The treatment of acute brain strokes requires very careful decisions in a very narrow time-window. Shortly after patients are admitted to the hospital and scans have been acquired, clinicians need to decide which treatment path offers best chances survival and avoidance of brain damage. These decisions have to be based on a multitude of 3D tomographic image data (CT perfusion maps and potentially thermal imaging) and other clinical indicators (patient age, NIHSS, etc).

Novel image processing techniques, mathematical models and machine learning algorithms, which can deal with the challenges of real clinical data, will be devised, implemented and tested during this research project. A large dataset of retrospective multispectral images of stroke patients is employed for the development and training of new models.

The new algorithms will be used to derive an automatic prediction of a pixel-wise map of tissue that is likely to be recovered if a certain treatment (in particular vessel recanalisation) is performed and how urgent this intervention is. The project is being carried out in close collaboration with the neuroradiology department.