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Schlagwörter:
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Zusammenfassung:
The inverse problem in Electro- and Magneto-EncephaloGraphy (EEG/MEG)
aims at reconstructing the underlying current distribution in the human brain using
potential differences and/or magnetic fluxes that are measured non-invasively
directly, or at a close distance, from the head surface. The solution requires repeated
computation of the forward problem, i.e., the simulation of EEG andMEG
fields for a given dipolar source in the brain using a volume-conduction model
of the head. The associated differential equations are derived from the Maxwell
equations. Not only do various head tissues exhibit different conductivities, some
of them are also anisotropic conductors as, e.g., skull and brain white matter.
To our knowledge, previous work has not extensively investigated the impact
of modeling tissue anisotropy on source reconstruction. Currently, there are no
readily available methods that allow direct conductivity measurements. Furthermore,
there is still a lack of sufficiently powerful software packages that would
yield significant reduction of the computation time involved in such complex
models hence satisfying the time-restrictions for the solution of the inverse problem.
In this dissertation, techniques of multimodal Magnetic Resonance Imaging
(MRI) are presented in order to generate high-resolution realistically shaped
anisotropic volume conductor models. One focus is the presentation of an improved
segmentation of the skull by means of a bimodal T1/PD-MRI approach.
The eigenvectors of the conductivity tensors in anisotropic white matter are determined
using whole head Diffusion-Tensor-MRI. The Finite Element (FE) method
in combination with a parallel algebraic multigrid solver yields a highly efficient
solution of the forward problem. After giving an overview of state-of-the-art inverse
methods, new regularization concepts are presented. Next, the sensitivity
of inverse methods to tissue anisotropy is tested. The results show that skull
anisotropy affects significantly EEG source reconstruction whereas white matter
anisotropy affects both EEG and MEG source reconstructions. Therefore, highresolution
FE forward modeling is crucial for an accurate solution of the inverse
problem in EEG and MEG.