Vatech (South Korea)

This study examines the relationship between communication with those charged with ‎governance and ESG performance. Communication with those charged with governance, ‎which refers to formal interactions between external auditors and governance structures, has ‎become increasingly significant in recent years due to heightened regulatory expectations. ‎This trend is driven by regulatory efforts to strengthen disclosure requirements, aiming to ‎enhance accounting transparency. Using data from Korean firms (2019-2020), the empirical ‎results show that more frequent communication with those charged with governance improves ‎ESG performance. Such interactions promote internal governance effectiveness, which in ‎turn supports long-term sustainability. In addition, the presence of female board directors ‎strengthens the positive relationship between governance communication and ESG outcomes, ‎emphasizing the value of board diversity‎.

Background/Objectives: To compare the image quality of chest radiography with a dual-energy X-ray imaging system using AI technology (DE-AI) to that of conventional chest radiography with a standard protocol. Methods: In this prospective study, 52 healthy volunteers underwent dual-energy chest radiography. Images were obtained using two exposures at 60 kVp and 120 kVp, separated by a 150 ms interval. Four images were generated for each participant: a conventional image, an enhanced standard image, a soft-tissue-selective image, and a bone-selective image. A machine learning model optimized the cancellation parameters for generating soft-tissue and bone-selective images. To enhance image quality, motion artifacts were minimized using Laplacian pyramid diffeomorphic registration, while a wavelet directional cycle-consistent adversarial network (WavCycleGAN) reduced image noise. Four radiologists independently evaluated the visibility of thirteen anatomical regions (eight soft-tissue regions and five bone regions) and the overall image with a five-point scale of preference. Pooled mean values were calculated for each anatomic region through meta-analysis using a random-effects model. Results: Radiologists preferred DE-AI images to conventional chest radiographs in various anatomic regions. The enhanced standard image showed superior quality in 9 of 13 anatomic regions. Preference for the soft-tissue-selective image was statistically significant for three of eight anatomic regions. Preference for the bone-selective image was statistically significant for four of five anatomic regions. Conclusions: Images produced by DE-AI provide better visualization of thoracic structures.

Single-image super resolution (SR) task has evolved mainly in natural imaging and achieved impressive performance by employing vision transformer-based methods. We can consider using these deep-learning-based SR methods to obtain high-resolution medical images to confirm the possibility of improving the resolution of radiography images. In this paper, we apply a vision transformer as a single-image SR method in dynamic X-ray radiography (DXR). Because utilizing high-resolution images in DXR is difficult due to the limitation of resources, applying a binning technique to downsize images is inevitable even though the technique degrades the image quality. However, the SR simulations show that the network model can restore the aliased signals and significantly improve the modulation transfer function from the downsized images. Therefore, using the SR method based on the vision transformer, we can effectively restore high-resolution images from downsized images and thus develop high-performance DXR systems.

The online version contains supplementary material available at 10.1007/s10068-022-01207-8.

Lung cancer is currently the first leading cause of worldwide cancer deaths since the early stage of lung cancer detection is still a challenge. In lung diagnosis, nodules sometimes overlap with ribs and tissues on lung chest radiographic images, which are complex for doctors and radiologists. Dual-energy subtraction (DES) is a suitable solution to solve those issues. This article will develop an efficient iterative DES for lung chest radiographic images. Moreover, we propose an automatic algorithm for accurately determining bone and soft-tissue factors for subtraction. The proposed algorithm for determining the bone and soft-tissue factors is based on window/level ratio and radiographic histogram analysis. First, we take the image sampling from the original size 3072 × 3072 to 512 × 512 to reduce the processing time while achieving the bone and soft-tissue factors. Next, we compute the window/level ratio on the soft-tissue image. Finally, we determine the minimum value of the ratio to obtain the optimal soft-tissue and bone factors. Our experimental results show that our proposed algorithm achieves a minimized runtime of 200 ms, outperforming the GE algorithm’s time of 4 s. The runtime of our DES of 6.066 s is shorter than the Fujifilm algorithm of 10 s while visualizing nodules on soft-tissue images and obtaining a similar quality of the soft-tissue images compared with the other algorithms. The academic contributions include the proposed algorithm for determining bone and soft-tissue factors and the optimized iterative DES algorithm to minimize time and dose consumption.

