Quantification and cross-validation of biomarkers for multi-parametric / multi-modal imaging in pre-clinical models of infectious diseases

The current available tests for diagnosing human brain disorders have different disadvantages and drawbacks, each single disease can be excluded by differential diagnosis, but no real certainty is achievable. Therefore, the medical community feels the urge to develop a strategy able to identify quantitative biomarkers for the etiological diagnosis of diseases, their staging and potential therapy monitoring. Preclinical models of the brain diseases (brain tumours, brain infections, neurological disorders, neurodegeneration, neuroinflammation etc.) are a first step for gaining more insight of underlying disease mechanisms. This MSCA-ITN INSPiRE-MED project at KU Leuven aims to establish multimodal imaging approaches, combining MR spectroscopy, multi-parametric MR imaging, PET and optical imaging methods to identify the most suitable quantitative biomarkers for the assessment of disease progression and therapy monitoring. Hereby, I will focus on brain infections and brain tumours. I will apply machine learning strategies in collaboration with the engineering department at KU Leuven (ESAT) to diagnose and monitor the brain diseases and their treatment. We will test these strategies in collaboration with the other partners of the project for cross-validation of longitudinal in vivo imaging data (for example, secondment at the University of Barcelona for brain tumour models and secondment at Bruker Biospin to learn about PET/ MRI methods for preclinical research). Moreover, these strategies of preclinical studies are expected to be translatable to clinical application by using matching clinical cases. First animal models that will be tested are brain infections due to Cryptococcus neoformans and brain tumours using Gl261 and Hs683 cell lines.