Abstract: A high-resolution silicon detector probe, in coincidence with a conventional PET scanner, is expected to provide images of higher quality than those achievable using the scanner alone. Spatial resolution should improve due to the finer pixelization of the probe detector, while increased sensitivity in the probe vicinity is expected to decrease noise. A PET-probe prototype is being developed utilizing this principle. The system includes a probe consisting of ten layers of silicon detectors, each a 80 x 52 array of 1 x 1 x 1 mm(3) pixels, to be operated in coincidence with a modern clinical PET scanner. Detailed simulation studies of this system have been performed to assess the effect of the additional probe information on the quality of the reconstructed images. A grid of point sources was simulated to study the contribution of the probe to the system resolution at different locations over the field of view (FOV). A resolution phantom was used to demonstrate the effect on image resolution for two probe positions. A homogeneous source distribution with hot and cold regions was used to demonstrate that the localized improvement in resolution does not come at the expense of the overall quality of the image. Since the improvement is constrained to an area close to the probe, breast imaging is proposed as a potential application for the novel geometry. In this sense, a simplified breast phantom, adjacent to heart and torso compartments, was simulated and the effect of the probe on lesion detectability, through measurements of the local contrast recovery coefficient-to-noise ratio (CNR), was observed. The list-mode ML-EM algorithm was used for image reconstruction in all cases. As expected, the point spread function of the PET-probe system was found to be non-isotropic and vary with position, offering improvement in specific regions. Increase in resolution, of factors of up to 2, was observed in the region close to the probe. Images of the resolution phantom showed visible improvement in resolution when including the probe in the simulations. The image quality study demonstrated that contrast and spill-over ratio in other areas of the FOV were not sacrificed for this enhancement. The CNR study performed on the breast phantom indicates increased lesion detectability provided by the probe.

Abstract: Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests.

Abstract: We describe a new positron emission time-of-flight apparatus using liquid xenon. The detector is based in a liquid xenon scintillating cell. The cell shape and dimensions can be optimized depending on the intended application. In its simplest form, the liquid xenon scintillating cell is a box in which two faces are covered by silicon photomultipliers and the others by a reflecting material such as Teflon. It is a compact, homogenous and highly efficient detector which shares many of the desirable properties of monolithic crystals, with the added advantage of high yield and fast scintillation offered by liquid xenon. Our initial studies suggest that good energy and spatial resolution comparable with that achieved by lutetium oxyorthosilicate crystals can be obtained with a detector based in liquid xenon scintillating cells. In addition, the system can potentially achieve an excellent coincidence resolving time of better than 100 ps.