| |
| Title: | Nonrigid registration of diffusion tensor images
10.1999-3.2001
|
| Full Report: | HTML Version, PDF Version |
| Abstract: | This thesis discusses diffusion tensor imaging as a Magnetic Resonance Imaging
modality. Diffusion tensor imaging allows the observation of molecular diffusion in
tissues in vivo and therefore the molecular organization in tissues. The main
interest in this case is the observation of myelinated fibertracts in the brain of premature
born babies. Myelination of fibers in the white matter of the brain is a fast process in the
last few weeks preterm and the observation of this process gives an insight in the
development of the human brain and allows a better and earlier detection of small injuries
or abnormalities.
The goal is to match diffusion tensor images of neonates, and
to build an enabling technology to ultimately generate a
statistical atlas of the development of the brain in babies between 28 and 40 weeks
postconceptional age. While it was not possible to build the statistical atlas in the time
given, the complete process from preprocessing of the data to nonrigid
alignment of
diffusion tensor images has been implemented and successfully applied on some
exemplary cases.
To better understand the characteristics of diffusion tensors and to be able to prove the
correctness of the algorithms, a new way of displaying diffusion tensors was
implemented. This method visualizes the diffusion tensor as ellipsoids in a
voxel raster.
The following report outlines the medical background, the imaging acquisition process
and the data processing path. The reader should be able to
understand diffusion tensor imaging and the matching principles used and expand the
provided software to fit specific and further needs.
To successfully build a meaningful atlas of the development of the brain in neonates, a
number of three-dimensional cases needs to be processed and a statistical analysis of the
results has to be performed. Therefore a correct incorporation of the different voxel
dimensions has to be implemented. As the data quality of the diffusion tensor images in
baby scans is very low, the incorporation and combination with other scanning modalities
should be considered.In the past few years virtual reality based systems have been proposed
and realized for many medical interventions. These simulators
have the potential to provide training on a wide variety of
pathologies. So far, realistic generation of anatomical variance and pathologies have not
been treated as a specific issue. It has to
be possible for a physician to generate an individual surgical scene
for every training session.
|