when matching diffusion tensor data.

As any displacement of tensor data can be performed in two separate steps, (displacement of the single components and local transformation of the tensor), any displacement can be applied on tensor data. It would also be interesting to compare the results when using displacement fields derived from other then tensor data. This should be easy if the tensor data is aligned with the SPGR data. Any transformation, (rigid and nonrigid), applied on the SPGR data can also be applied on the tensor data and the corresponding local transformation of the tensors performed after the displacement of the single components.

In Section 7.3 regarding the interpolation of the
data, it was mentioned that the variogram models have to be empirically
selected for any given alignment. A way to find the optimal variogram
model would be to select landmark points manually in a number of cases and
match the corresponding points. Then the resulting displacements can
be statistically analyzed to find the best variogram function for the
interpolation of the displacement fields.

The visualization of the tensors as ellipsoids is only useful for
small data volumes. As can be seen in the example images in Appendix
B, the information provided becomes too large for
an interpretation. The next step is to display the tensors as fibers,
i.e. connect the tensors by following the largest
eigenvalue. Fiber-tracking is a large research area; the correct
identification of a path is certainly not trivial. VTK provides a class
`vtkHyperStreamline` to connect tensors. As the images with the
tensor-ellipsoids suggest, it should be possible, at least in certain regions with
clear orientation of the tensors, to apply this VTK data
structure to connect single tensors. The connected tensors are then
visualized as a tube, which would represent a fiber. A prototype for
such a
function is included in the class `display.h`.

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