Reconstruction and visualization of neuronal pathways from diffusion tensor data

has become an important source of medical data. About a decade ago, a
novel MRI technique called diffusion tensor imaging (DTI) evolved. Due
to its ability to reflect the location and structure of fibrous tissue
such as white matter in vivo, this technique has gained increasing
interest in different research disciplines.
For neurosurgery, DTI data is of high value since information about the
location and the course of white matter tract systems is provided, thus
supporting the anatomical information obtained from MRI. White matter
tracts, i.e. motor or sensory pathways, are important structures within
the human brain. In order to avoid neurological deficits after brain
surgery, these fiber tracts must remain intact.
However, the reconstruction of neuronal structures from DTI data is a
non-trivial task due to the complex tensor information that is captured
per voxel. For this reason, extensive research has been conducted in
recent years in order to develop techniques for the processing and
visualization of DTI tensor data.
In this talk, new techniques for the reconstruction and visualization of
white matter tracts are presented which contribute to current research.
The different approaches were developed in collaboration with
neurosurgeons and are intended to support preoperative planning and
intraoperative guidance in surgical interventions. For this purpose, a
DTI toolbox comprising dataset processing, tensor reconstruction,
filtering techniques, fiber tracking and connectivity analysis, hull
algorithms and different visualization approaches has been developed.
In the future, the research currently conducted in the field of DTI will
contribute to the further improvement of planning in neurosurgery and to
the reduction of the inherent risk of postoperative neurological
deficits for the patients.