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: Compton Cameras emerged as an alternative for real-time dose monitoring techniques for Particle Therapy (PT), based on the detection of prompt-gammas. As a consequence of the Compton scattering process, the gamma origin point can be restricted onto the surface of a cone (Compton cone). Through image reconstruction techniques, the distribution of the gamma emitters can be estimated, using cone-surfaces backprojections of the Compton cones through the image space, along with more sophisticated statistical methods to improve the image quality. To calculate the Compton cone required for image reconstruction, either two interactions, the last being photoelectric absorption, or three scatter interactions are needed. Because of the high energy of the photons in PT the first option might not be adequate, as the photon is not absorbed in general. However, the second option is less efficient. That is the reason to resort to spectral reconstructions, where the incoming. energy is considered as a variable in the reconstruction inverse problem. Jointly with prompt gamma, secondary neutrons and scattered photons, not strongly correlated with the dose map, can also reach the imaging detector and produce false events. These events deteriorate the image quality. Also, high intensity beams can produce particle accumulation in the camera, which lead to an increase of random coincidences, meaning events which gather measurements from different incoming particles. The noise scenario is expected to be different if double or triple events are used, and consequently, the reconstructed images can be affected differently by spurious data. The aim of the present work is to study the effect of false events in the reconstructed image, evaluating their impact in the determination of the beam particle ranges. A simulation study that includes misidentified events (neutrons and random coincidences) in the final image of a Compton Telescope for PT monitoring is presented. The complete chain of detection, from the beam particle entering a phantom to the event classification, is simulated using FLUKA. The range determination is later estimated from the reconstructed image obtained from a two and three-event algorithm based on Maximum Likelihood Expectation Maximization. The neutron background and random coincidences due to a therapeutic-like time structure are analyzed for mono-energetic proton beams. The time structure of the beam is included in the simulations, which will affect the rate of particles entering the detector.