Super-resolution imaging within reach

Although several optical techniques have been recently developed in order to overcome the resolution limit in microscopy, the imaging of sub-wavelength features is still a real challenge. In practise, super-resolution techniques remain difficult to build or are photo-toxic for the biological samples. However, microsphere-assisted microscopy has recently made super-resolution imaging accessible to scientists (e.g. optical metrologists, engineers and biologists). This paper presents an easy-to-implement optical setup to perform full-field and contactless super-resolution measurements of nanostructured media or biological elements. For this purpose, a classical microscope was enhanced by introducing a transparent microsphere. We show that this rather simple approach makes it possible to achieve a lateral resolution of 200 nm in air, i.e. the visualization of feature sizes of 100 nm

Complexity assessment and technical aspect of coronary angiogram and percutaneous coronary intervention following transcatheter aortic valve implantation

Background: Performing selective coronary angiogram (CA) and percutaneous coronary intervention (PCI) post transcatheter aortic valve implantation (TAVI) may be challenging with various success rates of coronary ostia engagement. Methods: Among all patients who underwent CA and/or PCI after TAVI from our single center TAVI registry, ostia cannulation success was reported according to the quality of ostia engagement and artery opacification, and was classified as either selective, partially selective or non-selective but sufficient for diagnosis. Results: Among the 424 consecutive TAVI procedures performed at the aforementioned institution, 20 (4.7%) CA were performed in 19 (4.5%) patients at a median time of 464 days post TAVI (25–75% IQ: 213–634 days). CA were performed in 7 CoreValve, 9 Evolut R, 1 Evolut PRO and 2 Edwards Sapien 3 devices. Transradial vascular approach was attempted in 9 procedures (45%, right n = 6 and left n = 3) and was successful in 8 (40%) patients. A total of 20 left main artery ostium cannulation were attempted leading to a diagnostic CA in all of them with selective engagement in 65%.  Engagement of the right coronary artery in 2 out of 15 attempted cases failed due to a low ostium in conjunction with a high implantation of a CoreValve prosthesis. 11 PCI (55% of CA) including 2 left main lesions were performed. In 4 patients (36.4% of the PCI), an extension catheter was required to engage the left main. All planned PCI were successful. Conclusions: Post TAVI CA and PCI are challenging but feasible even after supra-annular self-expandable valve implantation

Machine learning and reduced order modelling for the simulation of braided stent deployment

Endoluminal reconstruction using flow diverters represents a novel paradigm for the minimally invasive treatment of intracranial aneurysms. The configuration assumed by these very dense braided stents once deployed within the parent vessel is not easily predictable and medical volumetric images alone may be insufficient to plan the treatment satisfactorily. Therefore, here we propose a fast and accurate machine learning and reduced order modelling framework, based on finite element simulations, to assist practitioners in the planning and interventional stages. It consists of a first classification step to determine a priori whether a simulation will be successful (good conformity between stent and vessel) or not from a clinical perspective, followed by a regression step that provides an approximated solution of the deployed stent configuration. The latter is achieved using a non-intrusive reduced order modelling scheme that combines the proper orthogonal decomposition algorithm and Gaussian process regression. The workflow was validated on an idealised intracranial artery with a saccular aneurysm and the effect of six geometrical and surgical parameters on the outcome of stent deployment was studied. The two-step workflow allows the classification of deployment conditions with up to 95% accuracy and real-time prediction of the stent deployed configuration with an average prediction error never greater than the spatial resolution of 3D rotational angiography (0.15 mm). These results are promising as they demonstrate the ability of these techniques to achieve simulations within a few milliseconds while retaining the mechanical realism and predictability of the stent deployed configuration

Microsphere-assisted phase-shifting profilometry

In the present work, we have investigated the combination of a superresolution microsphere-assisted 2D imaging technique with low-coherence phase-shifting interference microscopy. The imaging performance of this technique is studied by numerical simulation in terms of the magnification and the lateral resolution as a function of the geometrical and optical parameters. The results of simulations are compared with the experimental measurements of reference gratings using a Linnik interference configuration. Additional measurements are also shown on nanostructures. An improvement by a factor of 4.7 in the lateral resolution is demonstrated in air, thus giving a more isotropic nanometric resolution for full-field surface profilometry in the far field. (C) 2017 Optical Society of AmericaPeer reviewe

Open-Ended Learning: A Conceptual Framework Based on Representational Redescription

International audienceReinforcement learning (RL) aims at building a policy that maximizes a task-related reward within a given domain. When the domain is known, i.e., when its states, actions and reward are defined, Markov Decision Processes (MDPs) provide a convenient theoretical framework to formalize RL. But in an open-ended learning process, an agent or robot must solve an unbounded sequence of tasks that are not known in advance and the corresponding MDPs cannot be built at design time. This defines the main challenges of open-ended learning: how can the agent learn how to behave appropriately when the adequate states, actions and rewards representations are not given? In this paper, we propose a conceptual framework to address this question. We assume an agent endowed with low-level perception and action capabilities. This agent receives an external reward when it faces a task. It must discover the state and action representations that will let it cast the tasks as MDPs in order to solve them by RL. The relevance of the action or state representation is critical for the agent to learn efficiently. Considering that the agent starts with a low level, task-agnostic state and action spaces based on its low-level perception and action capabilities, we describe open-ended learning as the challenge of building the adequate representation of states and actions, i.e., of redescribing available representations. We suggest an iterative approach to this problem based on several successive Representational Redescription processes, and highlight the corresponding challenges in which intrinsic motivations play a key role