Global approaches and local strategies for phase unwrapping

Phase unwrapping, i.e. the retrieval of absolute phases from wrapped, noisy measures, is a tough problem because of the presence of rotational inconsistencies (residues), randomly generated by noise and undersampling on the principal phase gradient field. These inconsistencies prevent the recovery of the absolute phase field by direct integration of the wrapped gradients. In this paper we examine the relative merit of known global approaches and then we present evidence that our approach based on “stochastic annealing” can recover the true phase field also in noisy areas with severe undersampling, where other methods fail. Then, some experiments with local approaches are presented. A fast neural filter has been trained to eliminate close residue couples by joining them in a way which takes into account the local phase information. Performances are about 60–70% of the residues. Finally, other experiments have been aimed at designing an automated method for the determination of weight matrices to use in conjunction with local phase unwrapping algorithms. The method, tested with the minimum cost flow algorithm, gives good performances over both simulated and real data

How do cardiologists select patients for dual antiplatelet therapy continuation beyond 1 year after a myocardial infarction? Insights from the EYESHOT Post-MI Study

Background: Current guidelines suggest to consider dual antiplatelet therapy (DAPT) continuation for longer than 12 months in selected patients with myocardial infarction (MI). Hypothesis: We sought to assess the criteria used by cardiologists in daily practice to select patients with a history of MI eligible for DAPT continuation beyond 1 year. Methods: We analyzed data from the EYESHOT Post-MI, a prospective, observational, nationwide study aimed to evaluate the management of patients presenting to cardiologists 1 to 3 years from the last MI event. Results: Out of the 1633 post-MI patients enrolled in the study between March and December 2017, 557 (34.1%) were on DAPT at the time of enrolment, and 450 (27.6%) were prescribed DAPT after cardiologist assessment. At multivariate analyses, a percutaneous coronary intervention (PCI) with multiple stents and the presence of peripheral artery disease (PAD) resulted as independent predictors of DAPT continuation, while atrial fibrillation was the only independent predictor of DAPT interruption for patients both at the second and the third year from MI at enrolment and the time of discharge/end of the visit. Conclusions: Risk scores recommended by current guidelines for guiding decisions on DAPT duration are underused and misused in clinical practice. A PCI with multiple stents and a history of PAD resulted as the clinical variables more frequently associated with DAPT continuation beyond 1 year from the index MI

Use of scaling information for stochastic atmospheric absolute phase screen retrieval

Scaling information is an important tool for the description of natural processes. Many applications of SAR (differential) interferometry lead to a set of sparse phase measurements, e.g. the monitoring of permanent scatterers. In this case, the atmospheric phase screen component of a given SAR image can be estimated over the PS sparse grid. Usually such data have to be unwrapped and then interpolated on a regular grid. We investigate the utility of the scaling information, valid for atmospheric phase screen data, in the process of unwrapping a set of sparse measurements. We show how the power-law behaviour of the data variogram can be used as an a priori constraint for optimization through techniques such as simulated annealing. The results are interpreted in view of operational applications to real data

Following flood dynamics by SAR/optical data fusion

Synthetic aperture radar (SAR) acquisitions are particularly useful to produce flood maps thanks to their all-weather and day-night capabilities. However, repetition intervals of radar instruments are in the order of several days for routine operations, reaching daily or higher frequencies only in tasked conditions. Therefore, to follow flood dynamics, images acquired by different sensors at different times may be beneficial. In the present work, multi-temporal SAR intensity, InSAR coherence and optical data are considered to describe a flood event occurred in the Basilicata region (southern Italy) on December 2013. In this case study, optical data have a twofold role: they allow to follow the flood dynamics (because SAR and optical data have been acquired in different dates during the inundation event), and they add information concerning the land cover of the analyzed area. The data fusion approach is based on Bayesian Networks (BNs). It is shown that the synergetic use of different information layers can help detect more precisely the areas affected by the flood, reducing false alarms and missed identifications which may affect algorithms based on data from a single source. The produced flood maps are compared to reference maps, independently obtained; the comparison indicates that the proposed methodology is able to reliably follow the temporal evolution of the phenomenon, assigning high probability to areas most likely to be flooded, reaching accuracies of up to 89%