The general aim of the project is to reconstruct complete cortical cerebrovascular networks on the basis of partially incomplete, high-resolution tomographic data.
We are developing intelligent algorithms based on known morphological features of the cerebral vasculature. Ultimately we will generate a full vessel network based on insufficient data. Even if we focus on optimizing the results of an incomplete and error-prone measurement, the developed technology will in the future also support enhancing low resolution-images that cannot resolve smaller vessels, by filling up the missing parts of the vascular network based on a tissue footprint, either explicitly or in a statistical fashion. This would facilitate a reduction of imaging efforts substantially by moving to lower resolution imaging with the gain of a larger field of view e.g. of µCT instead of synchrotron radiation based tomography, or even extending the application domain to in vivo investigations relying on macroscopic imaging modalities like CT or MRI. Secondly, the methodology may later be used to combine individual patches of a sample to a larger volume by filling up the voids in a consistent fashion, even allowing the integration of image information extracted from different modalities.