Geophysical Joint Inversion for Accurate Brain Myelin Mapping
The research community has long been on the trail of a substance that could be a key to understanding many neurological diseases: myelin is a biomembrane that surrounds most neurons in the human brain. “In many diseases, like multiple sclerosis for example, the myelin content of the brain decreases,” explains Prof. Dr. Tony Stöcker of the German Center for Neurodegenerative Diseases (DZNE). “Myelin can therefore serve as a marker for early detection: if we observe in good time that the myelin content is decreasing, then we could catch the onset of a disease at an early stage.”
The trouble is that it has been difficult so far to precisely quantify how much myelin is present. Experienced radiologists can assess brain tissue subjectively from MRI scans, where the variations in contrast allow them to draw conclusions about the state of the myelin, however they cannot determine the exact amount.
In this project, a method will be developed that can automatically calculate how much myelin is present in the brain from a combination of different MRI scans. The researchers will be using methods that are already being used in geophysics to deduce information about underground layers of earth from images of the ground surface. This involves complex mathematical tools and inversion methods, which have never before been used in medical imaging.
If successful, the new method could greatly facilitate the early detection and even the treatment of neurodegenerative diseases.
Tackling the segmentation and tracking challenges of growing colonies and microbialdiversityAn artificial intelligence will observe the growth of bacteria: from microscope images of bacterial cultures taken at regular intervals, it will precisely track the development and division of individual cells – even when multiple bacterial species are cultivated together.
Neuroimaging Biomarkers for Restless Leg SyndromeThe aim is to develop a software solution that can analyse enormous amounts of data on tens of thousands of subjects from large-scale health studies. Using restless leg syndrome as an example, genomic data will be combined with neuroimaging data in order to identify new biomarkers with the help of machine learning methods.
Breaking resolution limit of electron microscopy for magnetic materialsA new method will make it possible to take images of the magnetic properties of materials under the electron microscope and to correlate these properties with their atomic structure. In order to achieve high resolution, a special algorithm must be developed to compute the magnetic properties from the microscope data.