Research in Computer-Aided Radiotherapy (CART) and Computer-Aided Surgery (CAS) focuses on leveraging advanced medical imaging, computational modeling, and innovative technologies to enhance cancer treatment and surgical interventions.
CART research is dedicated to enhancing radiotherapy accuracy and effectiveness through 2D-3D-4D medical imaging, methods for automatic structures delineation (segmentation), rigid and elastic image registration for planning and on the integration between in-room imaging and technologies for organ motion management, patient position, treatment verification and tumor localization in image-guided radiotherapy, as photon and particle therapy (proton and carbon ion therapy).
Specific research topics are:
- Time-resolved robust treatment planning and radiation dose delivery for the treatment of moving organs (Organ Motion Management).
- Methods for treatment personalization and adaptation are developed and tested relying on Multi-parametric Imaging and clinical-related information and advanced modelling tools.
- Design, development and clinical testing of robotic technologies for in-room patient imaging (In-room Systems) and 2D-3D, 3D-3D image registration are part of the activities of the group in Computer Aided Radiotherapy.
- Validation Strategies for testing and benchmarking of computer-aided solutions.
A notable collaboration with the National Centre of Oncological Hadrontherapy (CNAO, Pavia, Italy) has resulted in the design, manufacturing, installation, commissioning and clinical implementation of a customized integrated system for precise optical and image-based patient positioning and verification in proton-therapy and carbon-ion therapy.
CAS research is focused on modeling of anatomo-pathological structures from volumetric images (CT, MRI) to provide decision support tools in surgical planning and navigation.
Latest developments in the fields of Radiomics and Artificial Intelligence are currently under investigation in the framework of a project for lung cancer screening in collaboration with Università degli studi di Milano Bicocca (Milan, Italy) and CERN (Geneva, Switzerland). This project aims at finding imaging features which allow detecting lung nodules and predicting their probability of malignancy.