BackgroundCOVID‐19 is a serious respiratory disease, and wearing masks has become essential in daily life. Nevertheless, the number of people complaining of skin problems caused by wearing masks is increasing. Therefore, we investigated the characteristics of changes in sensitive skin caused by wearing a mask.Materials and methodsTwenty healthy Korean women with sensitive skin participated in this study. To determine any skin‐related changes caused by mask‐wearing, we evaluated redness, hydration, transepidermal water loss (TEWL), and moisture at 2.5 mm below the surface before and 4 h after wearing a Korea Filter 94 mask. In addition, we tested whether applying a moisturizer for 30 min after mask removal could reverse any mask‐induced changes.ResultsSkin redness and TEWL were significantly increased at 4 h after wearing a mask (p < 0.05), otherwise skin hydration and the 2.5 mm moisture were significantly decreased (p < 0.05). After applying the moisturizer, skin redness and TEWL were significantly decreased compared to their values 4 h after wearing masks (p < 0.05), whereas skin hydration and the 2.5 mm moisture were significantly increased (p < 0.05). Moreover, after applying the moisturizer, skin redness and TEWL were significantly reduced compared to the pre‐masking baseline (p < 0.05), whereas skin hydration was significantly increased (p < 0.05); the 2.5 mm moisture showed no significant change.ConclusionWe observed that wearing masks causes physiological changes in sensitive skin, whereas applying a moisturizer after removing the mask improved skin conditions.

Purpose: Flat-panel radiography detectors employ thin-film transistor (TFT) panels to acquire high-quality x-ray images. Pixel defects occur due to circuit shorts or opens in the TFT panel. The defects may degrade the image quality, as well as lower the production yield, and eventually raise the production cost. Hence, it is important to develop an appropriate defect correction algorithm for acquired images. Traditional correction algorithms are based on a complicated adaptive filtering technique, which exploits neighbor pixels, to faithfully preserve the edge components. Because of the complexity of the traditional sophisticated approaches, optimizing their correction performances is difficult. Approach: We considered various pixel-defect correction algorithms based on different deep learning models, such as the artificial neural network (ANN), convolutional neural network (CNN), concatenate CNN, and generative adversarial networks (GAN). We considered two cases of maximal defect sizes, and , and conducted extensive learning experiments to find the best structures of the learning models using the mean square error (MSE) as the loss function. Results: To conduct experiments, practical chest x-ray images were acquired from a general radiography detector. The MSE values of the correction results from ANN, CNN, concatenate CNN, and GAN were 69.40, 75.13, 68.21, and 73.77, respectively, and were much smaller than that of the conventional template match correction method. Conclusions: A concatenate CNN showed the best defect-correction performance. However, ANN could achieve a similar correction performance with much smaller encoding complexity. Therefore, the single-layer ANN can efficiently conduct defect corrections in terms of both correction and complexity.

In order to reduce the grid artifacts, which are caused by using the antiscatter grid in obtaining x-ray digital images, we analyze the grid artifacts based on the multiplicative image formation model instead of the traditional additive model, and apply filters to suppress the artifact terms. The artifact terms are aliases of the modulated terms from the harmonics of the grid frequency. Hence, several filters are required to efficiently suppress the grid artifacts. However, applying filters also distort the original image that will be recovered. If the distance between the origin and the center frequency of the artifact term is relatively large, then we can suppress the artifact term less distorting the original image. In this paper, by increasing the distances for a given sampling frequency of the image detector, we design antiscatter grids, which are good in terms of efficient grid artifact reduction. In order to design optimal grids, we formulate min-max optimization problems and provide optimal grid frequencies for a fixed grid angle with respect to the sampling direction of the image detector and optimal grid angles for a fixed grid frequency. We then propose using rotated grids with the optimal grid angles in digital radiography imaging. By applying band-rejection filters to the artifact, we can considerably reduce the grid artifacts when comparing to the traditional non-rotated grid case for real x-ray images.

We have investigated the optical properties of Gd 2 O 2 S:Tb granular phosphor screens for the use in indirect-conversion detectors by using the Monte Carlo method. For the optical model of the phosphor screen, it was regarded as a weak absorbing medium in which scattering is caused by refraction at boundaries between the phosphor grains and organic binders. For the estimation of the light collection efficiency, we included thin passivation (e.g. SiO 2 ) and Si layers as a photodiode in the Monte Carlo geometry only because the optical photons which escape from the phosphor screen exit and towards the Si layer can contribute to signals. In addition, optical coupling materials (e.g., optical fluids), which are practically used in the indirect-conversion detector, were considered. In the Monte Carlo simulations, various design parameters of the phosphor screen were considered such as the refractive index of an optical coupler and passivation layer, a reflection coefficient at the screen backing, and the thickness of an optical coupler. According to the simulation results, the optical coupler played a great role both in light collection efficiency and point-spread function (PSF). The maximum light collection efficiency was achieved when the refractive index of the optical coupler matched to either that of the phosphor screen or that of the photodiode. Moreover, the matched refractive index provided a lesser light spread in the resulting images. The simulation method and result can provide guidelines for a better design of indirect-conversion detectors based on a photodiode array coupled to a phosphor screen.

This study proposed two cursor capturing functions in mouse operation. One is to move the cursor over a target instantaneously (jumping function) when the cursor reaches around the target, and the other is to move the cursor over the target gradually (gravity function). The effects of cursor capturing functions were examined and compared with the normal condition (i.e., without the functions). Task completion times, number of errors, and subjective preferences were evaluated. The results showed that the gravity function had faster movement times and fewer errors than the normal condition. The gravity function was more preferred than the jumping and the normal condition.