Equipped with state-of-the-art technology, we develop and optimize MRI pulse sequences to acquire quantitative MRI parameters at ultra-high resolutions. We work on reconstruction- and postprocessing algorithms to compensate for artifacts associated with high field strength MRI systems. These efforts include developing methods that handle physiological noise and head motion or designing schemes to reduce scan times. We also optimize the acquisition of data on highly specialized equipment, such as implementing spiral acquisition schemes for diffusion imaging on one of world-wide only three Connectom scanners, and of data for specific neuroscientific applications, such as functional imaging of the basal ganglia.
Using a field strength of 7 Tesla, the "Arterial Blood Contrast" (ABC), which is based on the Magnetization Transfer effect, could be measured with an isotropic spatial resolution of 1.5 mm in combination with a conventional functional MRI contrast.
DeepcomplexMRI deep learnig reconstruction has been modified to process multi-echo MRI images. First results for different undersampling strategies suggest that performance is comparable to modern iterative algorithms like ENLIVE while taking only about 5 minutes to reconstruct a full 3D 1mm³/voxel resolved head image stack.
We implemented and optimized kt-points RF pulses to reduce bias and shading artifacts in whole brain ultra-high resolution MPM.
In this project, we study the resolution limits of different high-resolution functional magnetic resonance imaging (fMRI) methods to resolve differences within the cerebral cortex.