Abstract: Hadron therapy exploits the properties of ion beams to treat tumors by maximizing the dose released to the target and sparing healthy tissue. With hadron beams, the dose distribution shows a relatively low entrance dose which rises sharply at the end of the range, providing the characteristic Bragg peak that drops quickly thereafter. It is of critical importance in order not to damage surrounding healthy tissues and/or avoid targeting underdosage to know where the delivered dose profile ends-the location of the Bragg peak. During hadron therapy, short-lived beta(+)-emitters are produced along the beam path, their distribution being correlated with the delivered dose. Following positron annihilation, two photons are emitted, which can be detected using a positron emission tomography (PET) scanner. The low yield of emitters, their short half-life, and the wash out from the target region make the use of PET, even only a few minutes after hadron irradiation, a challenging application. In-beam PET represents a potential candidate to estimate the distribution of beta(+)-emitters during or immediately after irradiation, at the cost of truncation effects and degraded image quality due to the partial rings required of the PET scanner. Time-of-flight (ToF) information can potentially be used to compensate for truncation effects and to enhance image contrast. However, the highly demanding timing performance required in ToF-PET makes this option costly. Alternatively, the use of maximum-a-posteriori-expectation-maximization (MAP-EM), including total variation (TV) in the cost function, produces images with low noise, while preserving spatial resolution. In this paper, we compare data reconstructed with maximum-likelihood-expectation-maximization (ML-EM) and MAP-EM using TV as prior, and the impact of including ToF information, from data acquired with a complete and a partial-ring PET scanner, of simulated hadron beams interacting with a polymethyl methacrylate (PMMA) target. The results show that MAP-EM, in the absence of ToF information, produces lower noise images and more similar data compared to the simulated beta(+) distributions than ML-EM with ToF information in the order of 200-600 ps. The investigation is extended to the combination of MAP-EM and ToF information to study the limit of performance using both approaches.

Abstract: Proton therapy is a cancer treatment technique currently in growth since it offers advantages with respect to conventional X-ray and gamma-ray radiotherapy. In particular, better control of the dose deposition allowing to reach higher conformity in the treatments causing less secondary effects. However, in order to take full advantage of its potential, improvements in treatment planning and dose verification are required. A new prototype of proton computed tomography scanner is proposed to design more accurate and precise treatment plans for proton therapy. Our prototype is formed by double-sided silicon strip detectors and scintillators of LaBr3(Ce) with high energy resolution and fast response. Here, the results obtained from an experiment performed using a 100-MeV proton beam are presented. Proton radiographs of polymethyl methacrylate (PMMA) samples of 50-mm thickness with spatial patterns in aluminum were taken. Their properties were studied, including reproduction of the dimensions, spatial resolution, and sensitivity to different materials. Structures of up to 2 mm are well resolved and the sensitivity of the system was enough to distinguish the thicknesses of 10 mm of aluminum or PMMA. The spatial resolution of the images was 0.3 line pairs per mm (MTF-10%). This constitutes the first step to validate the device as a proton radiography scanner.

Abstract: In this paper we explore the effect of delta-ray emission and fluctuations in the signal deposition on the detection of charged particles in silicon-based detectors. We show that these two effects ultimately limit the resolution that can be achieved by interpolation of the signal in finely segmented position-sensitive solid-state devices.