Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures, but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues.Comment: 10 pages, 9 figure

Minimization of Temperature Ranges between the Top and Bottom of an Air Flow Controlling Device through Hybrid Control in a Plant Factory

To maintain the production timing, productivity, and product quality of plant factories, it is necessary to keep the growth environment uniform. A vertical multistage type of plant factory involves different levels of growing trays, which results in the problem of difference in temperature among vertically different locations. To address it, it is necessary to install air flow devices such as air flow fan and cooling/heating device at the proper locations in order to facilitate air circulation in the facility as well as develop a controlling technology for efficient operation. Accordingly, this study compares the temperature and air distribution within the space of a vertical multistage closed-type plant factory by controlling cooling/heating devices and air flow fans harmoniously by means of the specially designed testbed. The experiment results indicate that in the hybrid control of cooling and heating devices and air flow fans, the difference in temperature decreased by as much as 78.9% compared to that when only cooling and heating devices were operated; the air distribution was improved by as much as 63.4%

Expression of CYLD and NF-κB in Human Cholesteatoma Epithelium

The tumor suppressor CYLD is a deubiquitinating enzyme that inhibits activation of the NF-κB, which has key roles in inflammation and apoptosis. We hypothesized that CYLD may regulate the NF-κB signaling pathway in cholesteatoma. We conducted immunohistochemistry to examine the expression of CYLD and NF-κB in 16 cases of cholesteatoma and paired cases of retroauricular (RA) skin. In cholesteatoma epithelium, activated NF-κ B expression was significantly higher than in RA skin, whereas CYLD expression was significantly lower in cholesteatoma epithelium than in RA skin (P < .05). Furthermore, a significant inverse correlation was detected between CYLD and activated NF-κB expression in cholesteatoma epithelium (r = −0.630). We found that CYLD reduced and activated increased NF-κB in cholesteatoma epithelium in comparison to RA skin. The inverse correlation between CYLD and activated NF-κB in cholesteatoma may be involved in cholesteatoma epithelial hyperplasia

Imbalanced loss-integrated deep-learning-based ultrasound image analysis for diagnosis of rotator-cuff tear

A rotator cuff tear (RCT) is an injury in adults that causes difficulty in moving, weakness, and pain. Only limited diagnostic tools such as magnetic resonance imaging (MRI) and ultrasound Imaging (UI) systems can be utilized for an RCT diagnosis. Although UI offers comparable performance at a lower cost to other diagnostic instruments such as MRI, speckle noise can occur the degradation of the image resolution. Conventional vision-based algorithms exhibit inferior performance for the segmentation of diseased regions in UI. In order to achieve a better segmentation for diseased regions in UI, deep-learning-based diagnostic algorithms have been developed. However, it has not yet reached an acceptable level of performance for application in orthopedic surgeries. In this study, we developed a novel end-to-end fully convolutional neural network, denoted as Segmentation Model Adopting a pRe-trained Classification Architecture (SMART-CA), with a novel integrated on positive loss function (IPLF) to accurately diagnose the locations of RCT during an orthopedic examination using UI. Using the pre-trained network, SMART-CA can extract remarkably distinct features that cannot be extracted with a normal encoder. Therefore, it can improve the accuracy of segmentation. In addition, unlike other conventional loss functions, which are not suited for the optimization of deep learning models with an imbalanced dataset such as the RCT dataset, IPLF can efficiently optimize the SMART-CA. Experimental results have shown that SMART-CA offers an improved precision, recall, and dice coefficient of 0.604% (+38.4%), 0.942% (+14.0%) and 0.736% (+38.6%) respectively. The RCT segmentation from a normal ultrasound image offers the improved precision, recall, and dice coefficient of 0.337% (+22.5%), 0.860% (+15.8%) and 0.484% (+28.5%), respectively, in the RCT segmentation from an ultrasound image with severe speckle noise. The experimental results demonstrated the IPLF outperforms other conventional loss functions, and the proposed SMART-CA optimized with the IPLF showed better performance than other state-of-the-art networks for the RCT segmentation with high robustness to speckle noise. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.1

Wide-Field 3D Ultrasound Imaging Platform with a Semi-Automatic 3D Segmentation Algorithm for Quantitative Analysis of Rotator Cuff Tears

