Classification of Chimney EVAR-Related Endoleaks: Insights from the PERICLES Registry

Juxtarenal aortic aneurysms (JAAs) pose significant challenges for endovascular aneurysm repair (EVAR). A short or absent infrarenal neck typically excludes standard EVAR as a viable or reasonable treatment option. In this context, the use of chimney grafts (chEVAR) is gaining in popularity and applicability. These grafts are designed to course in the aortic lumen outside the main stent-graft to maintain normal perfusion to the involved target branches. As such, they may represent a promising and less resource-intensive option for management of JAAs. However, this technical strategy is not without challenges of its own, particularly the inevitable creation of \u201cgutters\u201d that result from the interaction of the chimney graft with the main aortic stent-graft. These gutters can become a conduit for type Ia endoleak formation, hence they represent the Achilles\u2019 heel of chEVAR. Current reports point to a relatively wide-ranging incidence (0%\u201313%) of type Ia endoleaks related to chEVAR. The PERICLES Registry collected the global transatlantic experience of 13 European and US vascular centers reporting 517 patients with complex aneurysms treated with EVAR and chimney parallel grafts. Overall, 6% of PERICLES chEVAR patients had a type Ia endoleak at completion angiography, but the rate of persistent endoleaks was only 2.9% at a mean 17.1 months of follow-up. Close review of the postoperative computed tomography angiograms of these persistent endoleak patients revealed distinct types and patterns of chEVAR-related type Ia endoleaks and form the basis of a new classification proposed herein. It is hoped that these observations will lead to development of new treatment algorithms for effective management of chimney-related endoleaks and, in some cases, to prevent them from occurring in the first place

One-Shot HDR Imaging via Stereo PFA Cameras

High Dynamic Range (HDR) imaging techniques aim to increase the range of luminance values captured from a scene. The literature counts many approaches to get HDR images out of low-range camera sensors, however most of them rely on multiple acquisitions producing ghosting effects when moving objects are present. In this paper we propose a novel HDR reconstruction method exploiting stereo Polarimetric Filter Array (PFA) cameras to simultaneously capture the scene with different polarized filters, producing intensity attenuations that can be related to the light polarization state. An additional linear polarizer is mounted in front of one of the two cameras, raising the degree of polarization of rays captured by the sensor. This leads to a larger attenuation range between channels regardless the scene lighting condition. By merging the data acquired by the two cameras, we can compute the actual light attenuation observed by a pixel at each channel and derive an equivalent exposure time, producing a HDR picture from a single polarimetric shot. The proposed technique results comparable to classic HDR approaches using multiple exposures, with the advantage of being a one-shot method

A stable graph-based representation for object recognition through high-order matching

Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem into a matching between graphs defined over feature points. The motivation is that the relational model would add more discriminative power, however the overall effectiveness strongly depends on the ability to build a graph that is stable with respect to both changes in the object appearance and spatial distribution of interest points. In fact, widely used graph-based representations, have shown to suffer some limitations, especially with respect to changes in the Euclidean organization of the feature points. In this paper we introduce a technique to build relational structures over corner points that does not depend on the spatial distribution of the features

Mergers of double neutron stars with one high-spin component: brighter kilonovae and fallback accretion, weaker gravitational waves

Neutron star mergers where both stars have negligible spins are commonly considered as the most likely, "standard" case. But based on observed systems, we estimate that actually a non-negligible fraction of all double neutron star mergers ($\sim$ 5 %) may contain one millisecond component. We use the Lagrangian Numerical Relativity code SPHINCS_BSSN to simulate mergers where one star has no spin and the other has a dimensionless spin parameter of $\chi=0.5$. These mergers exhibit several distinct signatures compared to irrotational cases. Morphologically, they are similar to unequal mass mergers and they form in particular only one, very pronounced spiral arm. Compared to the non-spinning cases, they dynamically eject an order of magnitude more mass of unshocked material at the original low electron fraction of the neutron stars and therefore produce particularly bright, red kilonovae and brighter kilonova afterglows months after the merger. We also find that the spinning cases have significantly more fallback accretion, with implications for late-time X-ray flares and the duration of the associated gamma-ray burst. Overall, the spinning case collisions are substantially less violent and they emit smaller amounts of shock-generated semi-relativistic material and therefore produce less pronounced blue/UV kilonova precursor signals. Their post-merger gravitational wave signal is weaker and, during the simulated time, substantially smaller amounts of energy and angular momentum are emitted. Therefore the central remnant contains a larger angular momentum reservoir and could remain an "active engine" for a longer time.Comment: 17 pages, 15 figures, submitte

A physics-driven CNN model for real-time sea waves 3D reconstruction

One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpre-dictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigat-ing the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC