Senior Supervisor N094 & N790
Image guided radiotherapy, multiparametric imaging for tissue characterization, pre-clinical animal research in the context of radiation oncology, dose response modelling, proton and carbon ion therapy.
High precision radiation oncology aims to destroy the tumor without damaging normal tissue and organs.
In order to achieve this delicate balance and to optimize treatment outcome in radiation oncology, following challenges are tackled in an interdisciplinary manner in clinical and pre-clinical medical radiation research.
First beam qualities (photons, electrons, protons, carbon ions) are characterized applying physical and biological methods (cell lines, spheroid/organoids, animal models).
Next, we perform research to improve our understanding of dose response at the molecular, cellular and tissue level. When applying radiation in cancer care, the role of imaging has dramatically increased ("if you can’t see it you can’t treat it, if you can’t treat it you can’t cure it").
Therefore, we develop and bring technological innovations in medical imaging into the clinic. Besides anatomic imaging, multiparametric functional imaging has become an important non-invasive tool for tissue characterization prior treatment and for response assessment. Although hardware developments have an intrinsic role since the advent of radiation medicine, software plays an equal role today.
Data Scientists develop tools to automate processes in the treatment chain (e.g. tissue segmentation, automated treatment planning, biological optimization, tumor sub-volume analysis) or to improve tissue sparing via image guided synchronization of organ motion and beam delivery. These developments are mainly knowledge driven approaches and based on artificial intelligence.
More recently we have started to build up a large clinical database to extract outcome information from real world clinical data to prove and generate clinically driven hypothesis.