by Beatrice Villata
Tumor treatment can be achieved with radiotherapy techniques by irradiating the target volumes with ionizing radiation. Conventionally, photons are the selected particles for the treatment, but in recent years interest in the use of charged particles has been steadily growing in medical physics. The energy deposition of particle therapy allows to spare the organ at risk around the target volume. At the same time, the energy deposition of particle therapy is greatly influenced by range uncertainties provoked by motion or variations in tissue density along the path, requiring precise treatment planning and monitoring techniques to ensure accurate and effective therapy delivery.
One way to achieve this is by analyzing the secondary particles created in the interactions between the charged particles and the tissues. Prompt gammas are candidates for this technique because their creation occurs instantaneously on the interaction site and can escape the patient. For these reasons, prompt gammas are not affected by washout effects and can be easily detected outside the patient.
Knowledge about the irradiated tissues can be recovered by analyzing Prompt Gamma emissions spectra. In the course of this project, we look for spectral abnormalities that could be related to the irradiated tissue and the range of irradiation. The dataset is created with a Geant4 Monte Carlo simulation, and an additional analysis is performed on the dataset presented in the paper “Towards Machine Learning aided
real‐time range imaging in proton therapy” .
The spectra are analyzed with a variational autoencoder (VAE). The final objective is to carry on this analysis online, and the final architecture will be coded on an FPGA to trigger in real-time a beam gating system.
 Lerendegui-Marco, J. et al. (2022) ‘Towards machine learning aided real-time range imaging in Proton therapy’, Scientific Reports, 12(1). doi:10.1038/s41598-022-06126-6.