Pushing the boundaries of FLASH irradiation: We are investigating neutron exposure to ensure the next generation of particle therapy is as safe as it is effective. Challenge FLASH irradiation is a promising method for significantly sparing healthy tissue during high-energy ionising radiation treatment. However, when using protons or carbon ions, fragmentation produces neutrons. Due to their high biological effectiveness, these neutrons pose a non-negligible risk of secondary malignancies. Furthermore, the extent of neutron exposure associated with bolus materials remains unclear for clinical use. Scientific Approach The project combines experimental measurements with Monte Carlo simulations. Local neutron dose distributions and energy spectra are measured using thermoluminescence ...

Decoding the impact of radiation: Using advanced Monte Carlo simulations to model biological effects at the cellular level for more precise cancer treatments. The Challenge Particle beams exhibit an elevated relative biological effectiveness (RBE) compared to photon beams. However, calculating RBE is highly complex; current models, such as LEM and MKM, show uncertainties of up to 30%, and they have not yet been clinically established for proton beams. There is a critical need for more precise predictions regarding effects at the cellular level. Scientific Approach The project employs Monte Carlo simulations as an efficient method to characterise the effects of ionising radiation. The focus is on further optimising and experimentally validating complex chemical and biological effects ...

Smart radiotherapy for personalized care: Leveraging Artificial Intelligence to adapt radiation doses in real-time to a patient's changing anatomy. Challenge Adaptive radiotherapy focuses on a patient's anatomical changes between fractions—such as weight loss or fluctuations in bladder and bowel filling. A key challenge is determining whether a recalculation and re-optimisation of the dose distribution is required to maintain treatment accuracy. Scientific Approach The project aims to develop and validate artificial intelligence neural networks to enable rapid, independent assessment of daily imaging data. This allows for a more efficient evaluation of whether anatomical changes necessitate a change in the radiation plan. Objectives and Impact The objective is to provide medical ...

Clearer images, lower dose: Revolutionising CT imaging through spectral technology to improve diagnostics while significantly reducing radiation exposure. Challenge The increasing frequency of CT examinations leads to higher radiation exposure across the population. Furthermore, the presence of metallic implants creates significant issues, such as beam starvation and streaks, which can compromise image quality and render images non-evaluable. Scientific Approach Modified imaging techniques combined with spectral CT are being developed to improve diagnostic quality while simultaneously reducing radiation exposure. Key objectives include improving the signal-to-noise ratio for better tissue differentiation, reducing metal-induced artefacts, and investigating ways to reduce the need ...