Stent Application for the Treatment of Cerebral Aneurysms

Rapid and striking development in both the techniques and devices make it possible to treat most of cerebral aneurysms endovascularly. Stent has become one of the most important tools in treating difficult aneurysms not feasible for simple coiling. The physical features, the dimensions, and the functional characteristics of the stents show considerable differences. There are also several strategies and tips to treat difficult aneurysms by using stent and coiling. Nevertheless, they require much experience in clinical practice as well as knowledge of the stents to treat cerebral aneurysms safely and effectively. In this report, a brief review of properties of the currently available stents and strategies of their application is presented

Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation

Single-image super-resolution (SISR) networks trained with perceptual and adversarial losses provide high-contrast outputs compared to those of networks trained with distortion-oriented losses, such as L1 or L2. However, it has been shown that using a single perceptual loss is insufficient for accurately restoring locally varying diverse shapes in images, often generating undesirable artifacts or unnatural details. For this reason, combinations of various losses, such as perceptual, adversarial, and distortion losses, have been attempted, yet it remains challenging to find optimal combinations. Hence, in this paper, we propose a new SISR framework that applies optimal objectives for each region to generate plausible results in overall areas of high-resolution outputs. Specifically, the framework comprises two models: a predictive model that infers an optimal objective map for a given low-resolution (LR) input and a generative model that applies a target objective map to produce the corresponding SR output. The generative model is trained over our proposed objective trajectory representing a set of essential objectives, which enables the single network to learn various SR results corresponding to combined losses on the trajectory. The predictive model is trained using pairs of LR images and corresponding optimal objective maps searched from the objective trajectory. Experimental results on five benchmarks show that the proposed method outperforms state-of-the-art perception-driven SR methods in LPIPS, DISTS, PSNR, and SSIM metrics. The visual results also demonstrate the superiority of our method in perception-oriented reconstruction. The code and models are available at https://github.com/seungho-snu/SROOE.Comment: Code and trained models will be available at https://github.com/seungho-snu/SROO

Optimal Voltage Control Using an Equivalent Model of a Low-Voltage Network Accommodating Inverter-Interfaced Distributed Generators

The penetration of inverter-based distributed generators (DGs), which can control their reactive power outputs, has increased for low-voltage (LV) systems. The power outputs of DGs affect the voltage and power flow of both LV and medium-voltage (MV) systems that are connected to the LV system. Therefore, the effects of DGs should be considered in the volt/var optimization (VVO) problem of LV and MV systems. However, it is inefficient to utilize a detailed LV system model in the VVO problem because the size of the VVO problem is increased owing to the detailed LV system models. Therefore, in order to formulate and solve the VVO problem in an efficient way, in this paper, a new equivalent model for an LV system including inverter-based DGs is proposed. The proposed model is developed based on an analytical approach rather than a heuristic-fitting one, and it therefore enables the VVO problem to be solved using a deterministic algorithm (e.g., interior point method). In addition, a method to utilize the proposed model for the VVO problem is presented. In the case study, the results verify that the computational burden to solve the VVO problem is significantly reduced without loss of accuracy by the proposed model.11Ysciescopu

Adjuvant Coil Assisted Glue Embolization of Vein of Galen Aneurysmal Malformation in Pediatric Patients

PurposeAdjuvant coils may offer advantages in flow control during glue embolization of high flow vein of Galen aneurysmal malformation (VGAM) patients but involves specific issues such as feasibility, durability and coil mass effect. The purpose of this study is to assess the outcome of adjuvant coils in addition to transarterial glue embolization for treatment of these patients.Materials and MethodsFive pediatric VGAM patients (age range; 11 weeks to 2 yrs 2 mos) with high flow fistulous angioarchitecture were treated with adjuvant coils 1) in the distal feeding artery and/or 2) in the vein of Galen followed by glue embolization of the shunt. The angiographic / clinical outcomes were assessed.ResultsAdjuvant coils were deployed in the distal feeding artery (n=3), vein of Galen pouch plus distal feeding artery (n=2). Additional transarterial glue embolization of the fistulae was successfully performed (n=4). Complete occlusion was achieved with coils in one case. Complete occlusion was achieved for all mural type cases (n=4). Residual feeders remained in a case of choroidal type of VGAM. No complications were noted related to the treatment. All patients showed normal development on follow up (range: 7.6 to 88.8 mo, mean 49.3 mo). Initial hydrocephalus improved on follow up despite coil mass effect in dilated vein of Galen.ConclusionAdjuvant coils for flow control with glue embolization may be a safe and effective treatment method for VGAM patients with high flow fistulous feeders