Diffusion tensor imaging (DTI) allows characterizing and exploiting diffusion anisotropy effects, thereby providing
important details about tissue microstructure. A major application in neuroimaging is the so-called fiber tracking where
neuronal connections between brain regions are determined non-invasively by DTI. Combining these neural pathways
within the human brain with the localization of activated brain areas provided by functional MRI offers important
information about functional connectivity of brain regions. However, DTI suffers from severe signal reduction due to the
diffusion-weighting. Ultra-high field (UHF) magnetic resonance imaging (MRI) should therefore be advantageous to
increase the intrinsic signal-to-noise ratio (SNR). This in turn enables to acquire high quality data with increased
resolution, which is beneficial for tracking more complex fiber structures. However, UHF MRI imposes some difficulties
mainly due to the larger B1 inhomogeneity compared to 3T MRI. We therefore optimized the parameters to perform DTI
at a 7 Tesla whole body MR scanner equipped with a high performance gradient system and a 32-channel head receive
coil. A Stesjkal Tanner spin-echo EPI sequence was used, to acquire 110 slices with an isotropic voxel-size of 1.2 mm
covering the whole brain. 60 diffusion directions were scanned which allows calculating the principal direction
components of the diffusion vector in each voxel. The results prove that DTI can be performed with high quality at UHF
and that it is possible to explore the SNT benefit of the higher field strength. Combining UHF fMRI data with UHF DTI
results will therefore be a major step towards better neuroimaging methods.
Functional MR imaging (fMRI) enables to detect different activated brain areas according to the performed
tasks. However, data are usually evaluated after the experiment, which prohibits intra-experiment optimization
or more sophisticated applications such as biofeedback experiments. Using a human-brain-interface (HBI), subjects
are able to communicate with external programs, e.g. to navigate through virtual scenes, or to experience
and modify their own brain activation. These applications require the real-time analysis and classification of
activated brain areas.
Our paper presents first results of different strategies for real-time pattern analysis and classification realized
within a flexible experiment control system that enables the volunteers to move through a 3D virtual scene in
real-time using finger tapping tasks, and alternatively only thought-based tasks.