It is now a well-known fact that proton therapy (PT) suffers from range uncertainties that limit the full exploitation of the dosimetric advantages of PT as an effective cancer treatment modality. Range uncertainties might result from errors in the estimation of the proton stopping power in tissue, patient setup errors, anatomical changes and organ motion. To mitigate the undesired effects of range uncertainties, several online range and dose monitoring techniques are being investigated. The project at hand focuses on further developments of a novel concept being developed within the so-called NOVO (Next Generation Imaging for Real-Time Dose Verification Enabling Adaptive Proton Therapy) project.

In the NOVO project, a novel detector array, NOVCoDA (the NOVO Compact Detector Array) capable of simultaneous detection and imaging of secondary prompt gamma-rays and fast neutrons is being developed in an international effort funded by the EIC Pathfinder Open. In this project, within RAPTORplus, the candidate will further explore pre-clinical evaluations of NOVCoDA via in-silico models supplied with experimental data collected in clinically relevant proton beamlines. As part of these evaluations, the project will also explore use of advanced data analysis techniques based on Artificial Intelligence (e.g., deep learning techniques and multivariate regressions) to enhance the detection probability of range and dose deviations from the available detector data.

The main objective of the project is the confirmation of range shift detection capabilities of NOVCoDA under pre-clinical conditions as well as unravelling NOVCoDA’s limitations in this regard. As an important part of the project, the clinical translation potential of NOVCoDA will be explored by comparison to the state-of-the-art systems that are based on the detection of secondary prompt gamma-rays, in particular, prompt gamma-ray imaging (PGI) and timing (PGT) systems.