In a carbon ion treatment the nuclear fragmentation of both target and beam projectiles impacts on the dose released on the tumour and on the surrounding healthy tissues. Carbon ion fragmentation occurring inside the patient body has to be studied in order to take into account this contribution. These data are also important for the development of the range monitoring techniques with charged particles. The production of charged fragments generated by carbon ion beams of 115-353 MeV/u kinetic energy impinging on carbon, oxygen and hydrogen targets has been measured at the CNAO particle therapy center (Pavia, Italy). The use of thin targets of graphite (C), PMMA (C2O5H8) and polyvinyl-toluene (plastic scintillator, CbHa) allowed to measure fragments production cross sections, exploiting a Time of Flight (ToF) technique. Plastic scintillator detectors have been used to perform the ToF measurements, while LYSO crystals have been used for the deposited energy measurement and to perform particle identification. Cross sections have been measured at 90 and 60 degrees with respect to the beam direction. The measured proton, deuteron and triton differential production cross sections on C, O and H, obtained exploiting the target subtraction strategy, are presented here as a function of the fragment kinetic energy.

In this work, an FPGA-based plain delay line TDC is presented, together with a theoretical model on its timing properties. The TDC features an automated calibration system implemented in the on-chip processor of a SoC-FPGA, uses a low amount of FPGA resources and is therefore suitable for applications requiring a high number of channels such as Time-of-Flight Positron Emission Tomography. We first investigated the importance of calibration and validated the theoretical model on the TDC timing properties. Finally, the device has been embodied into a two channel Positron Emission Tomography acquisition system and tested. We found the calibration essential to obtain a good time resolution (38 ps FWHM in comparison with a 78 ps FWHM obtained with the uncalibrated device). The model we developed is able to predict the TDC timing properties. They are shown to be related to the fundamental parameters of the used FPGA technology. In particular, the best achievable time resolution of this specific architecture (plain tapped delay line on FPGA) is set to about 30 ps by the sum of the setup and hold times of the registers in the FPGA. The timing resolution of the two-channel setup is about 118 ps.

The correct quantification of the dose released in charged particle therapy treatments requires the knowledge of the double differential fragmentation cross section of particles composing both beam and target. The FOOT experiment aims at measuring these cross sections for ions of interest for charged particle therapy applications. The paper describes the performance of the TOF-Wall detector of the experiment. The detector is composed of two layers of 44 cm x 2 cm x 3 mm plastic scintillator bars (20 for each layer), arranged orthogonally and read out by silicon photomultipliers. The detector is designed to identify the charge of fragments ranging from protons to oxygen ions, with a maximum energy of 700 MeV/u, by measuring the energy released in the scintillators and the time of flight with respect to a start counter. In this study, the detector was scanned with carbon ions of energy between 115 MeV/u and 400 MeV/u and with a 60 MeV proton beam. The measurements show an energy resolution (σE/μE) between 6% and 4% and a contribution of the detector to the TOF system time resolution between 25 ps and 20 ps (standard deviation) for carbon ions and between 100 ps and 80 ps for protons.

The TRIMAGE project aims to develop a brain-dedicated PET/MR/EEG (Positron Emission Tomography/Magnetic Resonance/Electroencephalogram) system that is able to perform simultaneous PET, MR and EEG acquisitions. The PET component consists of a full ring with 18 sectors. Each sector includes three square detector modules based on dual sstaggered LYSO:Ce matrices read out by SiPMs. Using Monte Carlo simulations and following NEMA (National Electrical Manufacturers Association) guidelines, image quality procedures have been applied to evaluate the performance of the PET component of the system. The performance are reported in terms of spatial resolution, uniformity, recovery coefficient, spill over ratio, noise equivalent count rate (NECR) and scatter fraction. The results show that the TRIMAGE system is at the top of the current brain PET technologies.

Objective. This study addresses a fundamental limitation of in-beam positron emission tomography (IB-PET) in proton therapy: the lack of direct anatomical representation in the images it produces. We aim to overcome this shortcoming by pioneering the application of deep learning techniques to create synthetic control CT images (sCT) from combining IB-PET and planning CT scan data. Approach. We conducted simulations involving six patients who underwent irradiation with proton beams. Leveraging the architecture of a visual transformer (ViT) neural network, we developed a model to generate sCT images of these patients using the planning CT scans and the inter-fractional simulated PET activity maps during irradiation. To evaluate the model’s performance, a comparison was conducted between the sCT images produced by the ViT model and the authentic control CT images—serving as the benchmark. Main results. The structural similarity index was computed at a mean value across all patients of 0.91, while the mean absolute error measured 22 Hounsfield Units (HU). Root mean squared error and peak signal-to-noise ratio values were 56 HU and 30 dB, respectively. The Dice similarity coefficient exhibited a value of 0.98. These values are comparable to or exceed those found in the literature. More than 70% of the synthetic morphological changes were found to be geometrically compatible with the ones reported in the real control CT scan. Significance. Our study presents an innovative approach to surface the hidden anatomical information of IB-PET in proton therapy. Our ViT-based model successfully generates sCT images from inter-fractional PET data and planning CT scans. Our model’s performance stands on par with existing models relying on input from cone beam CT or magnetic resonance imaging, which contain more anatomical information than activity maps.