- Exploitation of different image modalities, such as Computed Tomography (CT), anatomical and quantitative Magnetic Resonance Imaging (MRI), for radiotherapy treatment personalization. Among the investigated functional MRI techniques, a speficic focus is given to Diffusion-Weighted MRI (DW-MRI)
- Implementation of standardized and innovative image processing approaches, such as radiomics and machine learning, for patient stratification and treatment response prediction (Radiomics and Artificial Intelligence)
- Implementation of personalized tumor control probability and radiobiological models, by improving state-of-the-art methods with patient-specific image-related information
- Digital phantoms of micro-structural models are combined to Monte Carlo simulations of diffusion-weighted MR signals to better understand the interaction of the radiation beam with the tissue, especially for particle therapy applications.
Projects on multi-parametric imaging rely on the collaboration with the National Centre of Oncological Hadrontherapy (CNAO, Pavia, Italy) for data collection. A recent collaboration has been also established with the Centre for Medical Image Computing (CMIC) of the University College of London (UCL, London, UK) for its experience on Monte Carlo simulations in DW-MRI.
Additionally, activities on multi-parametric imaging benefit from the recent funded AIRC project TAILOR “A technical framework for combining multi-parametric imaging with advanced modelling in personalized radiotherapy”.
