Proton therapy is a tumor treatment technique that uses the unique dose profile of protons to deliver high doses to the tumor while sparing surrounding tissue. This precision creates a need to actively verify the delivered dose during treatment. Many techniques for dose verification have been proposed; most focus on the spatial distribution of secondary particle emissions caused by proton-matter interactions. Among them are prompt gamma’s (PGs), i.e. high-energy photons emitted within 10 ps after a proton interacts with the tissue. Therefore, their emission is linked to the proton in time and space. Using a specialized measurement procedure – prompt gamma timing – and a tomographic-like reconstruction we can retrieve the PGs spatial and temporal distribution [1]. The combination of time and space enables the study of proton motion. Through non-linear regression with proton motion models, we then estimate the stopping power – a crucial material parameter to accurately plan and execute the treatment [2, 3]. Several challenges in instrumentation, reconstruction, modelling and postprocessing must be overcome to implement this unique approach. Still, we have used this novel combination of image computation techniques to estimate the stopping power of a homogeneous phantom with an accuracy of 2
Breast cancer screening in Asian populations faces significant challenges due to high breast density prevalence, which reduces mammographic sensitivity. Low-dose computed tomography (LDCT) scans acquired for lung cancer screening capture breast tissue and present an opportunity for opportunistic breast cancer risk assessment. This study develops and evaluates deep learning frameworks using multiple instance learning (MIL) for breast cancer risk stratification from LDCT scans combined with clinical features. Two complementary approaches were developed: an individual breast model using attention mechanisms for slice-level interpretability, and a bilateral model employing global pooling to capture asymmetry patterns. Evaluated on 60 patients, the individual model achieved 72.7