Rotator cuff tear (RCT) is a common injury that causes pain and disability in adults. The quantitative diagnosis of the RCT can be crucial in determining a treatment plan or monitoring treatment efficacy. Currently, only a few diagnosis tools, such as magnetic resonance imaging (MRI) and ultrasound imaging (US), are utilized for the diagnosis. Specifically, US exhibited comparable performance with MRI while offering a readily available diagnosis of RCTs at a lower cost. However, three-dimensional(3D) US and analysis of the regions are necessary to enable a better diagnosis of RCTs. Therefore, we developed a wide-field 3D US platform with a semi-automatic 3D image segmentation algorithm for 3D quantitative diagnosis of RCTs. The 3D US platform is built based on a conventional 2D US system and obtains 3D US images via linear scanning. With respect to 3D segmentation algorithm based on active contour model, frequency compounding and anisotropic diffusion methods were applied, and their effects on segmentation were discussed. The platform was used for clinical examination after evaluating the platform via the RCT-mimicking phantoms. As verified by the Dice coefficient(average DC: 0.663, volume DC: 0.723), which was approximately up to 50% higher than that obtained with conventional algorithms, the RCT regions segmented by the developed algorithm significantly matched the ground truth. The results indicated that the wide-field 3D US platform with the 3D segmentation algorithm can constitute a useful tool for improving the accuracy in the diagnosis of RCTs, and can eventually lead to better determination of treatment plans and surgical planning.1

Spacer grid effects on the heat transfer enhancement during a reflood

Paper presented at the 9th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Malta, 16-18 July, 2012.An experimental study using 6x6 and 2x2 square lattice rod bundles has been performed to investigate the effects of spacer grids on the heat transfer enhancement during a bottom-reflood phase. The spacer grids improve a turbulent mixing of flow and induces breakup of large droplets into smaller ones. These result in the heat transfer enhancement between the fuel rods and the surrounding fluid. Since the geometry of the spacer grid affects the turbulent mixing and droplet breakup behaviors, three types of spacer grids with different geometry were tested in the present study. In order to investigate the heat transfer enhancement by spacer grids, single-phase steam cooling and droplet breakup by spacer grid were separately investigated. For the convective heat transfer enhancement in singlephase steam flow, the heater rod surface temperatures were measured in the vicinity of the space grid. In single-phase steam cooling experiment, the heat transfer was enhanced at upstream and downstream of spacer grids. Downstream of the spacer, the heat transfer enhancement decays with the distance from the top end of the spacer grid exponentially. The heat transfer enhancement depends on the Reynolds number as well as the flow blockage ratio. A new empirical correlation was developed in order to account for the effect of the Reynolds number. For the droplet breakup experiment, the sizes and velocities of droplets were measured across the spacer grid. The droplet breakup ratio decreases with increasing the Weber number of the droplet impacting on the spacer grid. The droplet breakup ratio by spacer grids was relatively higher than conventional correlations.dc201

Effect of Flow Blockage on the Coolability during Reflood in a 2 ×

During the reflood phase of a large-break loss-of-coolant accident (LBLOCA) in a pressurized-water reactor (PWR), the fuel rods can be ballooned or rearranged owing to an increase in the temperature and internal pressure of the fuel rods. In this study, an experimental study was performed to understand the thermal behavior and effect of the ballooned region on the coolability using a 2 × 2 rod bundle test facility. The electrically heated rod bundle was used and the ballooning shape of the rods was simulated by superimposing hollow sleeves, which have a 90% blockage ratio. Forced reflood tests were performed to examine the transient two-phase heat transfer behavior for different reflood rates and rod powers. The droplet behaviors were also investigated by measuring the velocity and size of droplets near the blockage region. The results showed that the heat transfer was enhanced in the downstream of the blockage region, owing to the reduced flow area of the subchannel, intensification of turbulence, and deposition of the droplet

Anesthetic management of an adult patient with Rett syndrome and limited mouth opening -A case report-

Rett syndrome is a neurological disease that occurs only in females and it manifests with mental retardation, seizures, movement disorders, autistic behavior and abnormal breathing. A 19-year-old female with Rett syndrome underwent ophthalmologic surgery under general anesthesia at our institution. Airway control was difficult due to her limited mouth opening. We recommend that anesthesiologists should have proper knowledge about this disease and the patients to avoid the complications and problems that can be encountered during the perioperative period