Popularity of Screen Golf in Korea and Its Sociocultural Meaning.

The purpose of this study was to examine the popularity of screen golf, golf played using an indoor golf simulator, in Korea and to further explore its sociocultural significance. This study conducted a case study in which purposeful sampling was employed to recruit 15 participants. The results revealed that screen golf was popular in Korea because its facilities were easier to access; screen golf centers were found at convenient locations, and screen golf was more affordable than playing golf at the golf course. The combination of screen golf and the bang culture that is particular to Koreans has led them to accept the former as a familiar space for leisure activities. The results further revealed that screen sport has sociocultural significance in that its considerable popularity has led to the integration of virtual reality (VR) sports into daily life, thus making the division between sports and games less evident. Golf, a sport once considered as being an exclusive hobby for rich elites, has become popular among the general public, destroying the hierarchal notion that some sports harbor. This is meaningful as screen golf has played the role of an agent for sport socialization, encouraging people to participate in golf even on a course, unlike any other VR sport. Furthermore, this pastime has secured its position as a subculture in and of itself, becoming popular throughout the world.

Unsupervised anomaly detection in MR images using multicontrast information.

PURPOSE: Anomaly detection in magnetic resonance imaging (MRI) is to distinguish the relevant biomarkers of diseases from those of normal tissues. In this paper, an unsupervised algorithm is proposed for pixel-level anomaly detection in multicontrast MRI. METHODS: A deep neural network is developed, which uses only normal MR images as training data. The network has the two stages of feature generation and density estimation. For feature generation, relevant features are extracted from multicontrast MR images by performing contrast translation and dimension reduction. For density estimation, the distributions of the extracted features are estimated by using Gaussian mixture model (GMM). The two processes are trained to estimate normative distributions well presenting large normal datasets. In test phases, the proposed method can detect anomalies by measuring log-likelihood that a test sample belongs to the estimated normative distributions. RESULTS: The proposed method and its variants were applied to detect glioblastoma and ischemic stroke lesion. Comparison studies with six previous anomaly detection algorithms demonstrated that the proposed method achieved relevant improvements in quantitative and qualitative evaluations. Ablation studies by removing each module from the proposed framework validated the effectiveness of each proposed module. CONCLUSION: The proposed deep learning framework is an effective tool to detect anomalies in multicontrast MRI. The unsupervised approaches would have great potentials in detecting various lesions where annotated lesion data collection is limited.

Unsupervised learning of a deep neural network for metal artifact correction using dual-polarity readout gradients.

PURPOSE: A new unsupervised learning method was developed to correct metal artifacts in MRI using 2 distorted images obtained with dual-polarity readout gradients. METHODS: An unsupervised learning method is proposed for a deep neural network architecture consisting of a deep neural network and an MR image generation module. The architecture is trained as an end-to-end process without the use of distortion-free images or off-resonance frequency maps. The deep neural network estimates frequency-shift maps between 2 distorted images that are obtained using dual-polarity readout gradients. From the estimated frequency-shift maps and 2 distorted input images, distortion-corrected images are obtained with the MR image generation module. Experiments using synthetic data and actual MR data were performed to compare images corrected by several metal-artifact-correction methods. RESULTS: The proposed method resolved the ripple and pile-up artifacts in the reconstructed images from synthetic data and actual MR data. The results from the proposed method were comparable to those from supervised-learning methods and superior to the compared model-based method. The proposed unsupervised learning method enabled the network to be trained without labels and to be more robust than supervised learning methods, for which overfitting problems can arise when using small training data sets. CONCLUSION: Metal artifacts in the MR image were drastically corrected by the proposed unsupervised learning method. Two distorted images obtained with dual-polarity readout gradients are used as the input of the deep neural network. The proposed method can train networks without labels and does not overfit the network, even with small training data sets.

Non-contrast-enhanced peripheral MR angiography using velocity-selective excitation.

PURPOSE: To develop a new non-contrast-enhanced peripheral MR angiography that provides a high contrast angiogram without using electrocardiography triggering and saturation radiofrequency pulses. METHODS: A velocity-selective excitation technique is used in conjunction with the golden-angle radial sampling scheme. The signal amplitude varies according to the velocity of the flow by the velocity-selective excitation technique. Because the arterial blood velocity varies depending on the cardiac phase, the acquired data can be classified into systolic and diastolic phase based on the signal amplitude of the artery. Two images are then reconstructed from the systolic and diastolic phase data, respectively, and an image reflecting the differences between the two images is obtained to eliminate background and vein signals. The performance of the proposed method was compared with the quiescent-interval single shot (QISS) in eight healthy subjects and an elderly subject. RESULTS: The proposed method generated fewer residual venous and background signals than the QISS. Furthermore, the maximum intensity projection images, the relative contrast, and the apparent contrast-to-noise ratio results showed that the proposed method produced a better contrast than the QISS. CONCLUSIONS: The proposed non-contrast-enhanced peripheral MR angiography technique can provide a high contrast angiogram without the use of electrocardiography triggering and saturation radiofrequency pulses. Magn Reson Med 79:779-788, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A parallel MR imaging method using multilayer perceptron.

PURPOSE: To reconstruct MR images from subsampled data, we propose a fast reconstruction method using the multilayer perceptron (MLP) algorithm. METHODS AND MATERIALS: We applied MLP to reduce aliasing artifacts generated by subsampling in k-space. The MLP is learned from training data to map aliased input images into desired alias-free images. The input of the MLP is all voxels in the aliased lines of multichannel real and imaginary images from the subsampled k-space data, and the desired output is all voxels in the corresponding alias-free line of the root-sum-of-squares of multichannel images from fully sampled k-space data. Aliasing artifacts in an image reconstructed from subsampled data were reduced by line-by-line processing of the learned MLP architecture. RESULTS: Reconstructed images from the proposed method are better than those from compared methods in terms of normalized root-mean-square error. The proposed method can be applied to image reconstruction for any k-space subsampling patterns in a phase encoding direction. Moreover, to further reduce the reconstruction time, it is easily implemented by parallel processing. CONCLUSION: We have proposed a reconstruction method using machine learning to accelerate imaging time, which reconstructs high-quality images from subsampled k-space data. It shows flexibility in the use of k-space sampling patterns, and can reconstruct images in real time.

Development and effect of a cognitive enhancement gymnastics program for elderly people with dementia.

The purpose of this study was to develop a cognitive enhancement gymnastics program for the elderly with dementia and to verify its effect. The study was conducted on 27 people with dementia being treated in a dementia day care center in Incheon city. No statistically significant differences were found in the measures Mini-Mental State Examination for Dementia Screening (MMSE-DS), Short Geriatric Depression Scale (SGDS), Seoul Activities of Daily Living (S-ADL), or rock-paper-scissors. However, the MMSE-DS and rock-paper-scissors showed improvement after 12 weeks.